Alex Hedenstrand

The Intelligent Enterprise Resource Planning System from a Business Perspective

Information systems C-Thesis

Term: VT-19 Supervisor: Linda Bergkvist

Abstract Businesses are faced with an ever-changing environment of technology, one of these recurring changes is in the field of Enterprise resource planning (ERP) systems. Technologies such as business intelligence (BI), machine learning (ML) and internet of things (IoT), which are driving forces in shaping the next generation of ERP systems. These improved ERP systems can better support a company compared to a traditional ERP system. This thesis has examined what constitutes an intelligent-ERP and what possibilities it presents from a business perspective. To answer this question, a qualitative study approach was chosen by conducting interviews with professionals within the BI and ERP systems field. The interviews were structured in a loose fashioned way, aiming for a holistic perspective of the I-ERP systems phenomenon. This bachelor thesis transpired to show that I-ERP systems do improve business performance for companies that exhibit the needs. However, there occurred differences in the perception of the definition of how I-ERP systems should be defined between the participants and the literature.

1 Preface First and foremost, I would like to thank my thesis interview participants, without you this study would not be possible. Another big thank you goes out to my thesis supervisor Linda Bergkvist, along with my group of peers, your input, advice, and guidance has been immeasurable in helping me strive forward with my work. Finally, I would like to thank the special people in my life, my mother, Sebastian, and Malin for helping me through the ups and downs. A sincere thank you to all, Alex Hedenstrand

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Table of Contents

Abstract ...... 1 Preface ...... 2 1.1 Problem background ...... 5 1.2 Purpose ...... 6 1.3 Research question ...... 6 1.4 Limitation of the study ...... 6 1.5 Target group ...... 6 2 Methodology ...... 7 2.1 Scientific approach ...... 7 2.2 Qualitative interviews ...... 7 2.2.1 Data processing ...... 8 2.3 Choice of interview participance...... 8 2.3.1 Pilot interview ...... 8 2.4 Data analysis ...... 9 2.5 Literature criticism ...... 9 2.6 Validity and Reliability ...... 9 2.7 Ethics ...... 10 2.8 Model framework ...... 10 3 Literature overview ...... 11 3.1 Enterprise resource planning ...... 11 3.2 Machine Learning ...... 11 3.3 Machine learnings application ...... 11 3.4 Critical perspective of machine learning regarding privacy ...... 12 3.5 Bias in machine learning ...... 13 3.6 Business intelligence ...... 13 3.6. BI and ERP integration ...... 14 3.7 Internet of Things integration with ERP ...... 14 3.8 Intelligent enterprise resource planning ...... 15 3.8 Model of an I-ERP system ...... 17 4 Empirical findings ...... 18 4.1 Interview participants ...... 18

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4.2 Interview participants responses ...... 18 4.2.1 Machine learning from the perspective of participants ...... 19 4.2.2 Business intelligence participants responses...... 22 4.2.3 Participants responses regarding Internet of Things ...... 24 4.2.4 Participants responses regarding Intelligent Enterprise Resources Planning systems ...... 25 5 Analysis ...... 29 5.1 Machine learning in ERP systems context ...... 29 5.1.1 Limitation of machine learning ...... 30 5.1.2 Bias regarding machine learning ...... 30 5.2 Business intelligence in an ERP context ...... 31 5.2 Internet of Things ...... 33 5.3 Intelligent Enterprise Resource Planning ...... 34 6: Discussion ...... 37 Suggestion for future studies ...... 38 References ...... 39 Appendix 1: Contest form ...... 40 Appendix 2: Informations letter ...... 41 Appendix 3: Interview guide ...... 42

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1.1 Problem background An enterprise resource planning (ERP) system is an essential tool for any modern business. The global markets use of both ERP- and different IT systems has changed almost every aspect of daily trading. The ERP system has gone through many iterations during its lifetime, first from being a simple accounting system, and now being a fully supporting system for almost all business-related processes. According to Jenab at el. (2019 p.151) the intelligent ERP system (I- ERP) is the next evolution of current ERP systems. Compared to a more traditional ERP system, the I-ERP system uses technologies such as business intelligence and machine learning to better support digital transformation, but also enhance business decisions. The transition from the traditional ERP system is described by Carlsson-Wall et al. (2018, p.177) as a digitalization process caused by automation and artificial intelligence.

Another point of improvement is within the field of Internet of Things (IoT) devices. Almost all devices can be connected to the Internet and provide data for a system. The aim of an IoT device from an ERP systems perspective is to provide the system with automatic data input (Ching-Hai et al. 2018, p.246-247). The data could be structured and collected around technologies such as barcodes, RIFD, different sensors, etc. (Ching-Hai et al. 2018, p.246-247). The data provided by IoT devices presents new possibilities in making an ERP system more efficient (Ching-Hai et al. 2018, p.246-247). An example of IoT usage within the ERP systems field is the automatic data input to the logistics part of an ERP system (Ching-Hai et al. 2018, p.246-247). This could for example let a customer see where their product is currently in the shipping process.

Along with IoT, machine learning is also a new frontier in the ERP industry. The next step of the ERP systems lifecycle is currently pointing towards machine learning (Jenab et al. 2019 p.151). The basic premise of machine learning, according to Lee et al. (2018, p.111) is to transform data into useful predictions. An example of machine learning within the ERP field is demand forecasting enabled by using customer data to predict when sales will occur (Lee et al. 2018, p111). Using machine learning technology with an ERP system is a relatively new concept within the field. Jenab et al. (2019, p.152) explains machine learning can be applied to enhance the planning for a company by learning from its previous experience. According to Jenab at el. (2019 p.151) an ERP system that uses machine learning is both more intelligent and provides companies with more valuable information compared to a traditional ERP system.

According to Rahimi and Rostamib (2015, p8) business intelligence (BI) is a term describing the way business decisions are made with a data driven computerized system. Usually, BI systems are tightly coupled to an ERP system (Rahimi and Rostamib 2015, p9). Though these two systems have their inherent differences, they are essentially two sides of the same coin. As touched on briefly before, the I-ERP systems aims to connect BI along with machine learning and other technologies to provide businesses with effective and relevant analytics (Jenab at el. 2019, p.154).

Though some companies have implemented machine learning into different processes. Some machine learning applications have been proven to be difficult to implement correctly, as for 5 the case of recruitment of staff. For an example, in the case of Amazon it was a search for the “holy grail” in a machine learning based hiring tool (Dustin, 2019). Amazon had trained their machine learning model with over ten years of applications made to the company, most of which came from men (Dustin, 2019). Because of a larger majority of men compared to woman’s applications the machine learning model made generalization of the data favouring masculine word use (Dustin, 2019). This resulted in bias data which favoured men in the application ranking, which was the opposite intention of the implementation of the hiring tool (Dustin, 2019). Later Amazon tried to fix the machine learning application, but because the way that the algorithm was trained it proved almost impossible to fix.

Because of the relative new introductions of the I-ERP system on the market, business adoption is not widespread. Currently there exists a push on the market for businesses to start digitalizing their daily operations. This is where the I-ERP system appears in businesses peripheral view, both to better support a more intelligent company, but also to enhance business processes (Oodles ERP, 2018). 1.2 Purpose The purpose of this study is to examine what constitutes an I-ERP system and its potential from a business perspective.

1.3 Research question • What role does IoT, ML, and BI have in an I-ERP system? • Which possibilities and limitations does an I-ERP system present for a business?

1.4 Limitation of the study This study will not be providing the technical solutions for implementing machine learning or IoT to an ERP system, nor will technical descriptions of machine learning algorithms be reviewed. Rather, the study will do a deep dive on how these technologies can be applied for inquiring business improvements. Baseline descriptions of the technologies will be provided for a holistic view of the I-ERP systems field, and what possibilities and drawbacks lies with the application of technology. 1.5 Target group The target group for this study are professionals within the ERP systems field that wishes to gather a rudimentary knowledge about ML, BI and IoT along with the opportunities they present for an I-ERP system.

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2 Methodology 2.1 Scientific approach This bachelor thesis is consisted of a qualitative study approach. According to Patel and Davidsson (2011, p.14) a qualitative study approach is used when the data collection is focused on softer data, such found when conducting qualitative interviews. The qualitative study approach has been carried out by conducting interviews with 3 participants who have knowledge regarding the field of ERP and BI; along with the studying of prior related works such found in journals and books. This approach was chosen because of the relative new perspective of I-ERP within the ERP systems field. Therefore, careful examination of the theory and conducting qualitative interviews are more suitable for deriving a more coherent study of the phenomenon. Another argument for choosing a qualitative study according to Patel and Davidsson (2011, p14) is to answer the questions of what a phenomenon is, or rather “what is this?”. Therefore, a correlation between answering the question of what something is, and this bachelor thesis purpose can be argued. Consequently, the choice for a qualitative study is displayed both in the type of data examined, but also demonstrated in the choice of the purpose of this study.

2.2 Qualitative interviews Patel and Davidsson (2011, p81) explains that during a qualitative study it is common practise to apply qualitative interviews. Qualitative interviews have a lose structure meaning that the interview subject answers with their own words. In this study, the interviews where structured after the analytical framework (see appendix 3) to provide a logical arrangement of open-ended questions. The questions were asked in this way to let the respondents answer with their own words; while also letting the interviewee steer the conversations. The qualitative interview approach leads the study to construct the questions in a flexible arrangement; for instants, if the respondent conversed expressively to one of the questions more follow up questions would be asked. In some cases, the questions would be switched around to have a coherent discussion. After the first interview, some revisions were made to the interview guide; these were regarding question arrangement, wording of the questions along with a few added questions. The reasoning behind the changes were to improve the quality of future interviews along with improving issues with the interview guide.

To ensure that the study relayed respondent answers correctly both recordings and notes were taking during the interview. Before the recording started, verbal confirmation was giving from the participants, also they were provided with information on how the data would be stored and managed. The respondents were also giving the information letter regarding what the study wish to answer, also the respondents signed a consent form before the interview started, see (appendix 1 & 2).

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2.2.1 Data processing Patel and Davidsson (2011, p.120) explain that during a qualitative study there is no certain existing defined way of processing data. Usually qualitative methods are tweaked and tailored to best suit the researcher’s problem. For this reason, Patel and Davidsson (2011, p.120) have derived some guidelines and techniques for acquiring proper data processing in a qualitative study. One common approach is to use an audio recording while also noting important things in writing. A transposing of the interview is something that strongly recommended by Patel and Davidsson (2011, p.120). For this reason, both an audio recording along with handwritten notes where taken during interviews. After each interview was completed, a transposing process was preformed of the audio recording; to transform the audio into written text. Later the transposed interviews where used to form the empirical findings.

2.3 Choice of interview participance. The choosing of participants was fuelled by speaking to a professional within the ERP systems field, the professional recommended to interview companies that work directly with either BI, ERP systems or both. This was the contributing factor for choosing the participance and is the reason why professionals within the field of ERP and BI have been interviewed. Also, the professionals have a practical perspective which helps to enrich the analysis. The chosen company representatives where contacted mainly through e-mail or via phone calls. During the contact the experts where provided with a brief description of the topic of the interview. The interviews gave the study an enhancement with regards to both the results but also the analysis. The interview participance had differing perspective of ERP and Bi which in turn nourishes the holistic perspective. Likewise, most of the respondents where professionals with wide differences in experience within the field, some were well experienced consultants that have been working since the late 80s. Others, where less experienced compared to the senior level of experience. An interesting viewpoint for this bachelor thesis was to compare the differing perspectives to comprise what constitutes and I-ERP. By comparing the different answers from the respondents yielded to both similarities along with differences in opinion and perspective. 2.3.1 Pilot interview At the beginning of the study an exploratory interview was conducted to gather a rudimentary perspective of the field of I-ERP. After the exploratory interview, a pilot interview was conducted with the same participant. Patel and Davidsson (2011, p. 84) emphasise that a pilot interview is a way for qualitive studies to continuously work with the development of the interview guide, which is the basis for the study. The initial version of the interview guide had broader gaps, poorly formulated questions along with a less intuitive structure. The first pilot interview will

8 not be presented in the empirical finding, because of the initial problems along with it only being a test run of the interview guide. 2.4 Data analysis Patel and Davidsson (2011, p. 121) states that it is common during a qualitative study to start the primary analysis after performing an interview. During this bachelor thesis, a separate document was used to record interesting perspectives and quotes. Later this document was also used to conduct a preliminary analysis from the participants answers, while also reflecting how it corelates to the literature. The reflection recorded in the document range from initial thoughts, to more elaborate perspectives given from the interviewees. Some of the ideas and perspectives retained from the interview participants, are not included in the analysis due to either scope limitation or it being unessential towards the purpose of this study; this was however discussed in the conclusion chapter. 2.5 Literature criticism Patel and Davidsson (2011, p.42) explain that the most common approach for gathering knowledge is from literature in forms of journals, reports and books. This bachelor thesis mainly comprised of journals found through the OneSearch function located on the Karlstad University’s library website. Google Scholar has also been used in the search for journals and books. To receive a correct query from either search engine, parameters such as: date of publishing, search words and to discard some key words where applied to the searches. During the search for literature all the results where filter by peer reviewed only. This means that the articles presented are both reviewed and approved by other experts within the field. In the research world a peer reviewed journal holds more credibility compared to a journal without one; this is because of the journal undergoes extensive scrutiny from the other experts. Therefore, a peer reviewed journal is more credible than a non-reviewed journal. Another key element to find journals suited for this bachelor thesis was to apply certain key words such as: machine learning ERP, BI and ERP, I-ERP, etc. Most of these terms are not completely new to the field, but the idea of an intelligent ERP system is not widely studied. This means that most of the articles supporting the I-ERP system claim where from the years 2017-2019; which points to a relative “fresh” field or rather new perspective of ERP systems.

2.6 Validity and Reliability During a qualitative study validity and reliability are essential in the entire study. According to Patel and Davidsson (2011, p.105) the term validity in the context of a qualitative study is that the researchers is examining the correct phenomenon, which in turn is then often strengthened by a well-established literature chapter. Patel and Davidsson (2011, p.105) explain that the instruments used during the data collection need to be suitable and accurate. As mentioned before, in the case of a qualitative

9 study loosely structured interviews is often common practice. Though Patel and Davidsson (2011, p.105-106) explains that validity and reliability in the case of a qualitative study do not equate to the same as in the case of a quantitative study. Some researcher substitutes the term reliability with understanding or authenticity (Patel and Davidsson 2011, p.105-106).

The intent of this bachelor thesis is to comply with validity and reliability in the context of a qualitative study. The mindset of validity and reliability has been applied throughout the entire process of this study. For instance, a pilot interview was conducted to gather rudimentary knowledge about the field of I-ERP, along with testing the interview questions. Another point of contention is after an interview was completed, they were transcribed the same day, this was to ensure that the answers were not skewed by misremembrance. Also, the respondents were giving the opportunity to change statements said during the interview. 2.7 Ethics Vetenskapsrådet (2002) describes four different requirements that are the foundation of protecting an individual from harm when conducting a study.

The first requirement regards to inform the individual what the study aims to complete with an interview; what data points will be collected and what will be used from the individual in study. This was explained to participants before the interview took place, both verbally and in a written agreement (See consent form, appendix 1&2) of what data would be used in the study. After informing the participants of what data would be collected, a verbal agreement of how the data would be handle was settled, for example if a recording could be taken, along with how the recording would be stored; which was locally on an encrypted disk. Another key point is also to explain to the individual that participation is voluntary, and participation can be cancelled or retracted during any stage of the study. This was also reinforced by using a written agreement to reassure that the participation is voluntary and of a consenting manner. While also verbally conveying that any statement can be retracted from the study. The confidentiality requirement has been applied to the study by anonymizing personal details of the participants. Individuals of this study where assigned numbers and a fabricated name for concealing their true identities. The last requirement is usage of data, this corresponds to only using the data for research purposes, i.e. not lending or selling the data to a third party. The only reason that data is used is solely for scientific reasons of the studying the said phenomena. Along with the guidelines provided by the Vetenskapsrådet (2002) the laws regarding GDPR have been adequately followed by the terms provided by Karlstad University. 2.8 Model framework Because the field of I-ERP is relatively young, a framework was provided to show a summery of how the field can be understood. The aim of this framework was mainly for showing an

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abstract concept of an I-ERP system and simplifying it. Therefore, making it more manageable for the reader, along with aiding in the analysis writing process. 3 Literature overview 3.1 Enterprise resource planning According to Jenab et al. (2019, p152) an ERP system is a software package with several different components that together help support business processes. A typical business process that an ERP system can assist in are budgeting, supply chain, sales, logistics, customer service, etc. (Jenab et al. 2019, p152). An ERP system is serval modules that interlink together to exchange information on a shared database (Jenab et al. 2019, p152). 3.2 Machine Learning According to Nilsson (1998, p1) the definition of machine learning can be defined as a computer program that changes its structure or data, based on the inputs or external factors. The changes that occur within the computer program will then provide towards future self-improvement; which means that the machine learns from its experience (Nilsson 1998, p.1). An example of when learning has occurred by a machine or computer program is displayed by Nilsson (1998, p1) with the following: when you let a speech recognitions program improve by hearing several samples of people speech; a justification that the machine has learned is then valid (Nilsson 1998, p1). Witten (2016, p7) explains that what defines the term learning is one often correlated to philosophy rather than IT. Witten (2016, p8) emphasise that from a philosophical perspective it is a non-trivial task to define machine learning; the definitions of learning often lead to contradicting factual behaviours of the machine learning. Witten (2016, p8) has defined machine learning as following: “Machines or computer programs learn when they change their behaviour in a way that makes them perform better in the future.” 3.3 Machine learnings application Nilsson (1998, p2) explains that machine learning can be applied to show relationships with large amounts of data similarly to data mining. By conventional means of using humans for extracting these relationships is far less efficient compared to using ML (Witten 2016, p25). Witten (2016, p25) highlights this phenomenon of using machine learning through their example of weather forecasting. A database containing a 15-years of weather attributes such as temperature, humidity, cloud cover and windspeed where applied to machine learning algorithms to show which days where most similar in weather. The results from the machine learning algorithm where later compared to the results of human forecasters and where found to be faster and equally as accurate; rather than taking hours it took seconds for the ML algorithm to find and display the similar days (Witten 2016, p25).

According to Witten (2016, p14) machine learning mainly has two application for business these are, predictions and relationships in large data structures. In the use case of relationships correlation by machine learning is highlighted in the previous example. On the other hand,

11 prediction made by machine learning is explained by Witten’s (2016, p23) with the following example:

A loan company had issues with deciding which application to accept and which ones to discard. These cases had a common denominator of the clients having a poor credit report. The company trained a ML model with 1000 previous cases for the program to decide which clients to move forwards with and which to decline. The machine learning program could use data points from the customers to make predictions of which of them were most likely to pay the debt of the loan. The machine learning program made accurate predictions while also giving declined customers a reason for the dismissal (Witten 2016, p23).

From an ERP systems perspective, ML can be applied from these two different definitions. Jenab et al. (2019, p152) explains that ERP systems can use machine learning to achieve predictive analysis capabilities. The ability of an ERP system to make predictions Jenab et al. (2019, p152) corelates to improving processes and enhanced planning for company operation. The reason for these improvements is mainly because of a machine learning enabled system can learn from previous experience; meaning that management can correctly adapt to a changing environment (Jenab et al. 2019, p152). 3.4 Critical perspective of machine learning regarding privacy Current machine learning models does however exhibit some negative attributes towards privacy and bias. The two subsequent paragraphs will give a brief overview of potential problems that can arouse while using machine learning technology. Shokri et al. (2017, p3) explains that machine learning models can leak information by using a technique known as membership interference. This type of attack is conducted through targeting datasets in black-box machine learning services offered by large IT-companies such as Google and Amazon (Shokri et al. 2017, p3). Black-box machine learning refers to only knowing the output of a machine learning model, while datasets is the data used to train a machine learning model (Shokri et al. 2017, p3). Compared to a white-box model where the data sets are visible attacking a black-box model is more sophisticated according to Shokri et al. (2017, p3). Shokri et al. (2017, p4) quantifies the problem by drawing the comparison between using sensitive datasets such as in medical industry, these datasets can later be quarried to reveal private information about people. Since the information is sensitive non authorise users should not be able to access the information, which in turn creates the problem of a privacy breach (Shokri et al. 2017, p9). The exact definition of a privacy breach in a machine learning context is the following: “A privacy breach occurs if an adversary can use the model’s output to infer the values of unintended (sensitive) attributes used as input to the model.” - Shokri et al. (2017, p5)

Valid machine learning models generalize information from input to make accurate prediction; meaning that inputs to the model is not a part of the training dataset. Shokri et al. (2017, p5) explains that generalizing machine learning models cannot protect privacy from the previous definitions because of the way that the model draws correlations between inputs and datasets. 12

This results in a larger problem according to Shokri et al. (2017, p5) they claim that it can hold entire populations privacy at risk regardless of how a generalizing machine learning model was chosen and trained. 3.5 Bias in machine learning Howard et al. (2017, p1) explains that machine learning performance biases originate from poor representation of diversity in the dataset used in the training of the algorithm. This results in performance problems according Howard et al. (2017, p1). Imbalances in the datasets is difficult to differentiate between data and noise. Howard et al. (2017, p1) explains that this problem amplifies when using datasets provided through cloud-based services. The training sets offered by the service provider often contain readily available data from online queried sources that have a high probability of being bias. The problem with bias dataset in machine learning training is that it creates results which are often not representative of the entire spectrum. If the datasets are heavily skewed towards one favoured demographic it can tilt the results in their benefit (Howard et al. 2017, p1). 3.6 Business intelligence According to Rahimia and Abbasi Rostamib (2015, p8) BI is a hypernym term that covers methods and concepts to make stronger and improved business decisions. Information from BI is based of business data or information that originate from key business processes (Rahimia and Abbasi Rostamib 2015, p8). BI is leveraging the key information to yield improved business processes. Rahimia and Abbasi Rostamib (2015, p9) explains that BI has become a critical application for a company to provide insights, support better decisions making while also improving company performance. Rahimia and Abbasi Rostamib (2015, p9) emphasizes that BI has the following objectives: to gather data, to transform the data into useful information and to provide an interface for the information.

Chou et al. (2005, p343) explains that more organizations are turning towards BI systems to fulfil their needs of reporting and the structuring of data. This is because the ERP system does not often contain the necessary capabilities for supporting reporting and analysis (Chou et al., 2005, p343). A BI system works in tandem with an ERP system by retrieving the large amounts of data contained in the ERP system. The BI system provides a company with the capability of analysing both short and long-term business scenarios (Chou et al., 2005, p343). Chou et al. (2005, p343) explains that most businesses are dependent on a single source of information from the ERP system, which in turn weakens flexibility and response time. Managerial needs of discovering errors, improvement points and patterns are not often met with only the use of a conventional ERP system. According to Chou et al. (2005, p343) this is because an ERP system focus mainly lies with recording business transactions, creating predefined reports and managing the business transactions. Contrary, a BI system is used to analyse the vast quantities of data produced by the ERP system, which in turn generates essential information needed for decision making (Chou et al., 2005, p343).

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3.6. BI and ERP integration Zhou (2012, p.270) explains that companies today do not fear a shortage of data; the problem is now an abundance of it. BI has become the main tool in recent years to use the abundant amount of data too create usable information from the chaos (Zhou 2012, p.270). Rahimia and Abbasi Rostamib (2015, p11) explains that a BI system is highly reliant on an ERP systems large quantity of data and functionality. This is because an integrated BI system quarries data from the ERP system; hence for a company’s data often stored on an ERP system (Zhou 2012, p.270). According to Rahimia and Abbasi Rostamib (2015, p9) an integrated BI system coupled to an ERP system is a critical success factor for creating enhanced business operation. Rahimia and Abbasi Rostamib (2015, p11) emphasizes that BI and ERP integration can provide companies with a way to structure data repositories; which in turn assists in fast and effective decision making. The integration between BI and an ERP system results according to Zhou (2012, p.270) to strict process control and the production of a large quantity of accurate and correct data usable for business decisions. The integration also turns operational data into analytical data which also promotes correct decision making (Zhou 2012, p.270). An ERP system is often the ideal tool for developing a BI application according to Rahimia and Abbasi Rostamib (2015, p11).

The following quote highlights the integrational relationship between ERP and a BI: “The synergistic relationship between ERP and BI can indeed be the perfect storm, igniting improved performance and visibility.” - Rahimia and Abbasi Rostamib (2015, p11).

According to Rahimia and Abbasi Rostamib (2015, p11) successful businesses have an understanding that ERP and BI work together to provide the best and most complete access to data. This is done through not treating the ERP and BI systems as enterally separate entities, rather to treat them as to be two sides of the same coin (Rahimia and Abbasi Rostamib (2015, p11). The integration between BI and ERP also corresponds to the optimal use of the systems (Rahimia and Abbasi Rostamib 2015, p11).

3.7 Internet of Things integration with ERP Majeed and Rupasinghe (2017, p27) defines IoT as enabling an electronic devise through a one point of access, enabling communication with the device worldwide, at any point in time. Majeed and Rupasinghe (2017, p27) argues that IoT is a new revolution of the internet, objects and devices can recognise themselves through communication; rendering them more intelligent. The information that IoT collects resides from physical objects, devices and sensors (Majeed and Rupasinghe, 2017, p27). This information can be used to supply a wide array of systems with different parameters that provide for an improved human-machine relation operation (Majeed and Rupasinghe, 2017, p28). An example provided by Majeed and Rupasinghe (2017, p27-28) about how IoT can affect an organization positivity is in a manufacturing context. Majeed and Rupasinghe (2017, p28) argues that IoT can automate large parts of manufacturing by providing machines with adaptive and analytical capability’s not

14 possible with conventional means of manufacturing. This leads to a more streamlined, efficient and a more flexible manufacturing process. An IoT enabled process can be changed to fit the parameters of a desired outcome, meaning that a company becomes more flexible and adaptably. According to Majeed and Rupasinghe (2017, p28) the measurement of competitive advantage in the future for manufacturing companies will be measured from how efficient and how well they can adapt their processes. Majeed and Rupasinghe (2017, p28) explains that two key factors are needed to fully exploit IoT devise, these are full integration between devises and data management. Majeed and Rupasinghe (2017, p28) explains because the IoT connected devises output large quantities of data, a system is needed for both the structure of data, but also the analytics of it. Majeed and Rupasinghe (2017, p29) surveyed both SAP systems consultants and users of the SAP ERP system to gather data points about specific improvement points enabled by both the system and IoT machines. They found that a large part of an ERP systems transactional processes such as: error detection, time spent on inbound processes and outbound processes where improved with the usage of integrated solution of ERP system and IoT machines Majeed and Rupasinghe (2017, p34-35)

Majeed and Rupasinghe (2017, p35) emphasizes that traditional ERP systems must make a major leap for supporting the future industry. This is because of the increase use of IoT and the need for a system capable of meeting the future industry needs. Majeed and Rupasinghe (2017, p35) explains that an ERP system needs to provide an interface for viewing the data of the manufacturing process enabling the company to identify what changes need to be made for streamlining the processes. From a manufacturing perspective the use of an ERP system that supports the IoT functionality is a necessity for managing complex supply-chains Majeed and Rupasinghe (2017, p39). 3.8 Intelligent enterprise resource planning Jenab et al. (2019 p.154) explains I-ERP as using advance analytics and machine learning for processing data resulting in useful information. In the case of using machine learning with I-ERP, it can help reveal patterns and help identify unexpected customer behaviour. The I-ERP is the next step in the ERP systems field according to Jenab et al. (2019 p.151). The I-ERP system can provide a company with an increase in the production quality (Jenab et al. 2019 p.159). This phenomenon is highlighted through the data that is entered and stored in the system; an I-ERP system can provide statistical process controls while also monitoring the results (Jenab et al. 2019 p.159). Jenab et al. (2019 p.159). explains that if an error or anomaly was detected by the system it can notify a user automatically. Because the I-ERP is integrated more strongly to the organization compared to a traditional ERP system, it can help employees with diagnostics of a problem and how to address them correctly (Jenab et al. 2019 p.159). Some examples of the full integration between an I-ERP system and the organization other systems is explained by Jenab et al. (2019 p.159) with receipt functions, monitoring inventory and processing orders. The integration between these systems allows for higher control in quality assurance from a manufacturing perspective, which alleviates the decision process for managers.

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An I-ERP system can also learn by its self-experience and by the requirements formulated by the company says Jenab et al. (2019 p.159). This is the reason why an I-ERP system can decrease the costs associated with manufacturing quality while also providing real-time predictive analysis, lessening the risks of recalls and errors occurring during the manufacturing process (Jenab et al. 2019 p.159).

Another potential improvement for companies using an I-ERP system is an improved speed of operation regarding inventory management. Jenab et al. (2019 p.159) explains that because of the usage of a centralized system the internal boundaries between departments are non-factor. Jenab et al. (2019 p.159) emphasizes that employees can save both time and cost regarding inventory management by having more efficient production half-life. The I-ERP system can both monitor and solve various problems that can occur in the supply-chain (Jenab et al. 2019 p.160). Jenab et al. (2019 p.160) correlates the effective management of the supply chain to improved inventory management, which in turn results in improved speeds and quality regarding production. Thus, according to Jenab et al. (2019 p.160) a I-ERP is an attractive planning tool for managers.

An I-ERP system can improve the flexibility for a company for several different industries (Jenab et al. 2019 p.160). The improved flexibility of an I-ERP is because the system can seamlessly adapt into companies; if companies require more functionality from the system it can be easily added from the vendor (Jenab et al. 2019 p.160).

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3.8 Model of an I-ERP system

Figure 1: Framework over an I-ERP systems parts and relationships. Alex Hedenstrand.

This framework is composed by combining key aspects from the literature provide in the previous paragraphs. The framework aims to give a holistic perspective of the I-ERP; meaning that relationships are simplified, and other important aspects are not brought up due to the scope of the study. The framework is meant to show how the I-ERP systems different parts are composed; these different parts have relationships that in turn have aspects that provide a positive effect or negative effect for the company. These relationships are displayed in the framework through the black arrows. Positive aspects are marked by a plus (+) symbol, negatives aspects are marked by (-). The larger field titled I-ERP intends to conceptualize what an I-ERP systems parts are consisted of, along with the white fields displaying the positive attributes of the ERP and Bi systems. The two grey round shapes represent IoT and ML which are techniques that are often associated with the term I-ERP. These techniques derive business improvements marked by the bullet points. The red triangle represents when bias data is used in ML, which in turns creates incorrect information. The analytical framework includes a negative outcome

17 where the information is bias, which in turn would lead to an incorrect decision’s basis. Why a negative outcome was included in the framework was to display the possibility of an inconsistent data training set; which in turn exemplifies one of the possible drawbacks of using a I-ERP system. The positive outcome would then be on the contrary, meaning that non-bias data would be used for creating correct business decisions. The title integration is meant to display the important symbiotic relationship between the ERP and BI system, without this link the business improvements cannot be derived. Finally, the headline information is meant to show the transformation of data that occurs with ML along with BI.

4 Empirical findings 4.1 Interview participants This bachelor thesis has interviewed 3 participants. To anonymize the participants, fictional names have been used. The following table 1 will provide both a short description of the interview participant, along with the name that will be used as a reference to their statements made in the interview.

Table 1: interview participants

Interview Name Experience Title/Role Time Date participant (min) A John 20 years Senior consultant and enterprise 04/30- architect 53 19 B Julie 15 years Team leader for Business Analytics 50 05/24- 19 C Eric 4 years ERP consultant for business 41 06/10- development 19

4.2 Interview participants responses The following tables will provide a summary of the respondents’ answers. To better emphasize the interviewees perspective, quotes from the participants along with paragraphs will help provide their answers in more detail. The questions have been labelled with Q along with a number. These questions are taken from the interview guide (see appendix 3). The reason behind this approach is to give a clearer view of the empirical findings. The structure of the answers will not follow the interview guide; instead, it will follow the literature overview arrangement. The reason behind the arrangement decision is to give the empirical findings a familiar structure that reflects the previous chapter.

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4.2.1 Machine learning from the perspective of participants

Question Participant A: John answers Participant B: Julie answers Participant C: Eric answers

Q1: How do John does not work directly with Julie works with ML techniques mainly with BI but Eric’s company works with ML in an ERP you work implementing ML. John has consulted has also worked with optimization programs that systems context. Eric advocated that ML with with BI systems that have used the ML uses the technology. ML’s functionality is making is a machine or computer program that machine technology. projections/predictions learns from its mistakes for future learning (in improvement. the context John explains that machine learning in of ERP and the context of BI is used to perform BI)? analysis.

Q2: What John thinks the problem lies with Currently Julie sees no problems with machine Eric explains that ML is not as flexible as problems adoption. John explained that many of learning technology. Julie believes that the it is presented. Eric adds that because of do you his clients feel the technology is problem with ML lies in adoption. changes that occur in process the currently “suspicious”. algorithm needs to be retrained. Eric also see with emphasized that the main problem with Machine ML is market adoption. learning? Q3: What John says that ML can be used to Julie explains that the main benefit of using ML is Eric reciprocated that ML diminishes the are the minimize errors and lower the human to make more accurate predictions, rather than need for human labour, minimizes errors benefits of error source. relying on gut feelings. and for automating processes. using machine learning? Q4: What John explains that BI uses algorithms, but Julie explains that the usage of machine learning Eric argues from his perspective that BI is role does mainly others. He says that ML is rarer coincides with BI functionality to make predictions. in most cases a part of ERP; because an ML fill in a from his experience. John’s explains that These predictions can for example forecast future ERP system is often the heart and lungs BI system? he has not seen ML in widespread use in sales. of a company’s operation. By virtue of BI systems. Eric statement, the ML’s role in a BI system is therefore in an ERP systems context. Q5: Does John states that he has seen components Julie says that ML is not used in ERP systems, if Eric advocates that ERP systems do in there exists that use ML that are integrated with ERP that is the case then it is rather the components of fact use ML technologies. Eric elaborated ERP system system. John defines that ML and ERP the ERP system. Julie defines ML and ERP as with an example of ML usage in an ERP that use ML are separate things. But John states that separate. If algorithms are used in the ERP system, system with invoice interpretation. Eric techniques? he has seen ERP systems that have they are often well known, but could be perceived strongly suggested that ERP systems with implemented ML, but not on a wide as ML algorithms, which they are often not. ML functionality will become more scale. common and prevalent in the future. Q6: Do you John did not answer this question. Julie states that this is something that is a Eric sates that ML in itself cannot cause think possibility. If you are working with ML, you need to bias, on the contrary he argued that it is machine keep in mind where the data originated from. more benefitable to use the technology learning because of the exclusions of human techniques factors, such as emotion. can cause Eric did however later elaborate that bias? when ML is applied towards data for deriving relationships or analysis, the data that is used for training can be

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based in bias. Eric responded with “shit in, shit out”, he continued explaining that you need to be aware of the data’s origin and reliability, otherwise there is a risk for unwanted results.

During the interviews the three participants explained their view of ML in the context of ERP and BI. The following citation is from Julie, which draws attention towards her encounters with ML, along with a brief explanation of use case of ML in the context of BI.

“Yes, to a certain degree, I have encountered optimising programs that can make prognoses […] In BI it is something that comes up a lot. In the context of BI, ML is essential for making predictive analysis.” - Julie

The citation provided by John highlights his argument: “I do not believe the issues lies in the technology, rather the problem derives from us. Partially I think that it us as individuals that are suspicious to the technology.”- John

Julie argues from the same stance, as shown in the following citation: “Machine learning adoption in the market is quite immature. There currently exist tools, but a minority of users. There is more that needs to be done in the field.”- Julie

While Eric implied that market adoption was an issue; though he also argued a point that ML in its current state is not flexible enough. “If a change occurs in a process that ML is being applied towards the ML technology needs to relearn it. There currently exists limits to what ML can achieve, it is not very flexible.”- Eric

Another point that was brought up was the benefits of using ML from a business perspective. all the participants agreed that the ML technology can mitigate errors. John has a perspective focused towards mitigating the humans error source: “I believe the technology can be used to minimize errors, specifically the human error source; which is currently the largest cause of errors, in my opinion.”- John

While Julie has perspective towards business gaining new insights with the use of ML, this is through predictive analysis; Julie does however also highlight John’s point of mitigating errors through the argument displayed in the subsequent citation.

“First and foremost, it is the ability to make predictive analysis for supporting business decisions. The usage of ML lets a business gain the ability to look at numbers or the data with better precision. Rather than on relying on a gut feeling, decisions can be based on data. Also, the machine learning model is probably more precise than a gut feeling. - Julie

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John explains that BI systems use algorithms to derive prognoses and analysis. John states from his experience that he has not seen ML in use in a BI system. The following statement is from John’s interview, where he argues his stance.

“BI systems use several different algorithms. But in my case, ML is not common from my work experience. I believe the reason behind this is because ML still feels suspicious and futuristic. I think that the algorithms in use in a BI system is less hocus-pocus compared to ML. This is because these algorithms have been in use for a longer time, therefore the users of the system feel safer with them.” - John

Julie explains that ML is in use in BI systems. Julie points out that ML is used to fine tune prognoses as well as establishing relationships with other factors. The ensuing paragraph is reflected in the citation made by Julie during the interview.

“When we speak about BI, ML is a heated topic. ML is often used in BI to make predictive analysis. If we would imagine an ice cream company that want to predict future sales. It is not always previous years' sales that will determine how future sales will look. In this case, weather could be one deciding factors towards sales. [...] ML can be used to fine tune the future prognoses, by establishing relationships between data. It is important for businesses to know what factors that are deciding, there is no hocus-pocus.” - Julie

While Eric explains that from his perspective ML is used in ERP systems:

“I have seen ML being used in ERP systems, the easiest example is invoice interpretations […] I am aware of at least a few ERP systems that use ML techniques, another example is from the timber industry which has sensors that read data and transmit it to an ERP system; ML is used often to detect errors.” - Eric

Julie argues that an ERP system does not exhibit ML functionality, rather it can be defined as a module or added component. The following citation is made by Julie to highlight her argument:

“Not in itself, rather it can be components in the ERP system that use ML. […] If we assume from the definition we have today of ML, an ERP system does not currently have ML functionality. As stated before, then it is components or modules that have ML capabilities. In that case, there exists well know algorithms that an ERP system uses that could be defined as ML. But from my point of view, I would not define these algorithms as ML; because the model is never trained.” – Julie

A quote from Julie regarding issues of biased data in the context of ML.

“This is something that you need to keep in mind. When working with ML you must clean the data, while also thinking about the reality of the scenario which the data is being applied too. Other important things too keep in mind are, where the data originates from, how the data was collected, and which factors impacts the data; this is often done through statistical scenarios.” – Julie

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Quote form Eric highlighting his view of bias in ML context.

“I believe that ML can become less bias than us humans can, for the reasons of our emotions. On the other side if you give ML incorrect data, bias is of a high probability. A ML model will always interpret the information it was provided, even if it is faulty.” – Eric

4.2.2 Business intelligence participants responses Question Participant A: John answers Participant B: Julie answers Participant C: Eric answers

Q7: How John states that he consults with BI systems Julie explains that she works with a large Eric explains that his company works with BI does your along with other systems. ERP system, which creates transactions. functionality directly implemented in the ERP company Julie states that BI works with aggregating system. Eric adds that certain clients’ in some work with these transactions for spotting trends, cases may need more functionality, the BI systems? supporting decision making, spotting solution is then to add auxiliaries to the BI potential dangerous and to see the services. company’s KPI. Q8: What John explains that a BI system helps a Julie explains that a BI system supports Eric advocates that it depends on the does the BI company to visualize their data. A BI system visualisation of data. She emphasises that definitions of BI. In most cases Eric would system uses unstructured data that can be found in with a BI system a company can review define a BI system as a tool for creating support? ERP system in the form of transactions. data from different perspectives. A BI reports, supporting decisions making as well as John states that BI is very important for system is better at displaying the whole an analytical tool. Eric says that a BI system companies that want to visualize their data picture compared to an ERP system, which often uses ERP systems data to create the in an effective way. only indicates the current state of affairs. reports. Q9: How John explains that a BI system is better at An ERP system handles the transactions Eric stressed that in an ERP system is where a would you transforming data compared to an ERP and supports processes, says Julie, i.e. company has their data, while in a BI system is define the system. He also adds that a BI system has handling a customer order. In a BI system a where you can examine it. Eric made a difference more functionality regarding analysis and company can view this order; meaning comparison of an ERP system as being the between a reports. John says that an ERP system is not visualizes the data of the order processes heart and lungs of the company, while a BI n ERP an analytical tool, which on the contrary BI KPI’s. system could be a part of the human head. system is; simply a BI system grants a company versus BI? with the ability to draw conclusions from data.

Q10: How John explains that BI and ERP are two Julie explains that a BI system quarry’s the Eric argued that an ERP system and a BI system does the separate systems. John says that transaction from an ERP system, but it can live in a symbiotic relationship, where the BI relationship companies usually have ERP system as their be other systems. Julie states that a BI systems is a tool for analysis and an ERP look heart of operations. Other systems outside system and ERP system are different, they system is a tool for support daily operations. between BI of the ERP such as BI needs the same data. fore fill different needs for companies. Eric explains that from his work experiences he and ERP Today, many companies still use ERP as Julies states that these two systems are views these two systems as separate tools; systems? their data management system; but a different from one another. though he argues that are strongly linked master data management (MDM) system is together.

far superior. John states also that these systems are separate.

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Q11: How Integration is very important; it has always Julie explains that a company cannot yield Eric stated that it is of utmost importance that important been John says. He explains in previous ERP the benefits of a BI system without an a BI and ERP systems is integrated properly. is the systems it was near impossible to integrate integration between BI and ERP. If an Eric states that an integration ensures that the integration different system in today’s standard. John integration would not be present, the use data is handle in a correct way; which in turn between BI states the reason why it is important to of a BI system is far less effective. Simply, yields the ability towards creating deep and ERP, integrate different systems is because it Julie adds integration automates processes analysis of business-related events that can and why? automates processes. Without a proper and ensures that processes are correct. alleviate in decisions making. integration it results in far less effective

process, especially in the context of a value chain. Q12A: I believe the most important part of both Julie explains that it depends on the Eric emphasizes that the most important What are systems is the structure. John explains if a organization in question. She states that aspect of a BI system is live correct data. Eric the most company has an efficient data structure there is no single part of the BI system that states if a change is applied towards the data important system integration is easier to perform. The is more important than another one. the displayed information should change parts of a structure John says comes from an MDM Similarly, she explains that different BI instantly. Another key aspect for companies BI system, system, which is often today the ERP systems have their positives and negatives; using BI systems to be meticulous when adding and why? system. John explains that if an MDM these differences must be examined to find data to the system. Eric adds that this seems system is in place, a company can add the best fitting system. like a fundamentally and easy principle to

components to a system at need without adhere too, but in many cases, this is having to make large adjustments. John overlooked. Eric emphasizes if companies emphasizes that an MDM system is like a disregards precautions when entering data to mantra for him; if used correctly it breads the system; mistakes occurs causing thwart of flexibility for a company a proper solution. usage, rendering the BI system far less useful. Q12B: Similarly, to John’s previous statement it is Julie adds for some; it is best practise that Eric states that it depends of which field of What are the structure that is currently the most yields the best outcome of business business you look at; for instance, if you would the most important part, along with an ERP system improvements. Julie states that this is look at a smaller company then a standardised important that fits the customer’s needs. especially true for business that have could ERP solution will yield almost equal parts of an complex processes that an ERP system results in business improvements as a more ERP system cannot support. But if Julie hade to pick the complex and larger ERP system. Eric implies and why? most important aspect of an ERP system, that different companies exhibit different this would be full integration. Julie explains needs when it comes to an ERP system. Eric the reason behind this statement is does advocate the main premise of using an because a company can integrate more ERP system is for companies to save time, components and other vital parts to the costs and automating processes. system at need.

Julie explains the difference between ERP and BI in the following citation:

“An ERP system is a proper and well-rounded tool for recording transactional data and supporting business processes. An ERP system can track and manage these transactions. Later when a company wants to aggregate the information stored in the ERP system, they can use a BI system; for spotting trends, decisions making, spotting potential errors or dangers and to view a company’s key performance indicators.”- Julie

Eric also explains his view of the differences between a BI system and ERP system with subsequently paragraph: “An ERP system is where a company stores their numbers, while in a BI system is where a company examines the numbers. – Eric

Eric highlighted his view of the ERP and BI relationship with the following statement:

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“From my perspective an ERP system is the heart and lungs of company, while BI is a part of the human head. I see the BI system as a tool for analysing the information that exists in a ERP system. These two systems live in a symbiotic relationship, they are not the same thing, one system (BI) is for analysis while an ERP system is for business related processes, but at the same time they are strongly connected.” – Eric

The following statement is a citation made by Julie during the interview about drawbacks of using a BI system: “There is no single part of a BI system that is more important than another, the determining factors are which BI tools is in use, and the companies’ prerequisites. Every tool has its positives and negatives. “– Julie

4.2.3 Participants responses regarding Internet of Things

Question Participant A: John answers Participant B: Julie answers Participant C: Eric answers

Q13: How John explains that he does not Julie explains that she has direct experience with Eric states that he has experience working with do work personally work with IoT directly. John IoT technology. She says that a client to her IoT devises. Eric explains how a workshop used with IoT in states that he has experience with company wanted to automate their check out IoT devises to automatically ordering items the context some of his clients that have used to processes. To solve the problem, IoT was used to that went out of stock, along with assigning the of ERP? solution. supplement the steps between check out and items specific places on shelving. billing.

Q14: What John says that IoT can deliver Julie explains that IoT has such a wide use case Eric strongly emphasizes that the use case and possibilities automation in the sense of data input. that it can be applied to almost any process that the possibilities IoT presents are nearly does IoT John makes an example regarding of be automated. Julie makes an example of how endless. Eric elaborates with an example present? the industry sector: machines can for meat transport can be automated in the context regarding controlling a meeting rooms example input data directly into the risk management, or how a warehouse can sense temperature, humidity and air quality with IoT ERP system, which in turn derives where a product is located. devises. Though Eric adds that his example improved reaction time. might not be directly corelated towards an ERP systems context; it illustrates what is possible with IoT. Q15: What John states that it is too early to tell if Julie says that it is hard to pinpoint something Eric acknowledges that a potential problem drawbacks drawbacks exist, privacy could be an specific. The only case where Julie sees a risk with IoT as replacing human labour to a high does IoT issue; tough he emphasizes that this with the usage of IoT is when handling sensitive degree. Eric also adds that that IoT devises has present? purely speculator. data. Julie states that IoT can only supply a matured compared to previous iterations; company with more information that can be aspects such as size of devises decreasing used for decisions making. She also adds that the along with costs. Eric ends on a note explains technology has matured and is safer than that one other problem is lies in market previous iterations. adoption.

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Q16: What John refers to a previous statement, Automating regarding information input that can Eric explains that IoT devises in the context of can IoT mainly automation in the form of data be used for decisions making. Julie also ERP systems can help with automating data support in input. emphasize that because of IoT wide use case input. Eric refers to his previous example of an ERP benefits are equally as wide. Depending on the order out of stock items at a workshop. Eric system? need of said company, automation regarding adds that IoT devices presents a large problems in productions, delivery state, advantage in stock and logistics; automating automating check out, etc. Julie states that the information flows along with improving the use case for IoT is merely limited by the traceability of events. imagination.

The quote from Eric show the possibility of IoT:

“The application for IoT is near endless, currently there is too few companies willing to try IoT because the technology is untested in some fields. […] The customer I meet, often say why would we need this? The market is simply not aware of the benefits of using IoT, which is automating processes.”- Eric

4.2.4 Participants responses regarding Intelligent Enterprise Resources Planning systems

Question Participant A: John answers Participant B: Julie answers Participant C: Eric answers

Q17: How John explains that his company works Julie states that her company works with ERP Eric responded that his company works with does you with questions that companies can related questions through asking questions implementing different ERP systems solutions company face on today’s market regarding IT regarding requirements of operations, along for a customer. work with systems. John states different with wishes and initiative posted by the client’s ERP? scenarios that displays this: a company management; how we can implement and that has acquired several companies support our client’s through our ERP system. and therefore need to update their systems, or simply changing and older ERP system to more a modern one. John’s goal is to answer and lead companies towards a correct path. Q18: What John explains that an ERP system is Julie says that an ERP system is a fully Eric states that an ERP system supports many does an ERP often the motor of operation for a integrated that supports business processes aspects of a company, but mainly stock system company. The system helps to support and records business transactions. The management, logistics along with support for a business processes and recording the business processes that an ERP system administrative tasks and record keeping. business? transactions. supports range from administration tasks such as bookkeeping, to subprocess such as sales of goods to customers. Q19: From John explains from his point of view Julie states that who is the I-ERP intelligent Eric explains that his view of I-ERP is a system your point of there is no intelligent ERP system, for? Julie argues that an I-ERP is trade specific. that learns from it experience; inferring ML

25 view: what rather it is about a smart solution. John One possibility of what constitutes an I-ERP abilities. Eric says that an I-ERP system learns constitutes an states because the market changes at a might be a system that provides insights to a from mistakes, he uses an example regarding intelligent rapid pace, ERP systems today need to company. If ERP system is intelligent it is invoice recognition. The interpreting invoice ERP? be flexible; meaning that components integrated in a way that allows companies to function trains using the invoices, in the begin can be changed on a whim. Another see direct or indirect relationships. This is it is unreliable. Eric adds with more practices reason for a smart solution is that something that exists on the market, Julie time the invoice interpreter becomes more changing an entire ERP system is adds. But from her perspective this is not an accurate and can assess statement to a degree expensive. John says that he believes intelligent ERP system, rather an intelligent near 100%. that the architecture of the ERP system infrastructure. This could allow companies to is more important than intelligent build BI capabilities on the ERP system, or functionality. other components that can be regarded as intelligent. Therefore, an I-ERP system, Julie argues is a smart solution rather than intelligent functionality.

Q20: What is John says that no one thought it was Julie says that the differences between the I- Eric states the use of intelligent technology in the difference possible with self-learning systems, this ERP and traditional ERP is that they are more an ERP system eliminates processes that would between a I- was only displayed in science fiction he flexible, intuitive, has more functionality and have been performed manually. Eric adds that ERP and a adds. Another reason, John states is more strongly integrated. Julie bases these I-ERP compared to more traditional ERP traditional because computing power has become answers on her previous definition of a I-ERP, systems are more streamlined, while requiring ERP? cheaper in recent years, and more or rather intelligent infrastructure. less steps to complete a task. Eric also adds reliably available. In today’s market, that the reaction chain is far superior in an I- computing power is not a factor that ERP system. concerns most companies.

Q21: Why do John says that we live in a society that Julie re-states that it depends on which Eric explains that almost anything can be business need is constantly striving for progress in business the ERP is intelligent for. Business automated and made to be more efficient with I-ERP? technology. John explains because have different needs, therefore one business the help of I-ERP. Eric states that employees automation and minimizing errors are might be more inclined towards predictive are an expensive cost for companies; machines always factors that will push analytics, while another business does not can work 24/7, they are faster and do not development of digitalization, it is an exhibit the same need for the technology. require pay. Eric commented that repetitive inevitability that self-learning systems From Julie point of view the benefit of using a tasks that can be replaced by machines is an will become the norm. John draws a flexible ERP system that can add components inevitability; people should focus on more parallel between automation in the after their companies needs is superior than important topics in life, such as our industrial sector and administration. compared to a system that has ML or BI environment. When speaking about automation in functionality. Julie argues this point because factories it seems normal, but while she believes it is more important for a business speaking about automation in the to be flexible, needs for companies simply context of administration it sounds like changes over time. a revolution. John emphasises that many of the tasks performed today that are repetitive can be replaced by technology. The technology is far more effective than humans are at tasks, cost less money and makes fewer errors.

Q22: Do you John says that he believes that I-ERP Julie states that because she believes that an I- Eric emphasises that both an I-ERP system and think that I- can support businesses through ERP is not a specific system, rather the traditional ERP system can support a business, ERP can automation. John states that if a components of the system are intelligent. The it all boils downs the companies needs. support a human can perform a task, it is not use of these components can yield business business? impossible for a computer to fulfil the improvements in many shapes and forms. same need. If a task has repetitive steps that require minimal deviation a machine can often perform better than

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humans. Even if the task changes, a company could simply change the steps that the machine takes to complete the task. Tough John adds that the market is not mature yet, he predicts that these are the last few years of conventional ERP systems.

Q23: What John refers to previous statements, Julie refers to her previous statement of I-ERP. Eric continues with his previous example of advantages that I-ERP systems can support Components added on the ERP system can invoice interpretation; he compares a company does a business through reducing errors, costs yield automation of processes, insights, with 3-5 invoices a month to a company with business gain and improving effectiveness. reports, etc.; it depends on what component is upwards of thousands. In this scenario the from using a added on the ERP system. Julie states that the company with 3-5 invoices does not gain an I-ERP? needs of a company can be fulfilled by adding equal benefit compared to the other company. components on their existing ERP system. Julie Eric acknowledges that regardless of company adds that not all the companies exhibit the size, an I-ERP system will support a business same needs; instead companies should be better than a traditional ERP system. Eric focusing on having a flexible ERP system that explains that benefits for companies in this supports integrations of components rather case is corelated towards differences in needs, than focusing on intelligent functionality of an i.e. smaller companies does not exhibit the ERP system. same needs as large ones. Q24: What John explains because he is not entirely Julie says that it depends on which component Eric states that the current I-ERP systems have disadvantages confident, but it could be a critical of the system that is being used. For some not matured enough. Eric adds that for some lies with the perspective of machines replacing companies it might not be necessary to use professions their does not exists an I-ERP usage of I- humans. Though John adds throughout them, essentially the intelligent functions of system that can support their needs. Eric ERP? history, jobs have been replaced by the system become useless for the company. stressed that this problem is just a question of machines, such as spinning jenny, time along with adoption. Eric added that for factory workers etc. John states other companies or authorities handling sensitive tasks will be needed when technology data, an I-ERP system might not be suited, replaces conventional administrative because of the restraints of the data handling work; such as controllers, ML trainers, enforced by law. etc.

The three following citations are from John, Eric, and Julie’s perspective about what defines an I-ERP system.

“For me it is not about the intelligent ERP system rather it is more towards the smart solution. The smart solution from my perspective is an ERP system where a company can swap out components at a whim of need. […] With surgical procedures companies can remove or add components, this is more cost effective compared to changing an entire ERP system. I believe that the architecture of an ERP system is more important than the functionality. ”- John

“I perceive an I-ERP system as a system that can learn from its experience. The I-ERP system has functionality that learns from mistakes, the simplest showcase of this functionality is invoice interpretation. ”- Eric

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“I think the questions answer is: who is the ERP system smart for? Then it is usually industry specific. It is so that you may be able to create a smart ERP system that provides insights. However, you need to know what information is important to provide the insights. For instance, customer orders consist of certain components, however, there are other factors that affect them from outside sources. It can differ in different industries. In the case that the ERP system is intelligent, it would be interconnected, you would see indirect or direct relationships and connections between things surrounding the business. This is something that exists today, but I would not say that it is specifically the intelligent ERP systems, but rather the infrastructure that allows you to embed BI functionality directly in the ERP system.” – Julie

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5 Analysis 5.1 Machine learning in ERP systems context Jenab et al. (2019, p152) explains that ERP systems can use machine learning to achieve predictive analysis functionality to display relationships in data structures. Similarly, Julie states that ML can be used for predicting business events; though she also argues that this is when ML is used in a BI systems context. Currently, Julie believes that ML is not used in ERP systems in the same way that BI systems implement the technology. Rather, certain components of the ERP system use ML functionality rather than ML being a central point of the ERP systems root functionality. The following quote from Julie displays her view of ML in an ERP systems context. “(ML) Not in itself, rather it can be components in the ERP system that use ML. […] If we assume from the definition we have today of ML, an ERP system does not currently have rudimentary ML functionality. As stated before, then it is components or modules that have ML capabilities. In that case, there exists well know algorithms that an ERP system uses that could be defined as ML. But from my point of view, I would not define these algorithms as ML; because the model is never trained with datasets.” – Julie

Similarly, John also explained that ML functionality is something that is not seen often in ERP systems. John also reinforces the statement made by Julie; while also coinciding with points made by Jenab et al. (2019, p152) to what ML can achieve for business improvements; these being minimizing errors and analysing vast data structures. Eric did however not agree with Julie and John that ML is not used in ERP systems; he argues that ML is something that is adopted in some ERP systems. Though Eric elaborates that this functionality is often added to the ERP system. But from Eric’s perspective it is in its essence a part of the ERP system. The sequential quote from Eric explains his view: “I have seen ML being used in ERP systems, the easiest example is invoice interpretations […] I am aware of at least a few ERP systems that use ML techniques, another example is from the timber industry which has sensors that read data and transmit (IoT) it to an ERP system; ML is often used to detect errors.” – Eric

Eric, John, and Julie all agree towards a similar point to what businesses can achieve by using ML technology such as: minimizing errors, achieving predictive analysis capabilities and some forms of automation. Similarly, the participants and Jenab et al. (2019, p152) share a coherent theme to the use case of ML for company improvements. However, the points made regarding self-improvement of the ERP system is not implied by either John or Julie. Eric on the other hand, states that ERP systems can in fact exhibit self-improving functionality. Eric states that self-improvement of an ERP system can be showcased through invoice interpretation, which he argues directly correlates to ML usage in ERP system. The self-improvement of an ERP system corelates to Jenab et al. (2019, p159) statement that a I-ERP system uses ML for deriving self-improvements to the system, which then entails useable analytics such as

29 predictive ones. Sequentially, Julie strongly emphasizes that ML technology is mainly used by companies for achieving predictive analysis capabilities, which corresponds to the end effect of using ML described by Jenab et al. (2019, p159). John and Julie are in unison that ML is a component of an ERP system. John argues that it is a function of a component that is added on to the system. Eric never remarked if he perceived them as separate entities, other than the remark that it is often not a root function of the system. However, this will be discussed moreover in the later paragraphs of I-ERP analysis. Julie argued that the algorithms in use of ERP system can be perceived as ML but are in fact not a ML algorithm because no datasets where used to train the model. Julie and Witten (2016, p23) coincide in their definition of that an ML algorithm need datasets to be considered ML models. Therefore, Julie’s definition of certain algorithms in ERP system are in fact not a ML model. 5.1.1 Limitation of machine learning Eric does however oppose Jenab et al. (2019, p152) point made about adaptability; from Eric’s perspective ML is currently too inflexible. Eric explains that ML is currently in an immature state, it has problems with minor changes; meaning that if a process is changed, e.g. the invoice layout, the algorithm needs to be retrained. Because of this it leaves the previous iteration of the algorithm unusable, hence the need for total retraining from the new dataset. The following quote from Eric highlights his stance on ML usage in a business context. “If a change occurs in a process that ML is being applied towards the ML technology needs to relearn it. There currently exists limits to what ML can achieve, it is not very flexible.”- Eric Another limiting factor to using ML that was present in all the interviews were regarding market adoption. They all agreed that one of the largest factors limiting ML, is market adoption. The interview participants all stated likewise that the market is too immature; factors such as management not seeing the benefit of using ML technology was a theme presented throughout all the interviews, rather than the limiting factor being ML. Therefore, Eric, John, and Julie all point towards market adoption as the current root cause problem of ML. The quote from Julie derives a synopsis of the consensus between the participants. “Machine learning adoption in the market is quite immature. There currently exist tools, but a minority of users. There is more that needs to be done in the field.”- Julie 5.1.2 Bias regarding machine learning Julie and Eric commented on issues they perceive with bias in ML. Julie states that when training a ML model, there are steps of precautions that are wise to apply. Julie explains that a user of a ML technology must exhibit a critical mindset regarding the reality of a certain scenario; meaning that having a critical mindset of where the data originates from, how it is applied, how it was collected also how it is used. The following quote displays Julie’s predispositions regarding how to correctly handle and apply datasets in a ML context. “[…] When working with ML you must clean the data, while also thinking about the reality of the scenario which the data is being applied too. Other important things too keep in mind are,

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where the data originates from, how the data was collected, and which factors impacts the data […]” – Julie

Similarly, Eric advocated that if bias data, or incorrect data is used with a ML model the output will be a deficient answer. Though Eric stated that he believes that ML is less skewed with data analytics compared to humans, because of the lack of emotions. The following citation from Eric highlights his stance:

“I believe that ML can become less bias than us humans can, for the reasons of our emotions. On the other side if you give ML incorrect data, bias is of a high probability. A ML model will always interpret the information it was provided, even if it is faulty.” – Eric

Fundamentally Julie’s and Eric’s argument regarding bias share a theme. However, Julie’s argument highlights solutions to the key issue of biasness in ML. Rather Eric’s argument is more a barebone approach; meaning that if the data is poor, it corelates to a poor result and wise versa.

Julie’s argument does however bring up a point of contention with Howard et al. (2017, p1) view of poor datasets, which can imply skewed results. Howard et al. (2017, p1) argues that commercially available datasets are often heavily skewed towards a specific demographic; meaning that marginalized groups are often excluded or heavily underrepresented. The point of Howard et al. (2017, p1) argument compared to Julie’s is that it is hard, or near impossible to distinguish if datasets are favoured towards one certain demographic. However, Julie highlights that a critical mindset of the dataset’s origin, context in application and collection is a fundamental principal when using ML technology. Differently Eric’s argument is less saturated compared to Julie’s or Howard et al. (2017, p1) statements. Eric’s point that poor data provides poor results, which is a quite novice approach and provides insufficient precautions compared to the statements made by Howard et al. (2017, p1) or Julie’s. Despite this, Eric’s point that ML technology is less bias than humans, provides a different focal point in the discussion. Compared to Howard et al. (2017, p1), Eric argues that ML technology is less bias because of the exclusion of human emotion which often corelates to human error. However, fundamentally Eric’s argument does not share the base principle in the discussion regarding datasets in a ML context; therefore, it exceeds the scope of the discussion. 5.2 Business intelligence in an ERP context Julie and John both state the same function of the BI system is to visualize a company’s data, while Eric argues that BI has different definitions depending on the context it is being applied to. Eric does however add a definition that implies the same meaning as Julie and John’s. Similarly, Chou et al. (2005, p343) explains that companies use BI systems to create reports and structure data, which directly corelates to what the respondents reciprocated in their previous statements. However, John does explain that from his perspective there is a superior way to structure a company’s data; MDM systems, although this is outside this thesis scope it is worth mentioning.

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From Eric’s perspective the ERP system could be compared to the heart and lungs of a company’s operation, while a BI system is comparable to the human senses. This theme is also echoed in John’s description of an ERP and BI systems relationship. Julie weaves the definition of the relationships between ERP and BI system in the following citation: “An ERP system is a proper and well-rounded tool for recording transactional data and supporting business processes. An ERP system can track and manage these transactions. Later when a company wants to aggregate the information stored in the ERP system, they can use a BI system; for spotting trends, decisions making, spotting potential errors or dangers and to view a company’s key performance indicators.”- Julie

The relationship between BI and ERP has a coherent theme throughout all the participants responses. The respondents all stated that a BI system takes information stored in the ERP system for performing analysis. Julie added that BI systems do not exclusively have to use ERP system data for analysis, sometimes it involves other systems. All the participants agree that fundamentally these two systems are different. However, Eric argues from his experience that these systems has become closely coupled, so much so that they are at times hard to distinguish from one another. Eric’s statement corresponds to Rahimia and Abbasi Rostamib (2015, p11) views of ERP and BI systems integration, for instance as not to treat the systems as entirely separate, rather to view them as a symbiotic relationship. This relationship is something Julie and John also highlighted, for instance, usually the BI quarries the data stored in the ERP system too derive reports and analysis. Sequentially all the participants stress the importance of integration between BI and ERP systems. However, John and Julie emphasize that integration derives automation of company processes, while Eric says that the end effect is alleviating business decisions for companies. Rahimia and Abbasi Rostamib (2015, p11) concurs with Eric’s statements of end effect of a correct integration of an ERP and BI system is alleviating in business decisions. Although Rahimia and Abbasi Rostamib (2015, p11) argues from the point of structuring data sets in a BI system from an ERP system standpoint, which corroborates more in line with John and Julie’s view of the relationship between BI and ERP systems. However, Zhou (2012, p.270) can associate the differences in the participants answers with the explanation that both end effects are adequate, e.g. that a correct integration attains to strict process control which in turn then improves the decisions making for a company. Another point that the participants disagreed towards was the most important aspects of a BI system. John explained he thought it was the structure of data in a BI system; while Eric argued towards reliable data that is correct. John does however add that a correct data structure correlates to correct data in the system. On the contrary, Julie emphasizes her view of the most important aspect is dependent on the business, e.g. businesses express different needs. The following quote from Julie displays her stance. “There is no single part of a BI system that is more important than another, the determining factors are which BI tools is in use, and the companies’ prerequisites. Every tool has its positives and negatives. “– Julie

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Although quantifying the most important part of a BI system is not something that is entirely measurable, which is displayed by Julie’s previous statement. It simply varies from business to business.

Finally, this corelates to what all the participants agreed on, which is that the most important aspects of an ERP and BI system are that it fits the business, meaning the most benefits corelates towards best practice. However, Julie added that if a single component of an ERP and BI systems had to be chosen as the most essential; it would be full integration. Julie’s answer infers a similar meaning compared to John’s; structure and integration both resolve in flexibility letting companies add components at will. 5.2 Internet of Things All the participants are in consensus to what contributing factors for companies using IoT technology in the context of ERP systems; this being automation. Julie and Eric argue that the use case for IoT is both wide and broad, the technology is merely limited by the imagination of the company. The quote from Eric illustrates the point:

“The application for IoT is near endless, currently there is too few companies willing to try IoT because the technology is untested in some fields. […] The customer I meet, often say why would we need this? The market is simply not aware of the benefits of using IoT, which is automating processes.”- Eric

A similar theme is presented between Julie, John, and Eric of what IoT can support in an ERP system. All participants agree that IoT can support business and ERP systems with many different aspects such as inventory management, production and decision making. Julie and Eric also made remarks that IoT technology has matured greatly from previous iterations. Similarly, all the participants reciprocated a critical perspective towards the IoT technology, this was in a context when companies were handling sensitive data, i.e. personal information. Eric also adds the risk of IoT replacing human labour, but comments that this is more of a philosophical perspective rather than being a substantial risk for the use case of IoT.

A similar theme is also corelated to what Majeed and Rupasinghe (2017, p28) expressed about the possibility of IoT in an ERP systems context. Likewise, Majeed and Rupasinghe (2017, p28) explained that IoT devises can supply automation for a company in a wide range of use cases, such as data input, manufacturing, allowing for a more flexible productions process etc. There is a direct correlation to what Majeed and Rupasinghe (2017, p28) has stated compared to what the participants answered. However, none of the participants argued that IoT can contribute to benefiting factors such as competitive advantage for companies, like Majeed and Rupasinghe (2017, p28) mentioned. Although an important clause to have in mind is that Rupasinghe (2017, p28) research is based in the manufacturer field, which may vary from the participants background. However, arguing in the basis for automation being the main benefiting factor of using IoT; it can be stated that automation in a manufacturing context is in

33 fact a competitive advantage. Therefore, the participants and Majeed and Rupasinghe (2017, p28) are in unison in what IoT offers for a company, which is automation of processes.

Contrary to Eric’s previous quote from the first paragraph, Majeed and Rupasinghe (2017, p35) emphasizes that the main issue in the manufacturing field lies with traditional ERP systems lacking support for IoT technology. The differences between Eric’s argument and Majeed and Rupasinghe (2017, p35) is quite vast. There is no clear or concise answer that can be argued, only hypothetical ones. For this reason, some speculative answers could be that Majeed and Rupasinghe (2017, p35) arguments are formed from researching companies that have implemented IoT devise in their businesses. Contrary, Eric’s customer sample size of IoT implementation is most likely smaller, which corelates to his answer that market adoption is the issue. From this perspective there is a way to explain the differences in the answers. However, other possible answers could be demographic, markets, field of expertise etc. 5.3 Intelligent Enterprise Resource Planning The participants shared some themes throughout the interviews about their perspective of I- ERP. For instance, both Julie and John share a similar stance of I-ERP being a system with a smart solution that supports future integration, rather than having inherent intelligent functionality. This perspective is showcased in the following quote made by John.

“For me it is not about the intelligent ERP system rather it is more towards the smart solution. The smart solution from my perspective is an ERP system where a company can swap out components at a whim of need. […] With surgical procedures companies can remove or add components, this is more cost effective compared to changing an entire ERP system. I believe that the architecture of an ERP system is more important than the functionality. ”- John

Likewise, Julie argues that the case of I-ERP mainly boils down to an efficient, or smart infrastructure of the ERP system. Similarly, to John’s statement Julie reciprocates that a I-ERP system must be interconnected with different parts of the system. However, she argues that simply connecting BI functionality directly in an ERP system is not I-ERP, rather it is the infrastructure of the ERP system. Nonetheless, Julie adds that the I-ERP systems are her view industry specific, which is a point not brought up by John. The following quote from Julie’s highlights her arguments about infrastructure, market environment and I-ERP.

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“I think the questions answer is: who is the ERP system smart for? Then it is usually industry specific. It is so that you may be able to create a smart ERP system that provides insights. However, you need to know what information is important to provide the insights. For instance, customer orders consist of certain components, however, there are other factors that affect them from outside sources. It can differ in different industries. In the case that the ERP system is intelligent, it would be interconnected, you would see indirect or direct relationships and connections between things surrounding the business. This is something that exists today, but I would not say that it is specifically the intelligent ERP systems, but rather the infrastructure that allows you to embed BI functionality directly in the ERP system.” – Julie

Eric argues that an I-ERP system from his perspective is a system that learns from itself. Contrary to the other participants which argued for the smart infrastructure or solution, Eric’s perspective provides a I-ERP system favouring intelligent functionality such as ML, which is displayed the following citation:

“I perceive an I-ERP system as a system that can learn from its experience. The I-ERP system has functionality that learns from mistakes, the simplest showcase of this functionality is invoice interpretation. ”- Eric

However, Eric does advocate I-ERP systems do not currently fulfil the needs in all trades. Eric argues that the I-ERP system is still immature in certain markets, withal he adds that this problem is a question of time rather than a limitation of the system. The statement made about the market corelates to Julie’s previous quote: “Who is the ERP system smart for?”. Julie’s argument that the I-ERP systems can be a question of specific market needs, strongly corelates to Eric’s view that I-ERP systems are immature in certain markets. This points to a specific contention between Eric and Julie meaning that I-ERP system can be market specific.

A similar theme is presented between Eric and Jenab et al. (2019 p.159), for instance, Eric argues that an I-ERP system can learn from its self-experience which corelates to Jenab et al. (2019 p.159) statement: an I-ERP system can also learn by its self-experience and by the requirements formulated by the company. Jenab et al. (2019 p.159) explanation corresponds to Julie’s definition of an I-ERP being market specific. The explanation from Jenab et al. (2019 p.159) is that an I-ERP system is formulated by the company’s requirements, which strongly emphasizes a link that an I-ERP is a market specific system. However, Jenab et al. (2019 p.159) and Julie do differ in the interpretations that I-ERP systems have inherent predictive analysis functionality, i.e. the BI or ML capabilities as a fundamental function of an I-ERP system. Here both Julie and John state that in the case of predictive analysis functionality, it is an added component. Contrary to John and Julie, Jenab et al. (2019 p.154) explains I-ERP as a system that uses advance analytics and machine learning for processing data, resulting in useful information. The main synapses from John and Julies points compared to Jenab et al. (2019) is that an I-ERP system in itself is not intelligent, it is rather the added functionality of i.e., BI, ML or IoT that derive the benefits. However, Julie’s previous quote shares some points of how an I-ERP system can be

35 interconnected to show relationships between data, which strongly corelates to Jenab et al. (2019 p.154) explanation of an I-ERP systems benefits, such as identify customer behaviour, anomalies, displaying relationships between the data structures etc. The differences between Julie’s and Jenab et al. (2019) view of I-ERP is a question of how the definitions of an I-ERP system is interpreted, therefore; the question can be resided as: is it a smart infrastructure rather than an inherent intelligent functionality of the ERP system? This is what both John and Julie argued for, rather than placing the I-ERP system and ERP system as two separate entities, these two systems can be viewed as one and the same, only the I-ERP system may have more components added to it.

Another point of contention is Jenab et al. (2019 p.160), arguments of what an I-ERP can improve for a company such as: increased flexibility, automation, error detection and improved speed of operation along with quality. John concurs with Jenab et al. (2019 p.160) improvements of an I- ERP system, citing similar effects such as reducing errors and improved speed of operation. Comparing Jenab et al. (2019 p.160) statement to Julie’s answer, which entails a similar theme, though argued from the stance that it is rather the components that yield the improvements rather than the I-ERP system. However, Julie also argues for a focus on flexible integrated ERP systems that can support future integration, for future needs to be adequately supported. Another theme that is coherent between Julie and Eric is the shared view that the needs of companies are not universal; therefore, some functionality of an I-ERP system as defined as Jenab et al. (2019) are not needed in certain fields.

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6: Discussion The purpose of this study is to examine what constitutes an I-ERP system and its potential from a business perspective; this question was answered through they research questions, which will entail the consecutive paragraphs.

What role does IoT, ML, and BI have in an I-ERP system?

The role of each technology plays a part in the formation of an I-ERP system. For the case of IoT technology, it is used for automation, there were no wide differences in the participants responses compared to the literature. Therefore, IoT’s role is a clear case of automation being the contributing factor to an I-ERP system. Secondly, ML’s role presented a clear use case for businesses between the participants and the theory. There is a consensus of what ML can be used for deriving business benefits, which are: predictive analytics, showing relationships between data structures, minimizing errors along with certain forms of automation. The contention between the literature and the participants answers where if ML is an inherent functional part of an I-ERP, likewise this issue was raised similarly by some participants about BI being an essential part of an I-ERP. Finally, the participants and the theory all agreed too what BI offers for a business, this being: alleviating in decision making, reports, and analyses about current or past business affairs.

However, there was disagreement towards if ML and BI are an inherent function of I-ERP systems. Only one of the participants countered the belief of the two other participants whether I-ERP does in fact exhibit BI and ML capabilities. Because of these two crossroads between the participants there are two explanations that both entail an explanation. First, the participants work experience with I-ERP may vary, therefore creating a difference in opinion. However, the more likely argument is that this study had to few interview participants for fully saturating the topic. Therefore, more participants are needed to confidently describe a full conclusion about what fully constitutes an I-ERP system.

Which possibilities and limitations does an I-ERP system present for a business?

The possibilities presented for businesses using an I-ERP system are entailed by the technology surrounding the system. Businesses have different needs along with different prerequisites in certain markets. Likewise, this opinion was reflected by the participants along with a stern belief that the market is still to immature for these technologies. However, there was other issues raised with ML such as when sensitive data is handled, or how incorrect datasets can provide a skewed picture of events. The main synopsis about an I-ERP systems possibility for a business is that it simply depends on the business needs. Certain businesses along with markets display vast differences in needs and uses for these technologies. Hence, an I-ERP system is not a perfect fit currently for all businesses. Therefore, an argument could also entail that because of the immaturity of the I-ERP system it can conclude that the market is also to immature. 37

Suggestion for future studies The first suggestion for future studies is to conduct more empirical work. Because of time restraints along with the issue of Covid-19, interview candidates where sparce. Future studies may need many more to fully saturate the findings. Another reason for more participants is to get a wider array of perspectives regarding I-ERP. Finally, a comprehensive study about market adoption of I-ERP is also an interesting topic that needs further research.

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References

Carlsson-Wall, M. & Strömsten, T. (2018). Managing Digital Transformation. Göteborg: BrandFactory. 175-187.

Ching-Hai, L., Ting-Jou, D. & Zheng Han, L. (2018). Integration of ERP and Internet of Things in Intelligent Enterprise Management. International Cognitive Cities Conference (IC3), 246-247.

Chou, D. C., Bindu Tripuramallu, H., & Chou, A. Y. (2005). BI and ERP integration. Information Management & Computer Security, 13(5), 340-349.

Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting- tool-that-showed-bias-against-women-idUSKCN1MK08G [2019-03-04]

Howard, A., Zhang, C., & Horvitz, E. (2017). Addressing bias in machine learning algorithms: A pilot study on emotion recognition for intelligent systems. In 2017 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO) (pp. 1-7). IEEE.

Oodles ERP. (2018). Intelligent ERP: The Rise of New Opportunities for Businesses https://erpsolutions.oodles.io/blog/intelligent-erp/ [2019-03-20]

Jenab, K., Staub S., Moslehpour S., & Wu, C. (2019). Company performance improvement by quality based intelligent-ERP. Decision Science Letters, 8(2), 151-162.

Ledford, J. (2017). How Intelligent Does Intelligent ERP Need to Be? Retrieved from https://it.toolbox.com/blogs/erpdesk/how-intelligent-does-intelligent-erp-need-to-be-052317.

Lee, J. H., Shin, J., & Realff, M. J. (2018). Machine learning: Overview of the recent progresses and implications for the process systems engineering field. Computers and Chemical Engineering, 114, 111–121.

Majeed, A. A., & Rupasinghe, T. D. (2017). Internet of things (IoT) embedded future supply chains for industry 4.0: An assessment from an ERP-based fashion apparel and footwear industry. International Journal of Supply Chain Management, 6(1), 25-40.

Nilsson, N. J. (1998). Introduction to Machine Learning. An Early Draft of a Proposed Textbook.

Patel, R. & Davidson, B. (2011). Forskningsmetodikens grunder: Att planera, genomföra och rapportera en undersökning. Lund: Studentlitteratur.

Rahimia, E., & Abbasi Rostamib, N. (2015). Enterprise Resource Planning and Business Intelligence: The Importance of Integration. International Journal of Management Academy, 3(4), 7-14.

Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). Membership inference attacks against machine learning models. In 2017 IEEE Symposium on Security and Privacy (SP) (pp. 3-18). IEEE.

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.

Zhou, L. (2012). Research on the integration application of business intelligence and ERP. In 2012 International Conference on Management of e-Commerce and e-Government (pp. 269-271). IEEE.

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Appendix 1: Contest form Samtyckesblankett

Samtycke till att delta i studien: Jag har skriftligen informerats om studien och samtycker till att delta. Jag är medveten om att mitt deltagande är helt frivilligt och att jag kan avbryta mitt deltagande i studien utan att ange något skäl. Min underskrift nedan betyder att jag väljer att delta i studien och godkänner att Karlstads universitet behandlar mina personuppgifter i enlighet med gällande dataskyddslagstiftning och lämnad information

Underskrift

......

Namnförtydligande Ort och datum

......

Alex Hedenstrand (Student) E-post: [email protected] Mobil: 076-715 68 71

Linda Bergkvist (Universitetslektor och handledare) E-post: [email protected] Telefon: 054 700 17 79

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Appendix 2: Informations letter Informationsbrev

Information om studien: Studien vill undersöka vad som utgör ett intelligent affärssystem (I-ERP) samt vilka möjligheter systemet presenterar från ett affärsperspektiv. Personuppgifterna behandlas enligt ditt informerade samtycke. Deltagande i studien är helt frivilligt. Du kan när som helst återkalla ditt samtycke utan att ange orsak, vilket dock inte påverkar den behandling som skett innan återkallandet. Alla uppgifter som kommer oss till del behandlas på ett sådant sätt att inga obehöriga kan ta del av dem. Uppgifterna kommer att bevaras till dess att uppsatsarbetet godkänts och betyget har registrerats i Karlstads universitets studieregister för att sedan förstöras. Karlstads universitet är personuppgiftsansvarig. Enligt personuppgiftslagen (dataskyddsförordningen från och med den 25 maj 2018) har du rätt att gratis få ta del av samtliga uppgifter om dig som hanteras och vid behov få eventuella fel rättade. Du har även rätt att begära radering, begränsning eller att invända mot behandling av personuppgifter, och det finns möjlighet att inge klagomål till Datainspektionen. Kontaktuppgifter till dataskyddsombudet på Karlstads universitet är [email protected]

Alex Hedenstrand (Student) E-post: [email protected]

Mobil: 076-715 68 71

Linda Bergkvist (Universitetslektor och handledare) E-post: [email protected] Telefon: 054 700 17 79

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Appendix 3: Interview guide Intervjuguide Alex Hedenstrand Gå igenom samtyckes blankett samt ge en kort introduktion till vad studien kommer att handla om. OSB! Fråga om samtycke för att spela in samt presentera hur inspelning kommer att lagras. 1: Start frågor för att bygga en kort bakgrund och för att bli varma i kläderna.

• Vad är din titel/jobb? • Vad har du för bakgrund? • Hur länge har du jobbat inom denna verksamhet? • Berätta gärna lite hur din arbetsdag ser ut för dig förra veckan. 2: Inledande öppna frågor om området.

• Hur arbetare din verksamhet med affärssystem? o Vad stödjer affärssystemet?

• Hur arbetare din verksamhet med BI? o Vad stödjer BI systemet?

• Vilka delarna är det viktigaste inom ett BI/affärssystem system? o Varför är dessa delar viktiga? 3: Utvecklande frågor

• Hur skulle du definiera skillnaden mellan ett affärssystem kontra BI? o Hur ser relationen ut mellan dessa system? • Hur viktigt är integration mellan olika Bi och ERP? o Varför då?

• Vad anser du utgör ett intelligent affärssystem? o Vad är skillnaden mot ett traditionellt ERP? o Varför behövs intelligenta ERP? • Anser du att intelligenta affärssystem kan stödja/hjälpa företag? o Vilka fördelar kan man utnyttja av systemet?

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o Vilka nackdelar finns det i nuläget?

4: Tekniska frågor

• Hur jobbar ni med maskinlärning (i koppling till BI samt affärssystem)? o Vilka problem ser du i nuläget med maskinlärning? o Vilka fördelar finns det att utvinna från maskinlärning?

• Vilka roll fyller ML i ett BI system? o Finns det ERP system som utnyttjar ML tekniker? o Anser du att maskinlärnings tekniker kan orsaka partiskhet? • Hur jobbar ni med IoT i koppling till affärssystem? o Vilka möjligheter finns det med IoT? o Vilka nackdelar finns det med Iot? o Vad kan IoT stödja inom ett affärssystem? 5: Avslutande frågor.

• Har du något att tilläga?

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Appendix 3 (English version): Interview guide Interview guide Alex Hedenstrand Go through the consent form and give a brief introduction to what the study will be about. OSB! Ask for consent to record and present how recording will be stored. 1: Introduction questions to build a short background. • What is your title / job? • What kind of background do you have? • How long have you been working in this business? • Feel free to tell us what your working day looks like for you last week.

2: Initial open questions about the area. • How does your business work with business systems? o What does the business system support? • How does your business work with BI? o What does the BI system support? • Which parts are the most important in a BI / business system? o Why are these parts important?

3: Developing questions • How would you define the difference between a business system versus BI? o What is the relationship between these systems? • How important is integration between different Bi and ERP? o Why? • What do you think constitutes an intelligent business system? o What is the difference between a traditional ERP? o Why is intelligent ERP needed? 44

• Do you think that intelligent business systems can support / help companies? o What are the advantages of the system? o What are the disadvantages at present? 4: Technical questions • How do you work with machine learning (in connection with BI and business systems)? o What problems do you currently see with machine learning? o What are the benefits of machine learning?

• What role does ML play in a BI system? o Are there ERP systems that utilize ML techniques? o Do you think that machine learning techniques can cause bias? • How do you work with IoT in connection with business systems? o What are the possibilities with IoT? o What are the disadvantages of IoT? o What can IoT support within a business system? 5: Closing remarks. • Do you have something to add?

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