Data Mart with Lean Six Sigma Concept for Performance Level Assessment in Knowledge Management Framework Jevgeni Sahno, Eduard Shevtshenko, Tatjana Karaulova Tallinn University of Technology, Tallinn, Estonia [email protected] [email protected] [email protected]

Abstract: Nowadays, the rapid growth of competition in the market place, forces the companies to guarantee to their customers a reliable, sustainable and quality proofing production system. In this paper we consider KM framework that includes well known IS tools like PDM and ERP system, PDM- ERP middleware and novel DM that is the core of our framework. The combination and application of different tools and methods in the general framework allows transparent data flow between different systems, analysis of production operations and failures occurring during the production process. Our DM will play the role of a company’s “dashboard” that is similar to the one that a pilot has in the cockpit that describe the performance of an airplane. A business or production system may have similar “dashboard” which shows the process performance. Here we consider the application in DM, the three first steps of DMAIC (Define, Measure, Analyse, Improve and Control) Six Sigma approach which characterise the nature of the problem to be solved. In Define step for the object or problem to be studied, we will use the combination of PR data, Faults Classification standard DOE-NE-STD- 1004-92 and the data from FMEA table. For the Measure step we will use raw data from ERP system and specified algorithm in DM that allows assessing the Severity, Occurrence and Detection rating and then calculating RPN values in FMEA table. Further, the RPN values will be converted into the per cent values that present a sigma performance level for every operation, product type and common production system. In Analyse step it will be possible to perform analysis of operations and failures occurred, also this step prepares the base for the next two steps (Improve and Control) of Six Sigma approach where will be improvements of production process. The integration of the first three steps of Six Sigma in DM will ensure a company to monitor the changes of the production system in real time.

Keywords: Knowledge Management (KM), Product Data Management (PDM), Enterprise Resource Planning (ERP), PDM-ERP middleware, Data Mart (DM), Production Route (PR) card, Faults Classification, Failure Mode and Effect Analysis (FMEA), Six Sigma, Sigma performance level.

1. Introduction The internal issues that many manufacturing companies face today are often surround the idea that companies know the problems that lie in front of them: not reliable production processes, bad product quality, problems with product On-Time-Delivery (OTD) and ect., but they do not understand the roots of these problems. The Pareto principle states that roughly 80% of the problems are called by 20% of the causes (Koch et al. 2004). Hence, all problems in manufacturing start from small causes, e.g., low labour qualification and unbearable working conditions and old technologies applied which lead to the bad quality of product. As the result these causes may lead, to the loss of customers’ expectations and consequently position on the market. To survive in the competitive market companies should be expedient in technological resources and they should be able to demonstrate to a customer innovativeness, the quality of products and a functional quality proofing production system (Lõun et al. 2011), (Riives et all. 2012). A customer who is satisfied by the first buying experience needs to be satisfied again. Most customers care about quality willing to pay more than average market price to obtain product and see “the extras” as worth the additional expense (Matzler et al. 1998).

Today Six Sigma is a well-known methodology for quality and process improvement with an emphasis on defect prevention rather than defect detection. It is intended on reducing of variation and waste in the process. The bottom line results and competitive advantage are to be improved. Knowledge Management (KM) on the other hand is aimed at creating competitive advantage too, by creating new knowledge in an organisation (Arendt 2008). This paper is aimed at introducing the general idea of using DMAIC Six Sigma approach not only to improve the production processes but also to use it to create new knowledge within an organization too.

2. Literature Review Organizational knowledge is now recognized as a key resource and a variety of perspectives suggest that the ability to marshal and deploy knowledge dispersed across the organization is an important source of organizational advantage (Teece 1998). KM is performance of the activities involved in discovering, capturing, sharing, and applying knowledge in terms of resources, documents, and people skills to enhance, in a cost-effective fashion, the impact of knowledge on the company´s goal achievement (Becerra-Fernandez et al. 2004). KM initiatives in organizations are becoming important and firms are making significant Information Technology (IT) investments (Davenport et al. 1998). As knowledge is only created by individuals, managing it requires a framework (Nonaka et al. 1995). Therefore, we apply in our KM framework well known IS tools that accept data as input and process them into information products as output (O’Brien et al. 2008). With the help of IS technology, a company can become competitive in all phases of its customer relationships (Blake et al. 1984).

2.1 Data Mart Overview, Concepts and Tools Applied Data Mart (DM) is a repository of data gathered from Operational Data Stores (ODS) and other sources. The goal of the DM is to meet the specific demands of a particular group of knowledge in terms of analysis, content, presentation, and ease-of-use. DM present the data in terms that are familiar or in a format user want to see (Berson et al. 1997). Implementations of DM in several domains such as commerce, telecommunication and medicine have been thoroughly researched. It was proven to be useful and effective in the particular application domain of clinical research in heart surgery in Germany (Arnrich, et al. 2004). A similar research has not been done in manufacturing, despite of the potential benefits. Different reasons can be outlined. The majority of researchers in manufacturing are not familiar with DM methods and tools; many IT researchers are not familiar with manufacturing; the few researchers that have skills in both DM and manufacturing area may not have an access to manufacturing enterprise data (Wang 2007).

2.1.1 Six Sigma Six Sigma is a project-driven management approach intended to improve the organization’s products, services, and processes by reducing defects in the organisation. It is a business strategy that focuses on improving customer requirements understanding, business systems, productivity, and financial performance (Kwak et al. 2004). Six Sigma’s DMAIC method offers a thorough roadmap for analysis and diagnosis, driven by powerful tools and techniques (Heuvel et al. 2006). . Define is the first step of the Six Sigma process. During this step, a problem is identified and quantified in terms of the perceived result. The product and/or process to be improved is identified, resources for the improvement project are put in place, and expectations for the improvement project are set. The focus of the problem-solving strategy is kept on the customer's primary requirements. . Measure step enables an organization to understand the present condition of its work process before it attempts to identify where they can be improved. It provides the substance for the problem statement. During this step, the critical to-quality (CTQ) characteristics are defined, as well as the defects in the process or product and a physical model of the process is developed through graphical analysis. All the factors that the outputs are evaluated, and potential effects they have on failure modes are identified. The Measure step is based on valid data, so it eliminates guesswork about how well a process is working. . Analyse step adds statistical strength to problem analysis. Statistical analysis identifies a problem´s root cause by determining which factors contribute to the observed variation and how much of the total variation is explained by these factors. It can be used to calculate how much variation each dominant factor contributes to the overall problem. Interaction effects among the process variables can be observed through statistical testing. . Improve step aims to develop, select and implement the best solutions with controlled risks. The effect of the solutions that are then measured with the Key Performance Indicators (KPI) developed during the Measure step. . Control step is intended to design and implement a change to effect improvements based on the results demonstrated during the Improve step. The human element of the process is engaged to implement and manage changes in daily work activities required to achieve the targeted result of the change project. The Control step involves monitoring the process to ensure it has the discipline required to implement the change, capture the estimated improvement benefits, and maintain performance gains over the long term (Gregory 2004). Six Sigma utilizes analytical tools and processes to measure quality and eliminate variances in processes. The objective of Six Sigma is to produce near perfect products and services that will satisfy customers (Joanna et al. 2007). Dating back to the mid of 1980s, applications of the Six Sigma methods allowed many organizations to sustain their competitive advantage by integrating their knowledge of the process with statistics, engineering, and project management (Anbari 2002). Motorola was the first company who launched a Six Sigma project in the mid-1980s (Rancour et al. 2000). Today Six Sigma has made a huge impact on industry and yet the academic community lags behind in its understanding of this powerful strategy. It lacks a theoretical underpinning and hence it is academic responsibility to bridge the gap between the theory and practice of Six Sigma (Antony 2004). From the statistical point of view, the term Six Sigma is defined as having less than 3.4 defects per million opportunities or a success rate of 99.9997% where sigma is a term used to represent the variation about the process average (Antony et al. 2002). If a company is operating at three sigma levels for quality control, this is interpreted as achieving a success rate of 93.32% or 66807 defects per million opportunities. Therefore, the Six Sigma method is a very rigorous quality control concept where many organizations still performs at three sigma levels (McClusky 2000).

2.1.2 Lean Manufacturing Lean Manufacturing is a comprehensive philosophy for structuring, operating, controlling, managing and continuously improving industrial production systems (Detty et al. 2000).

2.1.3 Lean Six Sigma Lean Six Sigma is an integration of Six Sigma and Lean Manufacturing, both quality improvement programs originating from industry. Until recently, they were implemented as separate programs. The advent of Lean Six Sigma allows companies for the first time to combine the power of these methods into a single integrated toolkit (Aon Consulting, 2003). Nowadays these tools are highly complementary. Lean Six Sigma promotes continuous improvement of processes by both analysing sources of waste and reducing waste (Stephens 2007). Lean and Six Sigma contain a complementary range of tools and techniques which in reality will inevitably require a range of both of them. DMAIC is an effective problem solving structure that helps to be clear about what you are trying to achieve. For that reason, it is recommended to combine these techniques within a DMAIC structure (Brook 2010).

2.1.4 Production Route Card It is a card that gives the detail of an operation to be performed in a production line it is used to instruct the production people to take up the production work. The content and formats of the PR card vary from a company to company. In general it contains: an item and quantities to be produced; production time; dimensions; any additional information that may be required by the production worker. PR card traces the route to be taken by a job during a production process (PR Card 03.2013).

2.1.5 Faults Classification Standard

Figure 1. Faults classification for a machinery enterprise Reliability engineering is dealing with an analysis of the causes of the faults in factories. In our case study it was developed a Faults Classification based on DOE-NE-STD-1004-92 standard shown in Figure 1. There are seven major cause categories that have its subcategories. The basic goal for using this standard is to define the problems or causes that occurred during production process for each operation in order to further correct them (DOE-NE-STD-1004-92 03.2013). This standard was adapted and modified for the machinery enterprises.

2.1.6 Failure Mode and Effect Analysis Methodology For the past few years, companies have been trying to enhance the reliability of their products to grasp such opportunity for business development. Therefore FMEA was born (Sharon 1998). It is a systematic method of identifying and preventing product and process problems before they occur. It is focused on preventing defects, enhancing safety and increasing a customer’s satisfaction. The risk of a failure and its effects are determined by three factors: . Severity (S) – the consequence of the failure that should occur during process. . Occurrence (O) – the probability or frequency of the failure occurring. . Detection (D) – failure being detected before the impact of the effect realized. Every potential failure mode and effect is rated in three factors on a scale ranging from 1 to 10. By multiplying the rating for the three factors (S×O×D), a Risk Priority Number (RPN) will be determined for each potential failure mode and effect. The RPN will range from 1 to 1000 for each failure mode or operation. It is used to rank the need for corrective actions to eliminate or reduce the potential failures (Robin et al. 1996). All FMEAs are team based and the purpose of an FMEA team to bring a variety of perspectives and experience to the project. The team should be made of five to nine members. All team members must have some knowledge of group behaviour, they must be cross-functional and multidiscipline, to handle the problem to be discussed (Stamatis 2003).

3. Knowledge Management Framework Description The presented KM framework shown in Figure 2 starts from a CAD system where a design engineer creates a new Item and/or a BOM structure - product. Along with the CAD models and drawings, the engineer defines an item data in the PDM system. The item data contains different attributes that are “packed” under a general Designation Code, for example - (“XYZ”). This Designation Code is an item key that is used to logically connect the data between the PDM system (Saaksvuori et al. 2008) and ERP systems (Shevtshenko et al. 2007) through PDM-ERP middleware (PDM-ERP Middleware 03.2013). Each specific item and/or a group of items have its own unique Designation Code.

Figure 2. Knowledge Management framework

The PDM-ERP Middleware transfers Designation Code and the accompanying the Item Data from the PDM (arrows labelled by 1 and 2), finds a Reference Item Code by a matching Designation Code (along with the set of Reference Item Data from ERP) and copies them into ERP Master Data (arrows 3 and 4). The final step (arrows 5 and 6) describes a similar process where the middleware finds PR in the DM (matched by a Reference Item Code) for the given item and copy it into ERP Master Data (Sahno et al. 2012). Finally, the PR is released into a production floor in the form of a PR card.

3.1. DM Structure Development for KM Framework The purpose of our DM is to store a product data that are used in manufacturing for a specified order, analyse them, eliminate problems and assess sigma performance level for product type as well as for common production system (Sahno, et al. 2013). The functionality of the DM is provided by the PR card combined with the Faults Classification standard DOE-NE-STD-1004-92 and FMEA methodology that is served by a common database. Figure 3 illustrates the fact table of the DM structure, based on the Kimball bottom-up data warehouse design methodology (Kimball et al. 2002). This dimensional model contains data about concepts that are part of the manufacturing process. The dimensional model provides an easily communicable medium between people who understand the manufacturing process and IT workers who develop the software. It also provides the actual database structure of the DM. The developed DM enables management to access the data required for business reports from single location.

Figure 3. Fact table of the Data Mart structure

4 Case Study Our case study will describe the functionality of our DM with Six Sigma DMAIC approach with the first three steps. These three steps will provide the basis for implementation of last two steps. Also we will present the benefits of new approach: the process of data collection from ERP system to DM, data processing in real time (data from the previous day) and sigma performance level assessment.

4.1. Data Mart Structure Description with the Define, Measure and Analyse Steps In our example we will define the failure for each unique operation by assessing Severity, Occurrence and Detection and its RPN value in every product type that occurs during production process and the average RPN value per operation for common production system. This calculation of RPN value will allow an engineer to define the most critical operation in product or in common production system. The same assessment will be done for each Failure Group and Failure Cause mentioned in the Faults Classification. In other words, it will be a bi-directional analysis of a unique production operation and the failures that occurred. The Figure 5 presents an example of our DM where RP card, Faults Classification and a FMEA table were combined. We will consider our DM in the logical flow of the first three steps (Define, Measure, Analyse) of Lean Six Sigma DMAIC problem solving approach. Below is discussed each step that applied in DM.

4.1.1 Define In our example, in Define step, the object or problem we consider is: Failure Group and/or Cause that belongs to an Item (Item Name and its Designation Code (described in framework)) and Production Route (Work Centre, Operation Sequence, Operation Name and Operation Time). Other words, we define the problems that we need to solve in Improve and Control step.

4.1.2 Measure To measure the problem in DM, in our example we will use FMEA approach that will allow us to calculate Severity, Occurrence, Detection and its RPN value. Further, thanks to the calculated RPN value, an engineer can define the most critical process in production. Usually the Severity, Occurrence and Detection ratings in FMEA are assessed in a team, but from the author point of view this is some kind of “tricky” process because the team members can be differently educated and they can have different experience. During the discussion there could be different opinions and argues that makes it difficult to make right decision. Therefore, in this case study, during Measure step, we will measure the changes of these ratings in online mode (based on the data from the last day). In Figure 2 (arrow 7) represented the transfer of raw data from ERP system, that was collected from production floor system, to DM where they will be processed according to the specified algorithms. To attain more precise results that correspond to the real-time production status in production floor, we propose to assess Severity, Occurrence and Detection in the following way: . Severity: This rating will be assessed in finance value. In Figure 4 below presents the example of Severity measurement. Here is presented the product – the BOM of electrical generator that consists of three main assemblies (Connected Stator, Frame and Balanced Rotor). These assemblies contain another sub-assemblies and components and so on until the lower level. From the right side of each component or sub-assembly we can overview the value-added cost (in per cent value) from the total cost of final product. From the left side we can see the total value-added cost (in per cent value) of components or sub-assemblies that contain lower level components or sub-assemblies. If a component equals to 10% or less from final product cost and it does not influence on entire product quality e.g. the scrap component could be replaced or demounted from design point of view, in this case the rating of Severity will equal to 1 point. Another example if we have a component that can influence on the entire product quality e.g. the scrap component could not be replaced or demounted from design point of view from the final product, as the result the entire product may go to scrap. Here we have “Stator Coils” that has high severity rating. We can see that it costs 5% from total product, but in case it has some undetected failures it can cause to burning of entire product, loss of 100% of product. In this case the rating of Severity will equal to 10 points.

Figure 4. Generator BOM structure and its cost assessment

. Occurrence: This rating will be assessed according to the data or statistics collected from ERP system and specified algorithm. For example, suppose that production floor produces 100 units of a component type per month. From 100 units we have 10 units (Occurrences) of scrap, that means that we have 10% of scrap and that can be equal, for example to 1 point of Occurrence. In reality 10% of scrap per month it may be too much, therefore a company can develop its own algorithm or standard of scrap value assessment. For example if it occurs 5% of scrap or even more, we can define that it will equal to 10 points of Occurrences.  Detection: As the purpose of this rating intended on assessment of failure detection before it happens, we propose to assess the rating of measurement tool, for example during production of some specified quantity of units. The weld crack detection or Non-Destructive Test (NDT) can be done by magnetic flow detection or ultrasonic test or even by radiography detection; surface measurement can be done by simple measurement tool, sliding calliper or laser tracker; voltage test can be tested by voltage tester; or inspection can be done simply visually. If we use the high level detection tool like radiography device or laser tracker it will equal to 1 point, if we use visual inspection, it can reach up to 10 points. Also, the rating of every measurement tool should be assessed according to the statistics gathered from production floor. For instance, if we use some measurement tool to measure 100 units, and we have not detected failure or scrap, the Detection should be equal to 1 point, if we have detected for example 10 failures, the Detection should equal to 10 points.

4.1.3 Analyse Thanks to the fact table of the DM in Figure 3, it can be possible to start the Analysis step of a product and common production system, according to the calculated data in Measure step, in the bi- directional way by: . Operation. We shall group/sort equal operations that happened in a specified product or in a common production system to see the variability of the RPN value. Further, we shall calculate an average RPN value for a product and/or for a common production system to see an average variability and occurrence of different failures by a specific operation. . Failure Group and/or Failure Cause. It is similar to the Operation case. We shall group similar Failure Causes (sub-groups) according to their main group category – Failure Group. From that it can be possible to see the variability of the RPN value, calculate an average RPN, see the frequency and quantity of the failure occurred. Also it can be analysed every Operation per product as well as per common production system. When such analysis results are complete this helps in identifying the area on which to focus when building a final solution during the Improve step.

The first three steps of the DMAIC process (Define, Measure, Analyse) characterize the nature of the problem to be solved. Upon completion of these steps, the problem and its root cause(s) are known and the project charter becomes a finalized document. On the basis of these steps should be created chart for implementation of Improve and Control steps of Six Sigma approach.

Calculation of Sigma Performance Level We have the production process or the PR card with 12 operations where 20 failures occurred. The maximum possible RPN value for those 12 operations equal to (20x1000) 20000. In our case we will use this maximum RPN value to define the scope of the entire production process performance level, for a product, and for a common production system. This maximum value will equal to 100%. Further we shall calculate the actual percentage of the process capability for every produced product and also the average capability percentage of the common production system. As the result of it, according to the calculated percentage, we will define the sigma performance level for each product and for the common production system. To calculate the RPN percentage for both cases, we shall use the following Equation 1:

(1) where:

PRP – per cent of a failure by operation or a failure group/cause, RPNPC – RPN value for a particular failure group/cause or operation, RPNTotal – total RPN value by product or by a common production system.

As the result, according to the calculations for the given case (product) we have got the total RPN value of 2000 that makes 10% of the total theoretical RPN value of 20000. In order to calculate a sigma performance process yield we use the following calculation: 100% - 10% = 90%. Hence, we have got 90% of the process yield or positive performance level from the current product. On the basis of received results (10%) and according to the Table 1 that represents the sigma performance scale we can define the sigma performance level for the current example. Here we have got 2.78 δ or around 3 δ. Based on developed calculation it is possible to monitor on which sigma level a company operates at the current moment.

The result of this example represented the input data for possible “dashboard” that monitors the company sigma performance level. Thanks to this result, the company management (Black Belt or Master Black Belt) should develop Six Sigma Improve and Control steps to improve the way of costs decreasing, product quality, product On-Time Delivery (OTD) and a customer’s satisfaction. Figure 5. Combination of PR card, Fault Classification standard and FMEA table in DM Table 1. Sigma performance scale (Gregory 2004) Sigma Perfor- Defects per Process Process Process Estimated Cost mance Level Million Yield Capability Capability of Poor Quality Opportunities (Cp) (Cpk) (% Revenue) 1.0 δ 670000 33% Not capable Not capable >40% 2.0 δ 308537 69.2% Not capable Not capable 30-40% 3.0 δ 66807 93.32% 1.0 0.5 20-30% 4.0 δ 6210 99.38% 1.33 0.83 15-20% 5.0 δ 233 99.9767% 1.67 1.17 10-15% 6.0 δ 3.4 99.99966% 2.0 1.5 <10%

5. Conclusion In this paper we have presented a new Knowledge Management (KM) framework where were considered the item/product data flow between Product Data Management (PDM) and Enterprise Resource Planning (ERP) systems using a special middleware that integrated and synchronized the data between those systems and our novel Data Mart (DM). The integration provided to unite different data that generated the needed information for a product manufacturing. The framework allowed engineers to refuse from every day routine work and pay more attention to specific production issues. We have also presented the integration of the first three steps of Six Sigma DMAIC problem solving approach (Define, Measure, Analyse) into the DM. In the Define step we have defined the object or problem to be solved combining the Production Route (PR) card, Faults Classification standard DOE- NE-STD-1004-92 and Failure Mode and Effect Analysis (FMEA) methodology into a common database. For the Measure step, the data from production floor was collected into the ERP system and then transferred into the DM where each rating - Severity, Occurrence and Detection was processed according to the specified algorithm that allowed calculating RPN value for every process in online mode. Further, the RPN value was converted into the percentage value that finally showed us production process yield which enabled to estimate sigma performance level for the product and common production system. The combination of different concepts into the common database ensured us to perform Analysis step for production operation and the failures that occur during a production process on a product, and on a common production system level in bi-directional way. On the basis of this three steps in DM, it can be implemented the last two steps (Improve and Control) of Six Sigma approach.

The presented DM provides the report, on the basis of data from production floor that shows on which sigma performance level a company operates. The higher sigma level the higher a customer’s trust. The application of such approach in DM can be very useful for elimination of production failures, for a company when it is to be audited by a customer and for managers to make the right decision about further strategic development.

6. Acknowledgements This research was supported by Estonian Ministry of Education and Research for targeted financing scheme SF0140113Bs08 and Grant ETF9460 and European Social Fund’s Doctoral Studies and Internationalisation Program DoRa, which is carried out by Foundation Archimedes.

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