Sales Forecasting Deloitte Analytics Approach

Total Page:16

File Type:pdf, Size:1020Kb

Sales Forecasting Deloitte Analytics Approach Deloitte Analytics possible enhancements Contacts Having a sales predictive model is the first step towards creating a data-driven company. Alongside the predictive model, it is advisable to adopt a series of tools to support decisional business processes. Alfredo Maria Garibaldi Partner | Analytics Country Leader MONITORING TOOL [email protected] Building dashboards that visualize predicted Daniele Pier Giorgio Bobba results and comparisons with previous years Partner is a key tool for business users interested in [email protected] monitoring the actual performance of the company. Marco Leani Partner [email protected] Alberto Ferrario Director KPIs REPORTING [email protected] It is important to create a tool that gives users the possibility to analyze KPIs and detect misalignments or deviation from expected values or targets (e.g.: early-warning, alerts, traffic light charts…). PRESCRIPTIVE ANALYSIS Prescriptive Analytics extends beyond predictive analytics by specifying both the actions necessary to achieve predicted outcomes and the interrelated effects of each decision. This kind of analysis is able to answer questions such as “what do we need to do to achieve a specific forecast?” Our national team of over 200 professionals has proven experience in structuring, managing, and delivering Enterprise Information Management strategies and implementation services. Through the collective experience of local practice and leveraging TOOLS FOR DEVELOPING AND assets and best practices of our global WW Deloitte Analytics team, we have serve our REPORTING SALES FORECASTING customers with a broad array of toolkits, accelerators, models, leading-edge practices, diagnostics, and governance approaches to accelerate and improve the quality of EIM Deloitte Analytics has a vast knowledge of projects and ensure a focus on value creation. technical tools for data management, data Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company modelling and reporting in Sales Forecasting. limited by guarantee (“DTTL”), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also Access to relevant data-driven insights is a referred to as “Deloitte Global”) does not Sales forecasting necessity not only to formulate an effective provide services to clients. Please see www.deloitte.com/about for a more detailed business strategy, but also to monitor its description of DTTL and its member firms. Deloitte Analytics Approach execution. © 2018 Deloitte Consulting Srl The growing world Principles for a great Sales Forecast of data PROBLEM DEFINITION USE EXTERNAL DATA INVOLVE BUSINESS EXPERTS 1 Identify the main business goals and set expectations before any development phase. Robust predictions benefit from having Sales forecasting is not a one-time activity, high quality and easily accessible data. but an ongoing process that affects every Data has undoubtedly become the fuel for competitive These data can be enriched with external aspect of the sales pipeline. Therefore, it is DATA GATHERING & PREPARATION sources that can contribute improving the important not only to make predictions SETTING EXPECTATION Search and preprocess the data to define and advantage in the 21st century. quality of the predictions. Depending on based on the numbers on hand but also integrate the different data sources that will be Nowadays we generate and collect enormous volumes of data the product of the company, different to pair these numbers with qualitative 2 used as foundation for the models. and we are able to give machines the appropriate input for kinds of external data could be used. information in order to get a more realistic Data preparation is one of the most important and critical phases in a data mining project: data needs them to learn and predict outcomes by using algorithms to Below are some examples of open view of the business. This can be achieved external data: with appropriate communication and to be effectively interpreted and analysed. interpret raw data. • income-age geographical distribution collaboration between the business and • blogs or social networks the team involved in the construction of EXPLORATORY DATA ANALYSIS Why sales forecasting • articles the forecasting model. Primary analyses are carried out on data in order • macro-economic factors to use insights from results to define further steps. Sales forecasting allows companies to spot potential issues or risks and design appropriate • sector indexes COLLECTING INFORMATION 3 The KPIs that should be used in the machine corrective actions to mitigate them. learning models would be individuated and it 180 15% would be assessed how they are related to each other. 160 10% SALES DEMAND INVENTORY FINANCIAL 140 5% PLANNING PLANNING CONTROLS PLANNING MACHINE LEARNING 120 Sales forecasting helps The sales forecast is the The more accurate the Anticipating sales gives 0% The process of applying statistical algorithms on sales managers planning best way to get a good sales forecast, the better managers the 100 prepared dataset, providing a rigorous framework their future activities, estimate of the product prepared your company information they need to -5% 80 providing each of them demand. Sales teams are will be to manage its predict revenue and to test those models. -10% with a business plan for in the front line of inventory, avoiding both profit. Having good 60 4 Insights drawn from previous phases are used to managing their territory. business forecasting and overstock and stock-out forecasting information -15% Forecasting is the tool best positioned to gather situations. Stable gives a company the 40 choose the most appropriate models that could be that helps them identifing information about inventory also means ability to explore 20 -20% applied, evaluate pros and cons and implement the the necessary customers anticipated demand better management of possibilities to rise both MODELING INFORMATION solution. to meet their targets. your production revenue and net income 0 -25% M +1 M +2 M +3 M +4 M +5 M +6 M +7 M +8 M +9 M +10 DEVIATION REAL FORECAST VALIDATION & TESTING Assess models’ accuracy and robustness. As INTERNAL CONTINUOS GAIN MARKETING models are used to forecast future sales, they CONTROLS IMPROVEMENT INSIGHTS BENEFITS DEFINE CLEAR NEEDS BE FLEXIBLE TO CHANGE 5 should be generalized and be able to give reliable results outside of the dataset they have been Having an insight on the Continuous improvement Accurate sales Sales forecasting gives The key phase in creating a sales It is impossible to use a single model that projected production is a goal of many if not all forecasting can help you marketing an important forecasting solution is the understanding will ensure the track of the exact terms, developed on. rates gives the possibility businesses. By tracking data and gaining look at future sales. to have a better control forecasting sales and insights into areas where This offers the and the definition of the business needs: time, and context of every sale. Instead, of the internal continually revising improvements can be opportunity to schedule this allows to delimit the perimeter of what companies should focus on developing a RESULTS COMMUNICATION operations. By processes to increase made. Furthermore, it promotions if sales are is requested, what can be achieved and process that can be managed, re- Communicate effectively the advanced analytics anticipating future sales, accuracy, companies can can help understanding expected to be too weak managers can make improve all aspects of the customers’ behaviour how it can be achieved. Business evaluated, and modified as conditions 6 models results and translate them into actionable decisions about hiring, their business in order to increase knowledge is essential to define the most change. business insights. Models results should assist marketing and expansion performance conversion rates appropriate analytics tool. COMPARING EXPECTATIONS business in decision-making. The growing world Principles for a great Sales Forecast of data PROBLEM DEFINITION USE EXTERNAL DATA INVOLVE BUSINESS EXPERTS 1 Identify the main business goals and set expectations before any development phase. Robust predictions benefit from having Sales forecasting is not a one-time activity, high quality and easily accessible data. but an ongoing process that affects every Data has undoubtedly become the fuel for competitive These data can be enriched with external aspect of the sales pipeline. Therefore, it is DATA GATHERING & PREPARATION sources that can contribute improving the important not only to make predictions SETTING EXPECTATION Search and preprocess the data to define and advantage in the 21st century. quality of the predictions. Depending on based on the numbers on hand but also integrate the different data sources that will be Nowadays we generate and collect enormous volumes of data the product of the company, different to pair these numbers with qualitative 2 used as foundation for the models. and we are able to give machines the appropriate input for kinds of external data could be used. information in order to get a more realistic Data preparation is one of the most important and critical phases in a data mining project: data needs them to learn and predict outcomes by using algorithms to Below are some examples of open view of the business. This can be achieved external data: with appropriate communication and to be
Recommended publications
  • Enterprise Information Management (EIM): the Hidden Secret to Peak Business Performance
    Enterprise Information Management (EIM): The Hidden Secret to Peak Business Performance A White Paper by Vincent Lam and JT Taylor Table of Contents 1 EIM: What It Is and Why It Matters 1 Customer Service 1 Marketing Opportunities 2 Process Improvement 2 Regulatory Compliance 2 Fraud Detection 3 EIM: The Challenges 3 The Enterprise Information Lifecycle 4 Upstream 4 Instream 5 Downstream 6 Formulating Your EIM Strategy: Key Points to Consider 6 Unlimited Data Access 6 End-to-End Data Management and Quality Control 6 Maximum Flexibility 7 The iWay EIM Suite: True Enterprise-Wide Information Management 7 Unparalleled Data Quality Management 10 Comprehensive Master Data Management 13 The Broadest Information Reach 13 Multiple Levels of Information Latency 13 Laying the Foundation for Critical Information-Integration Initiatives 16 Conclusion EIM: What It Is and Why It Matters Over the past decade, organizations of all types and sizes have experienced significant growth in the volume of business information they generate and maintain. That information and the technology architectures that house it have also become increasingly complex. The Butler Group, a division of Datamonitor, estimates that approximately 80 percent of vital business information is currently stored in unmanaged repositories, making its efficient and effective use a nearly impossible feat. Enterprise information management (EIM) is a strategic business discipline that combines many of the key principles of enterprise integration, business intelligence (BI), and content management to streamline and formalize the activities associated with data storage, access, and handling. Comprehensive EIM initiatives blend processes and technologies to significantly improve the way information is managed and leveraged across a company.
    [Show full text]
  • Project Information Management PROJECT MANAGEMENT for DEVELOPMENT ORGANIZATIONS Project Information Management
    pm4dev, 2016 –management for development series © Project Information Management PROJECT MANAGEMENT FOR DEVELOPMENT ORGANIZATIONS Project Information Management PROJECT MANAGEMENT FOR DEVELOPMENT ORGANIZATIONS A methodology to manage development projects for international humanitarian assistance and relief organizations © PM4DEV 2016 Our eBook is provided free of charge on the condition that it is not copied, modified, published, sold, re-branded, hired out or otherwise distributed for commercial purposes. Please give appropriate citation credit to the authors and to PM4DEV. Feel free to distribute this eBook to any one you like, including peers, managers, and organizations to assist in their project management activities. www.pm4dev.com Project Information Management INTRODUCTION “If you fail to plan, you plan to fail.” “.. A major weakness is the ability of project staff to utilize their logframe for designing a coherent and integrated, overall information system, where a manageable and limited number of feasible information activities are planned, which together will ensure that effective effect and impact level monitoring will occur. It is typical for projects to end up collecting too much rather than too little information. Frequently though, much of this information is not relevant to monitoring the results and impacts for which the project is accountable, and that which is, is not collected sufficiently reliably or regularly. By restricting the number, but improving the quality and reliability of their major information gathering activities, projects will much improve their information systems.” CARE International EDIAIS Case Study Project Information Management Plan Detailed planning is critical to the development of usable, high quality information deliverables that meet the needs of internal and external information users.
    [Show full text]
  • Information Management Strategy I Table of Contents
    information management strategy i Table of Contents 02 Message from the DM 03 Strategic Alignment 04 The Vision 05 Executive Summary 06 Introduction WHAT IS INFORMATION? WHAT IS INFORMATION MANAGEMENT? WHY IS INFORMATION MANAGEMENT IMPORTANT FOR THE GOA? 08 Business Drivers 11 Strategic Goals GOAL ONE. GOAL TWO. GOAL THREE. 14 Moving Forward 15 Acknowledgements 16 Glossary Message from the DM Service Alberta is focused on working collaboratively to foster an open, transparent, accountable and participatory government. We recognize that information, along with people, finances and infrastructure, are key strategic resources in the Government of Alberta, and are at the very core of our operations. Service delivery activities, decision-making, policy development and planning activities are all founded on information. The quality, reliability and integrity of information are critical to good-decision making in the government. Proper management of information will transform the delivery of program and service outcomes, protecting Albertans from information security and privacy risks. This will help ensure that the needs of Albertans are met through efficient and effective operations. The Information Management Strategy creates the foundation we need to continually improve, transform and increase information sharing, accountability and transparency in the government. It articulates a clear vision with three goals that address key business drivers, and works toward promoting effective and innovative information management practices within the
    [Show full text]
  • The Impact of Information Technology on Supply Chain Performance: a Knowledge Management Perspective
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by The University of North Carolina at Greensboro THE IMPACT OF INFORMATION TECHNOLOGY ON SUPPLY CHAIN PERFORMANCE: A KNOWLEDGE MANAGEMENT PERSPECTIVE by Yuan Niu A dissertation submitted to the faculty of The University of North Carolina at Charlotte in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology Charlotte 2010 Approved by: _______________________________ Dr. Chandrasekar Subramaniam _______________________________ Dr. Antonis Stylianou _______________________________ Dr. Sungjune Park _______________________________ Dr. Arun Rai _______________________________ Dr. Thomas Stevenson ii © 2010 Yuan Niu ALL RIGHTS RESERVED iii ABSTRACT YUAN NIU. The impact of information technology on supply chain performance: a knowledge management perspective (Under direction of DR. CHANDRASEKAR SUBRAMANIAM AND DR. ANTONIS STYLIANOU) Supply chain management has become an increasingly important management tool to help organizations improve their business operations. Although information and communication technologies have been used extensively in supply chains, there is a lack of systematic evidence regarding the mechanisms through which IT creates value. Furthermore, as supply chain objectives are going beyond operational efficiency towards pursuing higher-order goals, such as understanding the market dynamics and discovering new partnering arrangements to provide greater customer value, the capabilities
    [Show full text]
  • Information Management Strategy
    Information Management Strategy July 2012 Contents Executive summary 6 Introduction 9 Corporate context 10 Objective one: An appropriate IM structure 11 Objective two: An effective policy framework 13 Objective three: Excellence in records and document management 15 Objective four: Changing the culture through communication and training with 18 effective monitoring and performance management June 2012 Action Plan to Best in Class Information Management 20 Croydon’s information management strategy 3 4 Croydon’s information management strategy Foreword by the chief executive There are practical business There are clear links between information reasons for having an management and performance management. information management This ranges from how we measure our strategy. Better information organisational performance right the way through management will create to our individual performance, through PDCS. efficiencies in accommodation, IT and This means that it is everyone’s responsibility in better use of its staff by the organisation to ensure that information is providing ready access to managed effectively. We have a duty of care to relevant information and the storage or our customers in ensuring that information is destruction of irrelevant information. handled in the best way possible and this strategy sets out how we intend to further improve This will, in turn, lead to a better service provided information management. to residents and other service users. It is up to every single one of us to help ensure the Increasingly our services are provided in success of this strategy. collaboration with a range of partners from all sectors. The effective and efficient provision of these services requires that information passes between organisations in a timely and appropriate manner.
    [Show full text]
  • Making Technology Fit: Designing an Information Management System for Monitoring Social Protection Programmes in St
    Making Technology Fit: Designing an Information Management System for Monitoring Social Protection Programmes in St. Kitts Kristina Pitula, Daniel Sinnig and T. Radhakrishnan Faculty of Engineering and Computer Science, Concordia University, Montreal Abstract This paper reports on the development of an Information Management System to monitor and plan social protection programmes for the St. Kitts’ Department of Social Work. Best practices for the design of such technologies require special consideration of users’ needs; in the case of St. Kitts, this included meeting the constraints of the small island environment. The collaborative process between the software developers and users, wherein users explain what they need and the software is designed accordingly, was complicated by the existence of a disconnect between users’ work practices and those supported by the proposed system. The challenge was thus to design a viable and sustainable information management system (IMS) relevant to the users’ work, and responsive to local constraints. The project revealed cultural differences between department workers and software developers. It suggested new methodologies for assessing user needs, eliciting their preferences, and building their capacity in electronic recordkeeping. It also highlighted the need to develop communication strategies for both clients and developers of technology. The lessons learnt have implications for improved practice when introducing IMS technologies into work contexts in microstates where they are currently not used. 1. Introduction In their continuing effort to find innovative strategies to maximize social and human capital, small island developing states (SIDS) have turned to the use of information and communication technology (ICT) to overcome the restraints posed by isolation, diseconomies of scale, and other limited resources (Atchoarena et al., 2008; Hage & Finsterbusch, 1987).
    [Show full text]
  • Information Management Planning (Pdf)
    G Information ii Management Information Management Planning March 2005 Discussion Draft Produced by Information Management Branch Open Government Service Alberta 3rd Floor, Commerce Place 10155 – 102 Street Edmonton, Alberta, Canada T5J 4L4 Office Phone: (780) 427-3884 Fax: (780) 422-0818 Website: www.alberta.ca/information-management-branch.aspx Email: [email protected] ©Government of Alberta ISBN 0-7785-3694-7 Information Management Planning Contents Contents .................................................................................... i 1. Introduction ......................................................................... 1 The need for information management planning ............................................. 1 Context and scope of the plan ...................................................................... 3 Developing the plan .................................................................................... 5 2. Establish Planning Team....................................................... 8 Objective ................................................................................................... 8 Who to involve ........................................................................................... 8 Activities ................................................................................................... 9 Checklist ................................................................................................. 10 3. Define Vision and Future State ..........................................
    [Show full text]
  • Improved Decision Making and Enhanced Recommendation Systems in Applications Made Possible Through Prescriptive Analytics
    INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 10, OCTOBER 2019 ISSN 2277-8616 Improved Decision Making And Enhanced Recommendation Systems In Applications Made Possible Through Prescriptive Analytics S.Viswanandhne, A.Saran Kumar, Granty Regina Elwin, Ranjeetha Priya ,V.Praveen, S.Priyanka Abstract: Prescriptive analytics is an advanced version of analytics that employs optimization tools in order to transform the outcomes of the analysis process into actions that would bring about improvement. Prescriptive analytics studies and processes the relationship between the various components of the data and gives a prediction about what could happen. Prescriptive analysis takes it one step ahead and in addition to forecasting the outcomes also provide recommendations on what should be done. Prescriptive analytics which in comparison with other forms of analytics is actionable would play a vital role in many fields including healthcare, self-driven vehicles, asset performance management, education, particularly the vast amount of e- learning content, business process optimization and many more. This paper discusses the role of prescriptive analytics in some of the prominent industries and how prescriptive analytics in hand with AI and Machine learning brings about improved systems that handle predicted outcomes in an improved manner. Keywords: Prescriptive Analytics, Descriptive Analytics, Predictive Analysis. ———————————————————— 1. INTRODUCTION 1.3 Prescriptive Analytics In today’s digital world, Data is growing everywhere. In fact Prescriptive analytics specifies what action to be taken in the data grows at rapid rate, doubling every two years. order to eliminate the future problem. Progressively, Data Analytics is examining the raw data, for the conclusion individuals in intelligence and analytics circles are about the information.
    [Show full text]
  • From Predictive to Prescriptive Analytics
    From Predictive to Prescriptive Analytics Dimitris Bertsimas Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02139, [email protected] Nathan Kallus Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, [email protected] In this paper, we combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisions in OR/MS problems. In a departure from other work on data-driven optimization and reflecting our practical experience with the data available in applications of OR/MS, we consider data consisting, not only of observations of quantities with direct e↵ect on costs/revenues, such as demand or returns, but predominantly of observations of associated auxiliary quantities. The main problem of interest is a conditional stochastic optimization problem, given imperfect observations, where the joint probability distributions that specify the problem are unknown. We demonstrate that our proposed solution methods, which are inspired by ML methods such as local regression (LOESS), classification and regression trees (CART), and random forests (RF), are generally applicable to a wide range of decision problems. We prove that they are computationally tractable and asymptotically optimal under mild conditions even when data is not independent and identically distributed (iid) and even for censored observations. As an analogue to the coefficient of determination R2, we develop a metric P termed the coefficient of prescriptiveness to measure the prescriptive content of data and the efficacy of a policy from an operations perspective. To demonstrate the power of our approach in a real-world setting we study an inventory management problem faced by the distribution arm of an international media conglomerate, which ships an average of 1 billion units per year.
    [Show full text]
  • Prescriptive Analytics for Staff Scheduling Optimization in Retail
    FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO Prescriptive Analytics for Staff Scheduling Optimization in Retail Catarina Alexandra Teixeira Ramos Mestrado Integrado em Engenharia Informática e Computação Supervisor: Carlos Manuel Milheiro de Oliveira Pinto Soares Second Supervisor: Yassine Baghoussi July 3, 2019 Prescriptive Analytics for Staff Scheduling Optimization in Retail Catarina Alexandra Teixeira Ramos Mestrado Integrado em Engenharia Informática e Computação July 3, 2019 Abstract Human Resource Management (HRM) is an area where improving processes is important to achieve higher performance and profit in an organization. In recent years, this area has gain focus in research of data mining techniques, however, a particular sub-domain stands out: staffing, that deals with gathering, training, placing and retaining the best people for particular jobs or tasks in the organization. In retail, shop floor employees have a great impact on sales since they interact directly with customers. Guaranteeing client coverage increases the possibility of clients being converted to sales and therefore, increase profit for the store. Ensuring client coverage could easily be done by allocating all employees to the store. However, allocation has costs and using all resources may lead to overstaffing. For this reason, optimization is needed to achieve the optimal number of shop floor employees in order to face staff demand. Prescriptive analytics is a type of data mining that aims at prescribing the best decisions. For this, it combines data mining models with decision support techniques. This allows to prescribe solutions and provide support through a more complex process. In this dissertation, the main goal is to build a prescriptive model that prescribes the best shifts and task allocation in order to maximize a store’s profit.
    [Show full text]
  • A Vision on Prescriptive Analytics
    ALLDATA 2017 : The Third International Conference on Big Data, Small Data, Linked Data and Open Data (includes KESA 2017) A Vision on Prescriptive Analytics Maya Sappelli Maaike H.T. de Boer TNO TNO and Radboud University Data Science, The Hague, The Netherlands Data Science, The Hague and Nijmegen, The Netherlands Email: [email protected] Email: [email protected] Selmar K. Smit Freek Bomhof TNO TNO Modelling, Simulation& Gaming, The Hague, The Netherlands Data Science, The Hague, The Netherlands Email: [email protected] Email: [email protected] Abstract—In this paper, we show our vision on prescriptive in Section III. In Section IV we use several application analytics. Prescriptive analytics is a field of study in which the domains, such as oil and gas, law enforcement, healthcare and actions are determined that are required in order to achieve logistics, to explain in which situations prescriptive analytics a particular goal. This is different from predictive analytics, might be fruitful and in which it will not. This paper ends with where we only determine what will happen if we continue a direction of future prescriptive analytics research. current trend. Consequently, the amount of data that needs to be taken into account is much larger, making it a relevant big II. DESCRIPTIVE,PREDICTIVE AND PRESCRIPTIVE data problem. We zoom in on the requirements of prescriptive analytics problems: impact, complexity, objective, constraints and ANALYTICS data. We explain some of the challenges, such as the availability The number of organizations that base their results on data of the data, the downside of simulations, the creation of bias analysis is growing.
    [Show full text]
  • From Information Management to Information Governance: the New Paradigm
    From Information Management to Information Governance: The New Paradigm hbrconsulting.com | [email protected] | 312.201.8400 © 2017 HBR Consulting LLC. All rights reserved. OVERVIEW The explosive growth of information presents management challenges to every organization today. Retaining volumes of information beyond its legal, regulatory, or operational value increases data storage costs and strains information technology resources. Further, there is an inherent risk in retaining information beyond its legal or business retention needs when it may be subject to discovery related to litigation, audits, or governmental investigations. Added to these challenges are increasing privacy and security issues. Information containing personally identifiable information, intellectual property, or other sensitive data, if inadequately protected, is subject to cyber attack and security breach. The potential results of such a breach can be devastating — millions of revenue dollars lost, sanctions and fines levied, executives fired, and reputation and customer trust irreparably damaged. Today’s siloed approach to information management Traditionally, distinct business functions within an organization have been designated as responsible for specific pieces of the information pie. Some functions with defined focus include the following: • Records Management, responsible for establishing the retention rules for records, has historically emphasized the management of hardcopy records and now struggles to manage digital information, often requiring an approach that falls outside of the traditional methodologies. • The Legal department, with a reactive focus on litigation and e-discovery needs, has often discounted “non-responsive” information from its purview. • Information Security has focused its attention on implementing key controls for the security and protection of data, everything from access control permissions to data loss prevention tactics to breach response plans, without necessarily taking into consideration the practical business needs for that data.
    [Show full text]