ADVISOR: (Committee Chair if MS Plan A or EdS Thesis or Field Project/Problem):

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1 1 Author: Foster, Robert, J. Title: Demand Planning: The Art and Science of Forecasting in a Consumer Technology Market The accompanying research report is submitted to the University of Wisconsin-Stout, Graduate School in partial completion of the requirements for the Graduate Degree/ Major: MS Operations and Supply Management Research Advisor: James Keyes, Ph.D. Submission Term/Year: Fall 2013 Number of Pages: 69 Style Manual Used: American Psychological Association, 6 th edition STUDENT: I understand that this research report must be officially approved by the Graduate School and that an electronic copy of the approved version will be made available through the University Library website I attest that the research report is my original work (that any copyrightable materials have been used with the permission of the original authors), and as such, it is automatically protected by the laws, rules, and regulations of the U.S. Copyright Office. My research advisor has approved the content and quality of this paper. NAME: DATE: ADVISOR: (Committee Chair if MS Plan A or EdS Thesis or Field Project/Problem): NAME: DATE: This section for MS Plan A Thesis or EdS Thesis/Field Project papers only Committee members (other than your advisor who is listed in the section above) 1. CMTE MEMBER S NAME: DATE: 2. CMTE MEMBER S NAME: DATE: 3. CMTE MEMBER S NAME: DATE: This section to be completed by the Graduate School This final research report has been approved by the Graduate School. Director, Office of Graduate Studies: DATE:

2 2 Foster, Robert, J. Demand Planning: The Art and Science of Forecasting in a Consumer Technology Market Abstract The objective of this study was to reduce forecast error and forecast bias metrics for Imation Corporation s consumer optical media category in their Canada sub-region. Achieving this objective required an assessment of current state forecasting practices and an assessment of business functions roles and responsibilities for inputs to the optical media forecasts. This study used Six Sigma methodology of process improvement and define-measure-analyze-improvecontrol (DMAIC) as the chosen method of evaluation. The use of a process observation chart and statistical regression testing resulted in redefining workflow, updating forecasting roles and responsibilities, and implementing standard work instructions and business processes. The conclusions of this study demonstrated the benefits improving forecast error and forecast bias can have on organizational goals and objectives.

3 3 Table of Contents Abstract... 2 List of Figures... 6 Chapter I: Introduction... 7 Statement of the Problem... 9 Purpose of the Study... 9 Assumptions of the Study Definition of Terms Limitations of the Study Methodology Summary Chapter II: Literature Review Sales and Operations Planning Demand review Supply review Finance review Demand Review Demand plan Business plans Marketing plans Sales plans History Forecasting Impacts to an Organization... 22

4 4 Top down forecasting Bottom up forecasting Hybrid forecasting Forecast metrics Forecast accuracy Forecast bias Summary Chapter III: Methodology Define Phase Measure Phase Analyze Phase Statistical analysis testing Summary Chapter IV: Results Define Phase Results Measure Phase Results Analyze Phase Results Statistical analysis testing Improve Phase Results Control Phase Results Goal Attainment Results Forecast error results Forecast bias results... 46

5 5 Inventory dollars Summary Chapter V: Discussion Limitations Conclusions Recommendations References Appendix A: Imation s Canada Sub-region Forecasting Project Charter Appendix B: Forecast Changes within Supply Lead Time Appendix C: Forecasting During Inventory Constraints Appendix D: Forecasting New Items Appendix E: Demand Classification Appendix F: Correlation - Forecast Error and On Hand Inventory Dollars... 69

6 6 List of Figures Figure 1: Inputs to sales and operations planning Figure 2: Inputs to the demand plan Figure 3: Operational benefits to collaborative forecasting Figure 4: Top down and bottom up forecasting Figure 5: Process observation chart Figure 6: Example of JDA statistical forecast Figure 7: Examples of business intelligence reports Figure 8: JDA statistical forecast with model exception Figure 9: JDA statistical forecast with algorithm parameters Figure 10: JDA statistical forecast with sales team overrides Figure 11: Regression testing output Figure 12: Imation's Canada sub-region historical forecast metrics Figure 13: Optical media current state forecast process Figure 14: Correlation between sales teams overrides and forecast error Figure 15: Correlation between sales teams overrides and forecast bias Figure 16: Optical media future state forecast process Figure 17: Forecast error metrics Figure 18: Forecast bias metrics Figure 19: Inventory dollars in relation to forecast error... 47

7 7 Chapter I: Introduction Imation Corporation (Imation), headquartered out of Oakdale, MN, is a global storage and data security company. Brands in their portfolio include Memorex, XtremeMac, and TDK Life on Records and their core products include magnetic tape media, compact discs (CD), digital video discs (DVD), Blu-ray discs (BD), flash drives, hard disk drives, and consumer electronics (Imation, 2012). Imation sells their products in the consumer and commercial markets reaching customers in over 100 countries. Imation s approach to balancing the demand and supply of their product portfolio is a process called sales and operations planning. Sales and operations planning is a cross functional business process that concludes with a periodic meeting to develop demand and supply plans (Lapide, 2002). The meeting is weekly or monthly and includes demand and supply managers, sales, marketing, finance and executive management. The purpose of the meeting is to agree on a single operational plan centered on achieving organizational goals and objectives. Imation utilized the sales and operations planning process for their Americas region which consists of sub-regions Canada, USA and Latin America. The components to their sales and operations planning process were three monthly meetings: demand review, supply review and finance review. The first step in Imation s sales and operations planning process was the demand review and the purpose was to agree on a demand plan, or forecast, for what could realistically be expected to sell in the next 12 to 18 months. The key functional units that provided input to the demand review are demand planners, sales, marketing, and product management. The demand review was critical in that it allowed business leaders to understand the condition of each business. If demand was greater than supply the impact to the organization were increased costs due to expedited freight, late shipments, customer fines and potential lost business. Conversely,

8 8 when supply was greater than demand costs also increased due to increased inventory and warehousing costs. The demand review facilitated balancing demand and supply to optimize inventory levels and mitigated costs. Demand planners at Imation were responsible for creating and maintaining statistical forecasts that were used as inputs to the demand review process. This was done through demand and supply software tool called JDA. The JDA software tool was developed by a company called JDA Software Group, they provide supply chain software solutions for companies in manufacturing, transportation and logistics, retail, and service industries. JDA s demand software tool uses historical sales patterns and applies a mathematical algorithm to create the statistical forecast. The software has numerous algorithms to choose from and will apply the best fit based on the type of historical sales patterns. JDA will categorize historical sales as seasonal, lumpy, erratic, or slow moving. If no historical sales are available, sales teams are required to generate the forecast. On a monthly basis, demand planners review each statistical forecast and adjust it to better align with recent demand patterns, seasonal trends or any change in demand consumption. The demand planners adjustments were subjective and they rely on forecasting experience and their knowledge of the category. When all statistical forecasts are fine-tuned they are reviewed by sales. If sales did not agree with the statistical forecast generated by the demand planner they created their own forecast through what was called an override. In JDA, sales teams have a data stream that mirrors the demand planners statistical forecast. When sales teams chose to create their own forecast they overrode the demand planners forecast by hard keying a new number in the sales data stream. When a demand planner disagreed with the changes sales were making to the forecast, the demand planner had to

9 9 convince sales to change the forecast. Responsibilities were set this way because sales were accountable for the forecast in the demand review. Imation s sales teams in the Canada sub-region were overriding the demand planners forecast on over 70% of their consumer optical media items. Canada s forecast error was greater than 60% compared to best in class of 15% to 20%. Additionally, their forecast bias, which measures over and under forecasting, was mostly positive which suggested they were over forecasting. Compared to a similar market, the USA sub-region forecast error was 40% with a forecast bias of plus or minus 15%. Statement of the Problem Imation s Canada sub-region consumer accounts were consistently missing forecast error and forecast bias metrics in their optical media category. This directly impacted the company s goals and objectives, specifically on hand inventory dollars. Purpose of the Study The purpose of this study was to evaluate Imation s Canada sub-region consumer forecasting practices for their optical media category. Additionally, roles and responsibilities for demand planners and sales teams were assessed. The goals of this study were to reduce forecast error by 5% and reduce forecast bias by 5% which, in addition, would support organizational goals of reducing on hand inventory dollars. Demand planners and sales teams roles and responsibilities were assessed with the goal of understanding the impact of human intervention on a forecast versus the use of a computer software generated forecast. The study used a 12 month time period, demand planners and sales teams in Imation s Canada sub-region used the Six Sigma define-measure-analyze-improve-control (DMAIC) approach to evaluate how optical

10 10 media forecasts were generated and assessed roles and responsibilities for the inputs to those forecasts. Assumptions of the Study This study assumed that improving Imation s Canada sub-region forecast error and forecast bias metrics for optical media was achievable by making adjustments to current state forecasting practices. In addition, it was assumed that the demand planner responsible for forecasting optical media would maintain and manage the inputs to the sales and operations planning process in order to limit the amount of human intervention in the forecast. Human intervention in the forecast was permissible when not taking action would result in negative impacts to the customer (e.g., promotional activity and new item sets). Definition of Terms DMAIC. A six sigma process that outlines the steps needed to create a completely new business process or product at six sigma quality levels ( APICS, n.d., para. 2). Forecast bias. Tendency of a forecast to systematically miss the actual demand (consistently either high or low) ( APICS, n.d., para. 2). Forecast error. The difference between actual demand and forecast demand, stated as an absolute value or as a percentage ( APICS, n.d., para. 2). JDA. Supply chain software vendor. Oliver Wight. Consulting agency consisting of business improvement specialists who educate, coach and mentor people to lead and sustain change ( Oliver wight international, 2011). Optical media. Compact disc (CD), digital video disc (DVD), and Blu-ray disc (BD).

11 11 Sales and operations planning. A process to develop tactical plans that provide management the ability to strategically direct its businesses. The process brings together all the plans for the business (sales, marketing, development, manufacturing, sourcing, and financial) into one integrated set of plans ( APICS, n.d., para. 2). Six Sigma. A methodology that furnishes tools for the improvement of business processes. The intent is to decrease process variation and improve product quality ( APICS, n.d., para. 2). Limitations of the Study The scope of this study was limited to Imation s Canada sub-region consumer accounts and the optical media category, specifically CD s, DVD s, and Blu-ray discs. It did not include America or Latin America sub-regions and it did not include commercial accounts. This study did not include product categories magnetic tape, flash drives, hard disc drives, and consumer electronics. Imation used three warehouses to supply optical media discs and this study was limited to a single warehouse, SouthHaven. The other two warehouses were excluded from the study at Imation s request. In addition, source of supply, lead times, minimum order quantities, and item rationalization were out of scope. Lastly, data collected was limited to the dates between December 2011 and August Methodology The methodology chosen to evaluate Imation s Canada sub-region consumer forecasting practices for their optical media category was Six Sigma. Six Sigma is a methodology that focuses on process improvement using a systematic approach. The study period was a 12 month time period and demand planners and sales teams used the Six Sigma define-measure-analyzeimprove-control (DMAIC) process to evaluate how optical media forecasts were generated and

12 12 assessed roles and responsibilities for the inputs to those forecasts. The DMAIC approach was chosen for this study because it allowed for a systematic evaluation of the existing process before attempting to make improvements. Define is the first phase of DMAIC and it identifies the problem needing to be addressed while also establishing the scope, goals, and benefits for the project or study. For the purpose of this study, a project charter was created and presented to Imation s management team for approval to move forward with the study. Measure is the second phase of the DMAIC process. The purpose of the measure phase is to gather basic information regarding the existing process and to quantify the problem using data. This study used a process observation chart to document the existing business process. Imation s business intelligence (BI) reporting tool was used as the measurement system. The third phase of DMAIC is analyze and its purpose is to understand cause and effect relationships from the measure phase and determine the root cause of the problem from the define phase. This study used statistical analysis, correlation, and regression testing to determine if a relationship existed between human intervention in the forecast and forecast error and forecast bias metrics. Improve is the fourth step of DMAIC and its purpose is to improve the process with learning s from the analyze phase. Potential solutions were developed to help control the inputs to the existing business process. For the purposes of this study a future state process chart was developed showing an alternative solution to the existing process. The alternative solution was validated with data collected in the analyze phase and presented to Imation s management team for feedback. The last step of DMAIC is control. The purpose of the control phase is to develop standards and procedures to ensure that future process performance does not deviate from learning s and improvements from the first four phases. For this study, work instructions and process maps were created as control plans to facilitate demand

13 13 planners and sales teams. Final direction from Imation s management team was pending on the recommended alternative business process. Summary Imation s Canada sub-region consumer accounts were consistently missing forecast error and forecast bias metrics in their optical media category. This directly impacted the company s goals and objectives, specifically on hand inventory dollars. This study evaluated Imation s forecasting practices and roles and responsibilities for their optical media category using Six Sigma methodology of process improvement. DMAIC was chosen as the method of evaluation and using a 12 month evaluation period, a project charter was developed and authorized by Imation s management team, a process chart was created to document the existing process, statistical analysis was used to evaluate inputs the forecasting process, and solutions were developed to address the problem. At the end of the evaluation a recommendation was presented to Imation s management team. The recommendation was brought together from findings during the evaluation as well as from best practices learned from Chapter II literature review where sales and operations planning and forecasting was researched.

14 14 Chapter II: Literature Review Forecasting optical media in Imation s Canada sub-region was resulting in unfavorable forecast error and forecast bias metrics. This directly impacted the company s goals and objectives, specifically on hand inventory dollars. The purpose of the study was to evaluate Imation s Canada sub-region consumer forecasting practices for their optical media category. Using a 12 month time period, demand planners and sales teams used the Six Sigma DMAIC approach to evaluate how optical media forecasts were generated and assessed roles and responsibilities for the inputs to those forecasts. The literature review will discuss three main themes: An overview of the sales and operations planning process, the demand review of the sales and operations planning process, and forecasting impacts to an organization. The sales and operations planning process overview will cover benefits from implementing the process and the major steps required to complete it. The demand review theme will introduce forecasting and the different methods used to generate a forecast. The last theme, forecasting impacts to the organization, will explain how different techniques can be used to generate a forecast along with how forecasts get measured. Sales and Operations Planning In the mid 1980 s, Dick Ling, an educator at Oliver Wight, was teaching a class on aggregate planning to a manufacturing company and used recognized manufacturing terms to better relate his course to the audience. The audience quickly pointed out that the manufacturing terms did not align well with the course materials. Shortly after, Oliver Wight surveyed 25 companies to come up with a name for teaching the process of aggregate planning and from that survey came the term sales and operations planning (Ling & Coldrick, 2011). Sales and operations planning provides an organization the framework to better understand forecasted

15 15 demand, supply abilities to meet that demand, and a financial picture of the revenue generated from those demand and supply plans. A robust sales and operations planning process has clear expectations on roles and responsibilities to the inputs of the sales and operations planning process and metrics to determine if the organization is meeting its goals (Crum & Palmatier, 2003; Lapide, 2002; Sheldon, 2006). The American Production and Inventory Control Society (APICS) define sales and operations planning as, a process to develop tactical plans that provide management the ability to strategically direct its businesses. The process brings together all the plans for the business into one integrated set of plans ( APICS, n.d., para. 2). In addition to a well-defined sales and operations planning process, corporate culture plays a role in realizing the full benefits of sales and operations planning. Some (Boyer, 2009; Milliken, 2008) suggest sales and operations planning is a top down management process while others (Lapide, 2004) argue middle managers are the decision makers empowered by top management. In both situations, the commonality is that multiple business functions need collaboration in order for sales and operations planning to be effective. Participants to the process include sales, customer service, marketing, purchasing, logistics, operations, finance and executive management or a surrogate from the executive team ( APICS, n.d.; Lapide, 2004; Ling & Goddard, 1988; Sheldon, 2006). In order for collaboration to take place across multiple business functions the corporate culture must enable trust, honesty, and openness. Successful collaboration removes departmental silos and places emphasis on what is best for the company (Mello & Stahl, 2011). Additionally, collaboration drives consensus decision making through sharing of data. This teamwork approach allows for sales and operations planning to drive a single operational plan centered on achieving organizational goals and objectives, not departmental goals and objectives (Lapide,

16 ). Also, organizations that emphasize responsibility and accountability in their corporate culture are more likely to have effective sales and operations planning processes (Mello & Stahl, 2011) because the participants feel more responsible for the plans coming out of the process and therefore hold individuals more accountable for the inputs going into the process. As Mello (2010) notes, Since people usually take their cues on how to act from others around themcolleagues, supervisors, managers, executives- the right type of corporate culture needs to be in place for an sales and operations planning process to be properly nurtured (p. 46). The benefits from a robust sales and operations planning process coupled with a corporate culture supporting it include: Improved communication throughout the business, improved inventory management and customer service, greater accountability, improved financial plans, and greater opportunity for profits (Ling & Goddard, 1988; Prokopets, 2012; Sheldon, 2006). Those benefits result from three key steps of the sales and operations planning process: Demand review, supply review, and finance review. Demand review. Every organization that utilizes a sales and operations planning process has unique needs and therefore will use a customized version of the process designed specifically for them. Irrespective of how the process is customized, the demand review is the first major step (Boyer, 2009; Lapide, 2002; Schorr, 2007a; Sheldon, 2006). The goal of the demand review is to establish a demand plan that is agreed upon by key stakeholders. The key stakeholders include demand and supply managers, sales, marketing, and product management ( APICS, n.d; Lapide, 2004; Ling & Goddard, 1988; Sheldon, 2006). Schorr (2007a) notes that a successful demand review has a robust demand management process behind it. The components of the demand management process consist of the demand plan, the assumptions of the demand plan,

17 17 and the action plans to execute the plan. Once the demand plan has been agreed upon in the demand review, supply plans are developed to execute the demand plan. Supply review. The second major step of the sales and operations planning process is the supply review and it generally takes place a few days after the demand review is complete. The goal of the supply review is to develop a supply plan that supports the consensus demand plan. The key stakeholders that develop the supply plan include the head of operations or supply chain, demand and supply managers, manufacturing and production, procurement, logistics, distribution, and finance (Schorr, 2007b). In developing the supply plan, three critical aspects of the supply chain are discussed and measured: Customer service levels, inventory, and operating costs. If gaps exist between the demand plan and supply plan, the supply review addresses the actions needed to fulfill the demand plan. Once supply plans are determined, finance will dollarize both demand and supply plans in order to provide management financial data to support business decisions. Finance review. The last major step of the sales and operations planning process is the finance review and it takes place once demand and supply plans are finalized. According to many (Lapide, 2007; Prokopets, 2012; Schorr, 2007c), the role of finance in sales and operations planning is to take demand and supply plans that are in units and convert them into dollars. This allows management to see the financial evaluation of the demand and supply plans in terms of revenue, cash, profit, and loss. The goal of the finance review is to provide the organization a financial forecast, which includes a profit and loss statement, for the next 12 to 18 months. In addition to providing a financial forecast, Prokopets (2012) states that finance s role in sales and operations planning should expand to include overseeing the execution of the sales and operations planning process and determining business metrics.

18 18 A well-defined sales and operations planning process coupled with a supporting corporate culture allow an organization to fully realize the benefits from sales and operations planning. As shown in Figure 1, three major steps in the sales and operations planning process answer critical questions allowing an organization to better understand its business and make certain all business functions are working towards common goals and objectives. Financial Planning What revenues, costs, profits and investments reflect the sales and operations plans? Demand Planning What is the consensus for customer demand? Sales and Operations Planning (S&OP) What is the integrated plan that aligns demand, supply, and financial plans to achieve the business plan? Supply Planning What is the capacity to support the demand plan? Figure 1. Inputs to sales and operations planning (Prokopets, 2012) Demand Review The first major step in the sales and operations planning process is the demand review. The demand review is critical because the output of the demand review, an agreed upon demand plan, establishes the assumptions for the rest of the sales and operations planning process. Many (Bower, 2007; Wallace & Stahl, 2008; Schorr, 2007a; Lapide, 2003) state the demand review is the most important step of the entire sales and operations planning process. The process owner of the demand review will vary at each organization; however, the main attendees of the demand review will include demand and supply managers, sales,

19 19 marketing, and product management ( APICS, n.d; Lapide, 2004; Ling & Goddard, 1988; Sheldon, 2006). The demand review discusses organizational strategy through inputs such as business strategy (e.g., commercial and consumer), sales forecasts, customer plans, market intelligence, and product management plans. The process owner of the demand review gathers these cross functional inputs and presents the demand plan as a 12 to 18 month forecast. This is the forum where the assumptions of the demand plan get critiqued and discussed in terms of impacts to the organization. Additional topics discussed during the demand review include forecast metrics such as; demand plan accuracy, sales plan accuracy, and forecast bias. Changes from the previous months demand plan are discussed to determine if assumptions have changed or if they are short term trends. The output of the demand review, an agreed upon demand plan, establishes the assumptions for the rest of the sales and operations planning process. Supply plans are based on supporting the demand plan by measuring customer service levels, inventory and operating costs. Finance uses the demand plan to create a financial forecast in terms of profit and loss. In order to reach a consensus demand plan multiple inputs are needed from cross functional teams. Demand plan. The demand plan uses inputs from cross functional teams and merges them into a single a forecast. According to Dooley & Higgins (2006), the demand plan and forecast are independent of one another because demand plans have assumptions from key business functions whereas forecasts are statistically driven and fact based. In this view, the statistically driven forecast is used in sales and operations planning. However, others (Dear, 2008; Lapide, 2004; Schorr, 2007a; Sheldon, 2006; Wallace & Stahl, 2008) note that the terms can be used interchangeably and a demand plan/forecast must contain assumptions from other business functions in order for the demand review to be a collaborative effort.

20 20 In order to generate a 12 to 18 month demand plan assumptions are needed from other business functions because they can provide qualitative data that captures strategy, transformations, or market changes (Lapide, 2003; Lapide, 2004; Schorr, 2007a). Statistical forecasts are limited to using quantitative data which is sufficient for short term planning but lack the qualitative data needed for successful long term demand plans. A statistical forecast is the baseline forecast for which key business functions can layer in market intelligence. As shown in Figure 2, business functions layer in four key inputs to generate the demand plan. Marketing plans Business plans Demand plan Sales plans History Figure 2. Inputs to the demand plan (Sheldon, 2006) Business plans. According to Sheldon (2006), business plans are owned by the chief executive officer (CEO) and encompass organizational influences such as mission statements, vision and objectives, and business imperatives. These influences are needed as inputs to the demand plan because they bridge the gap between business objectives and execution of core processes to meet those objectives. Examples of business plans input would be aligning resources in sales and marketing to meet the desired outcome of a new product launch. This information and the corresponding assumptions belong in the demand plan in order for the

21 21 organization to have an accurate picture of the direction the organization is headed along with the financial implications. Sales and operations planning is centered on achieving organizational goals and objectives, and therefore, business plans must be incorporated into the most important process, the demand review, in order to guide the sales and operations planning process. Marketing plans. Marketing plans involve shaping and creating demand through promotions, price changes, product launch strategies, and external influences such as a competitive analysis. These plans focus on product lines or brands and are likely to be long range (Lapide, 2007; Schorr, 2007a; Wallace & Stahl, 2008). Sheldon (2006) notes, the marketing department needs to estimate what impact each of its strategies will have on customer behavior (p. 62). For example, price increases in a product category will most likely drive demand down, whereas a price decrease may create additional demand. The impact of the marketing plans should be visible in the demand plan in order to provide other business functions, such as supply, ample time to prepare for changes in demand consumption. Sales plans. Sales plans are integrated into the demand plan by adding market knowledge and are usually short term (Schorr, 2007a; Wallace & Stahl, 2008). According to Lapide (2002), the sales plans are aggregated into product groupings by sales districts or regions; however, Wallace and Stahl (2008) argue that a sales plan should be highly detailed at the item level and include specific customers. In both scenarios, the goal of the sales plan is to update the demand plan based on what is currently happening in the market. Adding sales information into the demand plan can use both internal and external inputs. Internal inputs consist of customer orders, shipments, on hand inventories, and current promotional activities. Companies that collaborate with their customers have access to external inputs such as category resets, item transitions, future promotional plans, and point of sale data.

22 22 History. Statistical forecasts are the baseline forecast for which key business functions layer in market intelligence and history is most often used to create the statistical forecast. According to Lapide (2003), using history as the basis for the statistical forecast allows for identifying recurring patterns such as seasonality or normal cyclical patterns. Using history also removes insignificant patterns that can be seen as real changes in demand. However, Sheldon (2006) cautions that using history is the most difficult input to use accurately. Businesses are constantly offering new products and services or promoting their business in such a way that history becomes less accurate. There is either not enough historical data to create a significant statistical forecast or, if enough history exists, the data has to be manipulated to find significance which creates bias in the forecast. Forecasting Impacts to an Organization Sales and operations planning facilitates a cross functional, collaborative forecasting process which includes demand and supply managers, sales, marketing, and product management ( APICS, n.d; Lapide, 2004; Ling & Goddard, 1988; Sheldon, 2006). Through this process organizations can realize the benefits of shared responsibility in forecasting. As shown in Figure 3, Wallace (2006) notes that the operational benefits can be seen as hard and soft. HARD BENEFITS Higher customer service More stable supply rates for production and procurement which result in higher productivity Lower finished goods inventory Shorter customer order backlogs SOFT BENEFITS Enhanced teamwork within the executive team and operating levels of the business Better decisions with less effort and time, yielding better results. Better financial plan that aligns with operational plan More valid and greater accountability for results Better control of the business through one set of numbers, and greater alignment of units and dollars More rapid and more controlled new product introduction Ability to see potential problems (e.g., imbalance of demand/supply) before becoming actual problems Figure 3. Operational benefits to collaborative forecasting (Wallace, 2006) Before these benefits can be realized an organization must determine how they will forecast and two common approaches are top down and bottom up.

23 23 Top down forecasting. The top down approach to forecasting starts with generating a forecast at an aggregate level. The aggregate level is determined by the organization and is based on a predetermined forecast hierarchy (Kahn, 1998b; Lapide, 1998, Lapide, 2006). Examples of the aggregate hierarchy include a geographic group, business unit group, or product group. Once the aggregate forecast has been generated, it gets disaggregated to the individual components that make up the aggregate group, as shown in Figure 4. Top down forecasting is beneficial when the variation of demand patterns at the aggregate level is the same as its lower level components. Many organizations see forecast accuracy improvements at an aggregate level because generally the percent of variation as a whole will be less than the percent variation of individual items (Lapide, 1998). Bottom up forecasting. The bottom up approach to forecasting starts with generating a forecast at the individual component level and then adding their sum to get the aggregate group forecast. Bottom up forecasting is best utilized when individual components have different demand patterns then the aggregate group (Kahn, 1998b; Lapide, 1998). This approach is generally preferred in operational forecasting because organizations plan for each individual component rather than their total sum. Bottom up forecasting allows the forecaster to work at a component detail level which is beneficial when incorporating internal and external data to the forecast. Internal inputs consist of customer orders, shipments, on hand inventories, and current promotional activities. Companies that collaborate with their customers have access to external inputs such as category resets, item transitions, future promotional plans, and point of sale data.

24 24 Figure 4. Top down and bottom up forecasting (Lapide, 1998) Hybrid forecasting. Many organizations will realize benefits from using both top down and bottom up forecasting methods. Choosing which forecasting method to use should be determined by the objective of the forecast. For example, a top down forecasting approach can be appropriate for developing strategic plans or budgets. Bottom up forecasting can be appropriate for production schedules or supply plans. As Kahn (1998b) notes, using the hybrid approach has both advantages and disadvantages: An advantage for undertaking this hybrid approach is that lower level information can be particularly useful in specifying forecasting technique requirements. One can determine which techniques would be most appropriate for which products versus a one technique fits all solution, which often is unsuccessful. A key disadvantage is the amount of time needed to conduct such an analysis. The forecaster could consider segmenting low level analyses/model-building by first analyzing critical items, and then continuing with analyses of less critical items when time allows. (p. 19) Organizations that have carefully planned, robust forecast hierarchies coupled with clear business strategies will find the most value using a hybrid approach to forecasting. Forecast metrics. The first major step in the sales and operations planning process, the demand review, concludes with an agreed upon demand plan which establishes the assumptions

25 25 for the rest of the sales and operations planning process. This demand plan then gets presented to other functional groups as a 12 to 18 month forecast. In order to determine the effectiveness of the demand review process it needs to be measured so gaps can be identified and improvements be made. A robust sales and operations planning process will have information technology (IT) support that allows the forecast to be measured at various levels of the forecast hierarchy (e.g., item level, product family level). The most common forecast metrics are forecast accuracy, which is arguably the most important (Lapide, 2004b), and forecast bias. Forecast accuracy. Forecast accuracy measures how close actual demand was to the forecasted quantity and can be calculated with various mathematical equations, at different time periods, and at different levels of the forecast hierarchy. The decision of how, when, and where in the hierarchy to measure forecast accuracy is determined by the process owner with input from key stakeholders. Measuring forecast accuracy allows the business to identify inefficiencies in the forecast and target areas for improvement. Measuring forecasts can be done either by calculating accuracy or error. Forecast accuracy measures how close actual demand is to the forecasted quantity and is constrained between 0% and 100%. Forecast error measures the deviation of the actual demand to the forecast quantity and is unconstrained, 0% to infinite (Hoover, 2009). Determining when to measure forecast accuracy will vary at each organization and will be influenced primarily by lead times. However, Wallace (2006) argues organizations should focus on a short term measure because the further out a forecast is measured the higher probability of random errors. Organizations can influence short term forecast accuracy by being flexible to market demands and adjusting forecasts to meet customer expectations. Many organizations will measure forecast accuracy at multiple points in the forecast cycle (1 month

26 26 out, 2 months out, etc.) but will only use accuracy at lead time for reporting in the demand review of the sales and operations planning process. Forecast accuracy can be measured at all levels of the forecast hierarchy and will vary at each organization based upon the structure of their sales and operations planning process. Two common ways forecasts are measured within the hierarchy is at the aggregate level and item level (Kahn, 1998a; Lapide, 1998, Lapide, 2006). Examples of the aggregate hierarchy include a geographic group, business unit group, or product group. Aggregate forecasts are likely to be more accurate because they reduce the amount of variability found at lower levels of the forecast hierarchy. An example of when aggregate forecast accuracy is used is the finance review of the sales and operations planning process. Finance reports out using an aggregated dollarized forecast versus using an item by item dollarized forecast. The second common way forecast accuracy gets measured is at the item level. This level of measurement is primarily useful during the second step of the sales and operations planning process, the supply review. A key measurement for supply planning is dollarized amount of inventory (Schorr, 2007b) and understanding forecast accuracy at the item level will allow them to manage expectations for acceptable amounts of inventory. If forecasts are too low they can cause stock outs, whereas, if forecasts are too high inventories build. Forecast bias. Forecast bias measures if a forecast is overstated or understated. This is done over a period of time and tracks actual sales performance compared to the forecast. Positive forecast bias suggests the forecast is overstated and negative forecast bias suggests the forecast is understated. Random bias will naturally occur in the forecast, meaning the forecast is just as likely to be positive as it is negative. This is not of concern because the positives and negatives will balance each other out. On the other hand, consistent forecast bias in one

27 27 direction or the other will have negative impacts to the supply chain (Wallace, 2006). Consistent positive forecast bias has negative impacts to the supply chain such as excess raw materials, excess finished goods, additional warehousing costs, increased inventory hold times, reduced margin, and increased inventory obsolescence. Consistent negative forecast bias results in more demand than supply which can lead to missed sales opportunities, reduced customer satisfaction, order expediting costs, and higher product costs. Summary Sales and operations planning is a cross functional business process that allows an organization to create a single operational plan centered on achieving organizational goals and objectives. It provides a framework to better understand forecasted demand, supply abilities to meet that demand, and a financial picture of the revenue generated from those plans. A successful sales and operations planning process is complemented with a corporate culture that enables trust, honesty, and openness which fosters collaboration throughout the business functions. Organizations that have robust sales and operations planning processes coupled with a supporting corporate cultures may experience improvements on the soft side of the business such as communication and accountability, but also hard benefits such as improved inventory management and customer service. These improvements are the result of three key steps of the sales and operations planning process: Demand review, supply review, and finance review. Measuring the effectiveness of the sales and operations planning process allows an organization to take action on those areas causing unfavorable results and it also identifies areas for evaluation and improvement. The common ways to measure sales and operations planning

28 28 are forecast accuracy and forecast bias. These metrics are evaluated at any level of the forecast hierarchy and should be aligned with all business functions throughout the organization. Organizations that implement robust sales and operations planning processes, have top management supporting the process, have all levels of the business buying in to the effectiveness of the process, and foster a collaborative corporate culture, will lead to a more streamlined supply chain with greater potential for profitability. Chapter III of this study evaluated Imation s Canada sub-region optical media forecasting process for their demand review which was an input to their sales and operations planning process. Six Sigma s DMAIC process was used to evaluate how optical media forecasts were generated and assessed roles and responsibilities for the inputs to those forecasts.

29 29 Chapter III: Methodology Forecasting optical media in Imation s Canada sub-region resulted in unfavorable forecast error and forecast bias metrics. This directly impacted the company s goals and objectives, specifically on hand inventory dollars. The purpose of this study was to evaluate Imation s Canada sub-region consumer forecasting practices for their optical media category. Using a twelve month time period, demand planners and sales teams used the Six Sigma DMAIC approach to evaluate how optical media forecasts were generated and assessed roles and responsibilities for the inputs to those forecasts. This chapter has developed the first three phases of DMAIC and how they were used in the study. Define Phase The first phase in the DMAIC problem solving technique is the define phase. The purpose of the define phase is to reach agreement on the scope, goals, and financial and performance targets for the project (George, Rowlands, Price, & Maxey, 2005). For this study, the first deliverable was a project charter. The project charter contained five key components: An overview, scope, objectives, assumptions, and supporting data. The project overview stated Imation s Canada sub-region had difficulties forecasting optical media and how making improvements to forecast error and forecast bias would align with management s goals and objectives, specifically reducing on hand inventory dollars. The project scope specifically stated the work content that was included and excluded from the study. The scope was separated into three target areas which included product categories, customer accounts, and product warehousing. Imation s Canada sub-region had 12 product categories and the scope of this study was limited to optical media, specifically CD s, DVD s, and Blu-ray discs. All of Imation s Canada sub-region customer accounts were in scope and the project

30 30 charter stated Imation s remaining regions, USA and Latin America, were to be excluded. Imation used three warehouses to supply optical media discs and this study was limited to a single warehouse, SouthHaven. The other two warehouses were excluded from the study at Imation s request. The project objectives placed metrics against what the study was to deliver. Specifically, reducing forecast error by 5% percent and reducing forecast bias by 5% percent. It was assumed reducing forecast error and forecast bias would not only improve forecasting metrics, it would indirectly reduce the amount of inventory dollars because Imation s supply planning team would procure product more accurately based on improved forecasts. Assumptions for the study were considered to be true and were agreed upon by project team members and key stakeholders. The assumptions stated demand planners would maintain and manage the forecast inputs to the sales and operations planning process. Sales teams would only add confirmed promotional volume or new item sets to the forecast in order to limit the amount of human intervention in the forecast. Human intervention in the forecast was permissible when not taking action would result in negative impacts to the customer. Additionally, monthly collaboration meetings were held between the demand planner and sales analyst to discuss the forecast in detail. The final step of the project charter had supporting data validating Imation s Canada sub-region had difficulties forecasting optical media. The completed project charter was reviewed with management for approval. The next phase of the study involved collecting and processing data. Measure Phase The second phase in the DMAIC problem solving technique is the measure phase. The purpose of the measure phase was to identify the current state process of the study and to determine how data will be collected to measure improvements (George et al., 2005). For this

31 31 study, a process observation chart was developed to understand the current state of how Imation s Canada sub-region forecasted optical media. The deliverable was to understand what work was being done and who was doing it. In order to set a baseline for the current process, the optical media demand planner and Canada s sales analyst met every other day during the November 2012 forecasting cycle to discuss and document (Figure 5) the optical media forecasting workload for their respective roles. Figure 5. Process observation chart The tools used for collecting data in this study came from two Imation sources: JDA and Business Intelligence. JDA is Imation s demand software tool that aides in developing statistical forecasts (Figure 6). Figure 6. Example of JDA statistical forecast

32 32 Business Intelligence is a data reporting tool that imports data from JDA and presents it back to the end user in the form of charts, graphs, or data tables (Figure 7). Figure 7. Examples of business intelligence reports The collection of data from JDA allowed key inputs of the forecast to be identified and noted on the process chart. Those key inputs were historical demand, demand planner overrides, and sales overrides. Also, JDA enabled this study to collect and evaluate each forecasted optical media item in Imation s Canada sub-region by identifying if an item was active or discontinued. Imation s Business Intelligence data warehouse provided the baseline metrics for this study and was the preferred tool for measuring forecast metrics during the course of the study. Forecast error and forecast bias were evaluated at two different levels. The first level measured the subregion as a whole and the second level measured the accounts within the sub-region. Measuring the accounts helped identify where the biggest opportunities for improvements were as well as prioritize resources towards those improvement efforts.

33 33 Analyze Phase The third phase in the DMAIC problem solving technique is the analyze phase. The purpose of the analyze phase is to find the causes affecting the key inputs and outputs of the study (George et al., 2005). Imation s Canada sub-region inputs for optical media forecasting came from JDA, demand planners, and sales teams. The JDA input to the forecasting process occurred on the first workday at the start of a new month. Every item with prior month actual demand was logged in JDA and with this new history demand software ran statistical algorithms to apply a new forecast model. The model then projected these historical patterns into the future thus creating a new statistical forecast. Demand planners reviewed statistical forecast exceptions to improve the forecast models validity. As shown in Figure 8, the system flagged a forecast model as an exception when the new statistical forecast fell outside of acceptable thresholds. Model exception indicated with yellow star Figure 8. JDA statistical forecast with model exception The next step in the forecast process was when demand planners expertise was added to the statistical forecast. Demand planners did not change a specific forecasted value, rather, they

34 34 adjusted the statistical algorithm parameters (Figure 9), which changed the overall forecast trajectory. User controlled parameters Figure 9. JDA statistical forecast with algorithm parameters In addition to tuning new statistical forecasts, demand planners managed product life cycles of existing items by managing forecasts on items in a phase out or discontinued status. The forecasts were adjusted to match on hand inventory levels so when inventory was depleted the item moved to an obsolete status. The next step in the forecast process was performed by sales teams. Every sales team reviewed their accounts statistical forecast in JDA. This statistical forecast was the result of the demand planners work done in the prior step. Sales teams reviewed each item and its corresponding market intelligence. Market intelligence was in the form of promotional activity, new item sets, pricing, or supply issues. Sales teams used human intervention to override the statistical forecast in order to capture the market intelligence in the forecast. Additionally, at Imation sales teams owned the forecast which meant they were held accountable for its performance. Therefore, they had the authority to change a forecast value

35 35 with or without cause. When demand planners and sales teams work was completed the forecast moved to the final step of the forecast process which was collaboration. Demand planners and sales teams met and reviewed the final forecast for each account. If a demand planner disagreed with a sales teams override in the forecast, it was their job to persuade the sales team to change it. As owners of the forecast, sales teams had final authority to the forecast that was presented in the final step of the forecast process, the demand review. Statistical analysis testing. Using historical data collected from December 2011 to November 2012, statistical analysis tests were performed on optical media to understand the cause and effect relationship between human intervention in the statistical forecast and forecast metrics for Imation s Canada sub-region. JDA captured a data point each time a sales team overrode a statistical forecast, as shown in Figure 10, and this allowed the study to measure correlation between sales teams intervention in the statistical forecast and forecast metrics. Override shown in forecast as red bars Figure 10. JDA statistical forecast with sales team overrides

36 36 Correlation is used to determine if there is a relationship between two variables (George et al., 2005; Miles & Shevlin, 2001), however, it does not imply one variable caused the other variable to happen. This study used regression analysis tests to determine if the variables were correlated. The results of the test focused on two outputs: Multiple R and P Value. In order for variables to show correlation they must have a Multiple R value between the ranges of.65 to 1 or -.65 to -1 (George et al., 2005). If it was determined that correlation existed, the next test was to determine if the correlation was significant. Significant correlation implies it is unlikely that the relationship between the two variables occurred by chance. A value for P, which measures significance, of less than.05 was evidence the correlation was significant and a P value greater than.05 suggests it was not significant (George et al., 2005). Figure 11. Regression testing output Regression analysis test results performed in this study would be recorded as testing output in a format as shown in Figure 11. The results measured the correlation between sales teams overrides and forecast error as well as sales teams overrides and forecast bias. Tests were performed at Imation s Canada sub-region aggregate level and also at each account level. The

37 37 accounts were measured in order to identify where the biggest opportunities for improvements were as well as prioritize resources towards those improvement efforts. Summary Forecasting optical media in Imation s Canada sub-region results were unfavorable forecast error and forecast bias metrics and directly impacted the company s goals and objectives. Using a 12 month time period, demand planners and sales teams used the Six Sigma DMAIC approach to evaluate how optical media forecasts were generated and assessed roles and responsibilities for the inputs to those forecasts. Chapter III described the steps in define, measure, and analyze phases for documenting how this study identified the problem, how the data was to be collected, and how the data was to be analyzed. Chapter IV will present findings from all steps in the DMAIC process as well as results on forecast error and forecast bias metrics from this studies trial period of December 2012 through August 2013.

38 38 Chapter IV: Results Forecasting optical media in Imation s Canada sub-region results were unfavorable forecast error and forecast bias metrics and directly impacted the company s goals and objectives. The purpose of this study was to evaluate Imation s Canada sub-region consumer forecasting practices for their optical media category. Additionally, roles and responsibilities for demand planners and sales teams were assessed. The goals of this study were to reduce forecast error by 5% and reduce forecast bias by 5% which, in addition, would support organizational goals of reducing on hand inventory dollars. Using a 12 month time period, demand planners and sales teams used the Six Sigma DMAIC approach to evaluate how optical media forecasts were generated and assessed roles and responsibilities for the inputs to those forecasts. Define Phase Results The purpose of the define phase was to reach agreement on the scope, goals, and financial and performance targets for the project (George et al., 2005). As illustrated in Appendix A, this study developed a project charter which included an overview, scope, objectives, assumptions, and supporting data. The supporting data was the final step of the project charter and Business Intelligence provided 12 months of historical forecast metric data from December 2011 to November The historical forecast metrics (Figure 12) demonstrated Imation s Canada sub-region consumer accounts were consistently missing forecast error and forecast bias metrics in their optical media category. This data was presented to Imation s management team to validate a problem existed and that it needed fixing. Imation s management team granted permission to conduct a study from December 2012 to August 2013.

39 39 Figure 12. Imation s Canada sub-region historical forecast metrics Measure Phase Results The purpose of the measure phase was to identify the current state process of the study and to determine how data would be collected to measure if improvements were being made (George et al., 2005). During the November 2012 forecasting cycle the optical media demand planner and Canada s sales analyst met to document the optical media forecasting workload for their respective roles. The results of their collaboration are shown in Figure 13. Figure 13. Optical media current state forecast process The current process started on work day one of a new month with the demand planner creating a statistical forecast and ended on work day 11 with a collaborative forecast in preparation for the monthly sales and operations planning demand review.

40 40 Analyze Phase Results The purpose of the analyze phase was to find the causes affecting the key inputs and outputs of the study (George et al., 2005). After analyzing the process observation chart this study identified critical inputs to the optical media forecast came from JDA, demand planners, and sales teams. Using 12 months of historical data collected from the define phase, statistical analysis tests were performed on optical media to understand the cause and effect relationship between human intervention in the statistical forecast and forecast metrics for Imation s Canada sub-region. The results of the test focused on two outputs: Multiple R and P Value. A Multiple R value between the ranges of.65 to 1 or -.65 to -1 is evidence of correlation. A value for P less than.05 is evidence the correlation is significant (George et al., 2005; Miles, 2001). Statistical analysis testing. The first test, shown in Figure 14, was to determine if correlation existed between the number of sales teams overrides and forecast error. According to George et al. (2005), a Multiple R value between the ranges of.65 to 1 or -.65 to -1 is evidence of correlation and the results of this test was a Multiple R value of meaning there was a strong relationship between sales teams overrides and forecast error. A value of P less than.05 is evidence the correlation is significant (George et al., 2005; Miles, 2001) and the result of this test was a P value of This data suggests the lower number of sales team overrides, the lower percentage of forecast error. The second test, shown in Figure 15, was to determine if correlation existed between the number of sales teams overrides and forecast bias. According to George et al. (2005), a Multiple R value between the ranges of.65 to 1 or -.65 to -1 is evidence of correlation and the results of this test was a Multiple R value of showing evidence of a weak relationship between sales teams overrides and forecast error. A value of P less than.05 is evidence the correlation is significant (George et al., 2005) and the result of this test was a P

41 41 value of , evidence that the correlation was not significant. This data suggests no relationship existed between the number of sales teams overrides and forecast bias. Based on the statistical tests performed in the analyze phase, this study concluded that sales teams human intervention on the statistical forecasts in Imation s Canada sub-region were negatively impacting forecast error metrics in the optical media category. Figure 14. Correlation between sales teams overrides and forecast error

42 42 Figure 15. Correlation between sales teams overrides and forecast bias Improve Phase Results The fourth phase in the DMAIC problem solving technique is the improve phase. The purpose of the improve phase was to take learning s from the analyze phase and implement solutions to the problem. Solutions to the problem are then trialed to determine if they control or eliminate the root cause identified in the analyze phase (George et al., 2005). Results from the analyze phase showed significant correlation existed between sales teams human intervention on the statistical forecast and forecast error metrics. The more times sales teams overrode the statistical forecast the worse the forecast error metric became.

43 43 Concluding this was a contributing factor to the root cause of missing forecast metrics, the primary point of evaluation became the process chart which detailed how optical media was forecasted. Using the current state optical media forecasting process chart as a baseline, demand planners and sales teams used a working meeting to redesign the process with the goal of minimizing the number of times sales teams used human intervention on the statistical forecast. The first change to the process made the demand planners owners of the forecast. Sales teams no longer had authority to change a forecast value without consent from the demand planner. Instead, sales teams established collaboration meetings with the demand planners to make forecast adjustments solely on promotional activity or new item sets. If the sales teams wanted additional forecast values changed, they needed data to support their recommendation. With this change in responsibility sales teams no longer needed four work days to complete their forecasting tasks. The second change to the process added one additional work day to the demand planners allotted time for tuning the statistical forecast models. These changes are shown in Figure 16. Figure 16. Optical media future state forecast process

44 44 Control Phase Results The last phase in the DMAIC problem solving technique was the control phase. The purpose of the control phase was to develop standards and procedures to ensure that future process performance does not deviate from learning s and improvements from phases one through four (George et al., 2005). For this study, work instructions and process maps were created as control plans to facilitate demand planners and sales teams during the forecasting process. Business processes were documented and work instructions were created to ensure clear roles and responsibilities were addressed throughout the forecasting cycle. The first business process, as shown in Appendix B, addressed forecast changes within supply lead times. Significant forecast adjustments that occurred within supply lead times required special actions by sales forecasters and supply planners. This situation was an exception to the future state process chart developed in the improve phase and therefore needed specific rules on roles and responsibilities. The second business process, as shown in Appendix C, addressed situations where an item being forecasted had inventory constraints. Sales teams received notification if an item they forecasted had inventory constraints and could have resulted in customer orders being unfulfilled. It was the demand planners role to discuss inventory allocation plans with the sales teams so necessary adjustments could be made in the forecast to capture back logged demand. The third business process, as shown in Appendix D, outlined the process demand planners and sales teams followed when forecasting new items. Due to long lead times on new items, Imation needed to enter a forecast in order to procure product prior to having commitment that the new item was placed at an account. Demand planners and sales teams collaborated on this effort based on the sales teams percent confidence level that the item would get placed, the number of

45 45 accounts showing interest in the new item, and overall volume. The last business process that was defined in the control phase was specific to demand planners tuning statistical forecasts. As shown in Appendix E, demand classification was a process within the JDA software tool that applied the best forecast model and parameters to the statistical forecast. This process benefited the demand planner because it reduced statistical forecasting process time by allowing the demand planner to manage forecasts by exception. The JDA system would tune forecast parameters and notify the demand planner of the forecasts that fell outside allowable thresholds. Goal Attainment Results The goals of this study were to reduce forecast error by 5% and reduce forecast bias by 5% which, in addition, would support organizational goals of reducing on hand inventory dollars. From December 2012 to August 2013 this study forecasted optical media in Imation s Canada sub-region using the future state process chart, work instructions, and process maps to measure if the changes made from the DMAIC process reduced forecast error and forecast bias. Forecast error results. The pre-study forecast error metric for Imation s Canada subregion averaged 74.69% from December 2011 to November The trial period for testing the changes made from the DMAIC process went from December 2012 to August 2013 and forecast error averaged 49.67% (Figure 17). This was a 33.50% reduction in forecast error. Figure 17. Forecast error metrics

46 46 Forecast bias results. The pre-study forecast bias metric for Imation s Canada subregion averaged 22.98% from December 2011 to November Additionally, forecast bias was positive 58.33% of the times meaning the forecasts were generally overstated. The trial period for testing the changes made from the DMAIC process went from December 2012 to August 2013 and forecast bias averaged 22.94% (Figure 18). This resulted in a 0.20% reduction in forecast bias. Also, forecast bias was positive 77.78% of the times which indicated that forecasts remained overstated during the trial period. Figure 18. Forecast bias metrics Inventory dollars. A secondary goal of this study was to support organizational goals of reducing on hand inventory dollars by means of reducing forecast error and forecast bias. This study assumed reducing forecast metrics would indirectly reduce the amount of inventory dollars for the reason that Imation s supply planning team would procure product more accurately based on improved forecasts. The pre-study inventory dollars for Imation s Canada sub-region averaged $26.6 million from December 2011 to November The trial period for testing the changes made from the DMAIC process went from December 2012 to August 2013 and inventory dollars averaged $24.7 million (Figure 19). This was a 7.2% reduction in inventory dollars.

47 47 Figure 19. Inventory dollars in relation to forecast error Summary Imation s Canada sub-region s forecast metrics were unfavorable which directly impacted the company s goals and objectives. Using a 12 month time period, demand planners and sales teams used the Six Sigma DMAIC approach to evaluate how optical media forecasts were generated and assessed roles and responsibilities for the inputs to those forecasts. The DMAIC process allowed for a systematic evaluation of the existing process and resulted in identifying a root cause to the problem. Using statistical tests, correlation and regression, it was concluded that sales teams using human intervention on the statistical forecasts were responsible for the unfavorable forecast error metric. An updated process chart was developed to improve the forecasting process and control plans were put in place to ensure that future process performance does not deviate from learning s and improvements from the DMAIC process. Imation s Canada sub-region optical media demand planner and sales analyst trialed the results of the DMAIC process from December 2012 to August The results of that trial resulted in forecast error reduction, forecast bias reduction, and inventory dollar reduction. Chapter V will further discuss results drawn from Chapter IV and provide recommendations to Imation s management team for continuing work related to this study.

48 48 Chapter V: Discussion Forecasting optical media in Imation s Canada sub-region results were unfavorable forecast error and forecast bias metrics and directly impacted the company s goals and objectives. The purpose of this study was to evaluate Imation s Canada sub-region consumer forecasting practices for their optical media category. Additionally, roles and responsibilities for demand planners and sales teams were assessed. The goals of this study were to reduce forecast error by 5% and reduce forecast bias by 5% which, in addition, would support organizational goals of reducing on hand inventory dollars. Using a twelve month time period, demand planners and sales teams used the Six Sigma DMAIC approach to evaluate how optical media forecasts were generated and assessed roles and responsibilities for the inputs to those forecasts. Limitations The scope of this study was limited to Imation s Canada sub-region consumer accounts and the optical media category, specifically CD s, DVD s, and Blu-ray discs. It did not include America or Latin America sub-regions and it did not include commercial accounts. This study did not include product categories magnetic tape, flash drives, hard disc drives, and consumer electronics. Imation used three warehouses to supply optical media discs and this study was limited to a single warehouse, SouthHaven. In addition, source of supply, lead times, minimum order quantities, and item rationalization were also out of scope for this study. Data collected for this study was limited to the dates between December 2011 and August This study was also limited to the Six Sigma methodology of process improvement, specifically, the DMAIC process. Lastly, evaluating forecasting roles and responsibilities were limited to the Canada optical media demand planner and Canada sales analyst.

49 49 Conclusions This study was successful in using learning s from the literature review and the Six Sigma DMAIC process to reduce Imation s Canada sub-regions optical media forecast error, forecast bias metrics, and on hand inventory dollars. In addition, the Canada optical media demand planner and sales analysts roles and responsibilities were redefined to align with updated business processes. The literature review explained the importance of a robust planning process in order for an organization to be successful at long term planning. Sales and operations planning process was reviewed in terms of creating a robust process through three key steps: Demand review, supply review, and finance review. Several key factors needed to be in place in order for an organization to be successful at sales and operations planning. The first key factor was collaboration because many business functions were part of sales and operations planning such as sales, customer service, marketing, purchasing, logistics, operations, finance and executive management. The second key factor was a corporate culture that enabled trust, honesty, and openness which removed departmental silos and placed emphasis on what is best for the company. And the last key factor was responsibility and accountability to the sales and operations planning process. Through collaboration, demand planners and sales teams achieved their results by using the Sig Sigma methodology of process improvement, specifically, the DMAIC process to reduce forecast error, forecast bias, and on hand inventory dollar for Imation s Canada sub-region optical media category. The first phase, define, identified Imation s Canada sub-region had a problem forecasting optical media by developing a project charter with historical forecast data supporting the problem statement. The second phase, measure, resulted in a process observation

50 50 chart which helped understand the current state of how Imation s Canada sub-region forecasted optical media. The third phase, analyze, analyzed forecast data through statistical correlation and found that strong correlation existed between the number of times the sales team used human intervention on the forecast and forecast error results. The fourth phase, improve, resulted in updates to the process observation chart with learning s from the analyze phase, and the last phase, control, resulted in updated business processes and process maps. The goals of this study were to reduce forecast error by 5% and reduce forecast bias by 5% which, in addition, would support organizational goals of reducing on hand inventory dollars. Goal attainment for this study resulted in forecast error reduction of 33.50%, reduction of forecast bias of 0.20%, and reduction in on hand inventory dollars of 7.2%. Recommendations The use of Six Sigma methodology and the DMAIC improvement process was focused on one of Imation s three sub-regions. Additionally, only the optical media product category was evaluated in this study. The use of the DMAIC improvement process in Imation s other sub-regions and product categories may also result in reduction of forecast error, forecast bias, and on hand inventory. Further analysis should be conducted in these areas to determine the need for additional Six Sigma activities. This study was successful in updating the workflow for Canada s optical media demand planner and sales analyst during the monthly forecasting cycle. The process of identifying the current state of the forecasting process, identifying opportunities for improvement, and implementing a future state process can be performed for other product categories as well. Additional research should be conducted to determine the potential opportunities for improving forecasting workflows within other sub-regions and product categories.

51 51 Improving forecast error and forecast bias resulted in on hand inventory reductions for this study. As shown in Appendix F, statistical regression tests demonstrated significant correlation exists between forecast error and on hand inventory dollars. The lower the forecast error percent, the lower on hand inventory dollars. Understanding this positive impact to the entire organization, Imation can use this information to further educate demand planners and sales teams the importance of improving forecasting metrics to meet organizational goals and objectives. In conjunction with educating demand planners and sales teams on the importance of improving forecasting metrics, the adoption of a culture that supports forecasting improvements would promote trust, honesty, and openness which would further benefit collaboration among multiple business functions. Collaboration reinforces the idea that employees are acting and placing emphasis on what is best for the company. In addition to internal collaboration, it is recommended that Imation evaluate the benefits of establishing an external collaboration process with both suppliers and customers. Establishing monthly supplier meetings, sharing data and performance metrics, and assisting suppliers with issues that may negatively impact supply chain performance may aide Imation in the development of their forecast. This collaboration would also provide relevant information for discussion in their monthly sales and operations planning meeting. Establishing collaboration with customers can result in sharing of point of sale data which benefits both Imation and the customer. Imation can use real time data to update forecasts and improve visibility in their supply chain which benefits the customer by having reducing stock outs resulting in maximized sales opportunities.

52 52 Another recommendation is that Imation add forecast error and forecast bias metrics to individual performance and rewards programs. Currently, demand planners are the only individuals whose performance and reward programs are tied to forecast error and forecast bias metrics. Including other individuals who play a role in developing the forecast, such as sales and marketing, would further emphasize the importance forecasting plays in driving the top and bottom line financial results of the organization. Moreover, the literature review has shown organizations that have robust sales and operations planning processes emphasize responsibility and accountability to the forecasting process. Including forecast error and forecast bias to individual performance and reward programs would result in a more serious approach to forecasting. Lastly, it is recommended that Imation evaluate combining the roles of a demand planner and supply planner. Currently, Imation s demand planners are part of the sales organization while supply planners are part of the operations organization. This setup provides a conflict of interest at times because sales is trying to maximize sales opportunities and will over forecast to ensure adequate inventory while operations is trying to drive down costs and inventory is the primary focus. Combining this role could remove bias from the sales and operations organizational hierarchy and allow for an independent view of the supply chain while still keeping the role closely in touch with the various business functions which is needed for a robust forecasting process.

53 53 References APICS online dictionary. (n.d.). APICS. Retrieved from dictionary Bower, P. (2007). How is your demand planning metabolism?. Journal of Business Forecasting, 26(1), Boyer Jr., J. E. (2009). 10 proven steps to successful S&OP. Journal of Business Forecasting, 28(1), Crum, C., & Palmatier, G. (2003). Demand management best practices: Process, principles, and collaboration. Boca Raton, FL: J. Ross Publishing. Dear, T. (2011). Forecasting the weakest link. Supply Chain Europe, 20(1), Dooley, B., & Higgins, R. (2006). S&OP or just good supply chain planning. Logistics & Transport Focus, 8(10), George, M., Rowlands, D., Price, M., & Maxey, J. (2005). Lean six sigma pocket toolbook. New York, NY: McGraw-Hill Press. Hoover, J., & Little, M. (2009). How to track forecast accuracy to guide forecast process improvement. Foresight: The International Journal of Applied Forecasting, 2009(14), Imation (2012). Vision, mission & values. Retrieved from Kahn, K. B. (1998a). Benchmarking sales forecasting performance measures. Journal of Business Forecasting Methods & Systems, 17(4), 19. Kahn, K. B. (1998b). Revisiting top-down versus bottom-up forecasting. Journal of Business Forecasting Methods & Systems, 17(2), 14.

54 54 Kahn, K. B. (2003). How to measure the impact of a forecast error on an enterprise?. Journal of Business Forecasting Methods & Systems, 22(1), 21. Lapide, L. (1998). New developments in business forecasting. Journal of Business Forecasting Methods & Systems, 17(2), 28. Lapide, L. (2002). New developments in business forecasting. Journal of Business Forecasting Methods & Systems, 21(2), 11. Lapide, L. (2003). Make the baseline forecast your trusted advisor. Journal of Business Forecasting Methods & Systems, 22(4), Lapide, L. (2004). Sales and operations planning part I. The process. Journal of Business Forecasting Methods & Systems, 23(3), Lapide, L. (2006). Top-down & bottom-up forecasting in S&OP. Journal of Business Forecasting, 25(2), Lapide, L. (2007). Sales and operations planning (S&OP) mindsets. Journal of Business Forecasting, 26(1), Ling, D., & Coldrick, A. (2011). Breakthrough sales & operations planning: The story and the players. Retrieved from Ling, R., & Goddard, W. (1988). Orchestrating success: Improve control of the business with sales & operations planning. Essex Junction, VT: Oliver Wight Ltd. Publications. Miles, J., & Shevlin, M. (2001). Apply regression and correlation: A guide for students and researchers. London, England: Sage. Mello, J. (2010). Corporate culture and S&OP: Why culture counts. The International Journal of Applied Forecasting, 2010(16),

55 55 Mello, J., & Stahl, R. (2011). How S&OP changes corporate culture: Results from interviews with seven companies. The International Journal of Applied Forecasting, 2011(20), Milliken, A. L. (2008). Sales & operations planning: Building the foundation. Journal of Business Forecasting, 27(3), Oliver Wight International: About us. (2011). Retrieved October 28, 2012, from Prokopets, L. (2012). S&OP: What you can learn from the top performers. Supply Chain Management Review, 16(3), Schorr, J. (2007a). The demand review. Business Excellence, 2007(3), Schorr, J. (2007b). The supply review. Business Excellence, 2007(4), Schorr, J. (2007c). The language of management. Business Excellence, 2007(5), Sheldon, D. H. (2006). World class sales and operations planning: A guide to successful implementation and robust execution. Ft. Lauderdale, FL: J. Ross Publishing. Wallace, T. (2006). Forecasting and sales & operations planning: Synergy in action. Journal of Business Forecasting, 25(1), Wallace, T., & Stahl, B. (2008). The demand planning process in executive S&OP. Journal of Business Forecasting, 27(3),

56 56 Appendix A: Imation s Canada Sub-region Forecasting Project Charter Project Overview The purpose of this project is to improve Imation s Canada sub-region forecast metrics in Optical Media. This will be accomplished through collaboration from Sales, Marketing and Demand Planning. This project supports the following executive MBO s: forecast accuracy improvements (owner: Garthwaite), and financial objectives (owner: Thielk). Project Scope Included: Excluded: Categories Categories CD AV Tape Cleaning/Maintenance Flash DVD Cables/Chargers Digital Audio Midrange Blu-ray Cases/Storage HDD Optical Drives Accounts: Best Buy Canada Canadian Tire Costco Canada Loblaws Other Commercial Canada Other Consumer Canada The Source Wal-Mart Canada Accounts: Sub-region USA Sub-region Latin America Warehouse: Warehouse: SouthHaven (SHK) Toronto (TOR) Nippon (IEH) Frontier (IE1) Direct Import (DSH) Other: Estimate to finish (ETF) Proforma Project Objectives Reduce forecast error 5% Reduce forecast bias 5% (+/-) o Reduce inventory dollars (indirect) Reduce Sales forecasting workload Project Assumptions Demand Planning will maintain and manage the forecast inputs to S&OP Sales will add confirmed promotional volume or new item sets to the forecast

57 57 Demand Planning (R. Foster) will hold monthly meeting during workdays 6-9 to review forecast with Canada Sales Analyst (KC Heller) o Sales Analyst will act as liaison between Demand team and Canada team Project will last approximate 3-6 months, starting December 2012 through approximately May 2013 Historical Forecast Metrics

58 58 Appendix B: Forecast Changes within Supply Lead Time Process Definition: Significant forecast adjustments that occur within supply lead times require special actions by sales forecasters and supply planners. Supply lead times vary by product so forecasters and planners must be aware of lead times in order to know when to use this process. Business Process Flow: Business Process Content: Sales forecaster receives new information that increases the forecast for an item or items more than 20%. Sales forecaster determines whether the change is within supply lead times (if not known contact Supply Planning) If the change is within lead time, sales forecaster documents change (item, upside and date) and notifies Supply and Demand planner. o Preferred method is to both followed up by phone call or face to face. Doing both steps a) documents the request and b) establishes criticalness of request. Supply planner reviews request and notifies sales forecaster and demand planner which items can and cannot be supported. o The supply planner assesses whether the change can be covered by normal safety stocks. (Add the additional demand directly into the SKU External Fcst table in the period required and can then Rerun the Calculate Plan process for their SKUs only. The results can be analyzed and discussed, and then the rows in the SKU External Fcst table can be deleted. The next batch run will return PLAN results to

59 59 normal. Alternatively, the supply planner can use Scenario functionality to make the assessment). o If the change can be covered by safety stock, the planner notifies the sales forecaster and demand planner and takes no further action. o If the change cannot be covered by safety stock, the supply planner places a requisition for the appropriate quantity and notifies the sales forecaster and demand planner of the action and the estimated arrival date of the inventory in the IMN warehouse. o The supply planner then assesses whether inventory allocation will be required for the item in question. If it is, the supply planner begins the inventory allocation process. The change(s) in forecast are made in JDA Collaborate by the sales forecaster. Comments are entered to help planning understand the circumstances surrounding the reasons for the change to forecast. The sales forecaster then communicates to the Demand Planner notifying them JDA Collaborate has been updated. The following day, the Demand Planner pushes the forecast to Supply using mid cycle forecast adjustment steps. Steps for Adjusting a Mid-Cycle Forecast: 1. Forecasts must be adjusted at DFU level 1 Example: Forecast adjustment (override) applied to a level 1 DFU.

60 60 2. Once the level 1 DFU has been adjusted, it then must be published. Publishing a draft forecast transfers it from the Fcst Draft table to the Fcst table. 3. Navigate back to the DFU page and select the level 1 DFU that needs to be published. 4. Select Publish Forecast (Default) from the related pages. Note: Publish Forecast is a JDA process. You will see a job status pop up while the process is running. 6. Once the publish process is complete, the forecast must be transferred to JDA Fulfillment. To accomplish, you must run the Transfer Forecast process. 7. Select the level 1 DFU that needs to be transferred and select Transfer Forecast (ALL_SKU s) from the related pages.

61 61 Note: Transfer Forecast is a JDA process. You will see a job status pop up while the process is running. 8. Once the process is complete, you can verify the forecast has successfully transferred to JDA fulfillment 9. Select from the JDA directory the FE page titled: DFUtoSKUFcst-Hz-Eaches 10. Select from the search prompt I_GIC and enter the corresponding DemandUnit / GIC that was sent to fulfillment. 11. The transferred forecast should be in this table.

62 62 Appendix C: Forecasting During Inventory Constraints Process Definition: Use this process when either of the following situations occurs: An item is currently or projected to be in a constrained inventory situation Sales team has submitted an increase in forecast on an item inside of lead time and need to receive the product even though we cannot support it. If the constrained product is at End of Life or will be constrained for more than a month, Demand Planning needs to inform the Product Line Manager for direction on how they want to allocate the product Business Process Flow: 1. If an item has projected inventory constraints for less than 2 weeks Supply Planning will manually reserve by account based on percent of lag 2 forecast and will communicate to the Strategic Account teams those quantities. a. These items are identified by Plan Analysis in JDA or Retail Wizard in BI. b. Percent of forecast is identified in the Retail Wizard in BI i. While in the tool, drill down but clicking on the on item number then customer forecast Note: Supply Planning will put all inventory on manual reserves in an ask planner reserve and then send the % of forecast quantities to Demand Planning, Sales and Ops Analysts. Those accounts not receiving any allocation but requiring allocation will need to address with Demand Planner before 3PM the following business day. Demand Planning will then advise Supply Planning the revised allocation. Supply Planning will now allocate inventory into manual reserves based on the final demand plan sent by the demand planner. 2. If an item has projected inventory constraints for greater than 2 weeks and only one strategic account assigned to that item, same guidelines apply as step 1. Note: Supply Planning will put all inventory on manual reserves in an ask planner reserve and then send the % of forecast quantities to Demand Planning, Sales and Ops Analysts prior to manually reserving. Those accounts not receiving any allocation but requiring allocation will need to address with Demand Planner before 3PM the following business day. Demand Planning will then advise Supply Planning the revised allocation. Supply Planning will now allocate inventory into manual reserves based on the final demand plan sent by the demand planner 3. If an item has projected inventory constraints for greater than 2 weeks and more than one strategic account team, steps 3a- 3e will be followed: a. Supply Planning will manually reserve any available inventory into an Ask Planner bucket. i. Supply Planning will then inform Demand Planning of the constraint and manual reserve. b. Demand Planning will lead investigation on root cause of the inventory constraint and propose an allocation plan. i. On a case by case basis, Supply and Demand Planning may collaborate on root cause and allocation plan

63 63 c. Demand Planning will review allocation plan with Ops Analyst and/or Sales Team and facilitate any discussions with regards to changing the allocation plan. If agreement cannot be reached, Demand Planning Manager, Finance Director, & Sales Directors will have final decision. d. Once consensus has been reached, Demand Planning will report to Supply Planning how much inventory each account is allocated. e. Supply Planning will manually reserve inventory in Oracle based on written direction from Demand Planning. Point of Contact Supply will remain point for discussions related to available inventory discussions for unforecasted customer demand within lead-time. Once it is known that an item is constrained, the Ops Analyst will coordinate communications for Key Accounts and the COR or Sales for those accounts without an assigned Ops Analyst. Demand will own allocation plan communication to Ops Analysts & Sales based on discussions from Step 3c.

64 64 Appendix D: Forecasting New Items Process Definition: Gathering sales intelligence on products that have the potential to sell into accounts and forecasting appropriately depending on confidence, volumes, # of customers and product life cycle. Business Process Flow: Business Process Content: Converting Speculation into Consensus general rules: o Take volume time probability as the base - in formula o If 3 or more customer are speculating, increase consensus (assumes we will have an outlet) - in formula o If 2 or fewer customers are speculating, decrease consensus (assumes we may have no outlet) - in formula o If product is already placed at account with high volumes, increase consensus (we will have an outlet) - DP discretion o If product is not placed at account with high volumes, decrease consensus (assumes we will not have an outlet) - DP discretion o If product is at the beginning of its life cycle, increase consensus - DP discretion

65 65 o If product is at the end of its life cycle, decrease consensus - DP discretion o If highest volume speculative channel has high confidence, increase consensus (assumes we will have a high volume outlet) - in formula o If highest volume speculative channel has low confidence, decrease consensus (assumes we will not have a high volume outlet) - in formula Rules on Items to speculate o Any Audio Player, Gaming Controllers, Gaming, Chargers, XM Chargers, Headphones >$50, and any new item that passed the development gates in the last month. o These groups are chosen because they generally have longer lead-times. In the case of new items, we want speculation before we place the first mass production order. This way project management has the chance to stop mass production and kill the project if the speculative volume is too low. o Items in the normal FCST should not be in the speculative FCST. This means that if an item was previously speculated on and we got a firm commitment, the sales forecaster should put In normal FCST in the speculative form and the demand planner should delete it. o Planners should guide and educate sales on which items should be speculated on during the monthly meetings. Monthly Process Steps (details in swim lane flow chart above): Project Management provides list of items that passed Development Gate Spec FCST lead notifies sales of new items Sales speculates on potential sales for long lead-time and new items DPs, PLMs, and Sales Forecasters meet to review/edit speculative forecast. Demand Planners generate recommended FCST based on Excel workbook formula provided by Spec FCST lead. Demand Planners edit consensus FCST based on current placements and product life cycle Demand Planners gain consensus during Demand Review Demand Planners provide Project Management with speculative FCSTs for items that passed Development gate.

66 66 Appendix E: Demand Classification Process Definition: This process defines how Imation handles demand classification to tune and update models. There are some rules and guidelines to this process. Optimizing models can be ran monthly by using the Default batch, while Classify Only will be ran quarterly but only on the default models with a creation date between 6 and 12 months old. The goal of running demand classification is to have the best model and parameters applied to the different levels of DFU s. Demand classification need to be ran on the different levels of DFU that are actively planned and always on request of the planner. The Fourier model will be used as default model until enough history points are available to apply a new model. This process can only be performed by the regional superuser. Business Process Content: To be able to run demand classification you need to select the Process: Classify DFU s and Tune Parameters. There are 3 options you can select: Classify Only - batch based on history type applying new models to DFU s Default - optimize parameters Tune Only - set parameters back to original values To run a batch you first need to Choose option set than a search selection (Run With) and then run the batch by selecting the run icon. Another option would be to go to the DFU FE and make your selection by using the search and filter option. When loaded go to related pages and select the batch you want to run. Classification Manager To view the output of the batches ran you need to go to demand classification manager. There are two tabs Classification Results and Algorithm Selection Results were you can find the different outputs. The results of the batches ran will only be shown in the DFU tables the next

67 67 day but are available in the Classification manager after running. The old DFU will be eliminated after the nightly batch. Classification Result - results per classification. Algorithm Selection Results - results of new models applied. Classification Results: By double clicking on the Class names you can see which DFU s have the pattern applied. Algorithm Selection Results:

68 By double clicking on the Algorithm model names you can see which DFU s have the model applied. By default the First recommended model will be applied automatically. The results need to be provided to the responsible planner. 68