1. Step one is do the calculation as described in the document.
2. Then write a seven pages recommendation report based on the current situation. see attachment for more details.
3. And please read the document carefully!!!

• Real Sportz is based on an actual forecast audit conducted through the University of Tennessee (names have been changed to protect the innocent).
• Real Sportz (RS) is a manufacturer and distributor of many different types of technical/high performance athletic apparel. RS sells a line of hoodies that are popular with sports enthusiasts across the country.
• Sales are supported through five distribution centers across the U.S., aligned with the five sales regions the company has identified.
• The RS hoodie is not considered a seasonal product, but there is some variability in demand.
• The company has been struggling to meet demand for the product in recent months, and you have been asked to examine the problem and make recommendations to the RS executives. You start with trying to understand the forecasting process.
The Forecasting Process
1. As it exists today, each sales region forecasts demand for its own market area. The process (such as it is,) is fairly similar across the five regions, and is summarized in the following paragraphs.
2. Some research on your part reveals that the forecasting process consists of two separate components: the weekly short-term look (STL), and the monthly market demand forecast (MDF). These two forecasts are not connected or reconciled.
3. The STL has an extremely short-term orientation, and is very tactical in nature. A great deal of the focus in the STL process is allocation of limited capacity to various customers. The MDF, on the other hand, is more focused on capacity planning, since it looks out a full year.
4. Forecasting efforts are based almost exclusively on input from customers and distributors in the form of spreadsheets and POS data. “We pretty much go by what the customer says they need,” reported one marketing manager. Yet others are concerned about the unreliability and upward bias build into customer forecasts. The forecasts submitted by some of the customers are viewed as their tools for assuring adequate inventory levels rather than actual projected demand.

5. There is at this time, no consistent, agreed-upon standard for collecting, analyzing, or interpreting market or customer-based data. The result is a lack of confidence on the part of the users of the forecasts in the usefulness of the aggregated numbers.
6. In addition, there is considerable second-guessing that takes place. For example, sales will second-guess customer forecasts (“Customer X is notorious for inflating numbers,” although no formal forecasting collaboration with customers is in place nor are customer forecasts measured); Sourcing will second-guess forecasts from sales; Fulfillment/Inventory Management will second-guess forecasts from sales; etc., etc. This second-guessing results in each separate link in the forecasting/planning chain essentially creating its own forecast, since there is a lack of confidence in the previous links.
7. On the positive side, RS does conduct formal weekly meetings between sales, production, and logistics/fulfillment in each region. These weekly Wednesday meetings are primarily exercises at balancing demand and supply. In preparation for these Wednesday meetings, individuals across RS spend all day on Mondays (and often into the night) compiling the “short term lookout” (STL), which serves as the demand input. It should be noted that these meetings are not designed to reach consensus on demand forecasts. Rather, they are more like weekly, tactically oriented Sales and Operations Planning (S&OP) meetings, designed to balance immediate demand with short term available supply. The marketing department is not normally represented in these meetings.
8. Ultimately, however, the forecasting process can be described as primarily driven by the financial plans and targets of the firm. All too often, the time-consuming attempts to accurately predict customer demand will end with an override made by the executive leadership team when the forecast is different from the financial plan/goals. Forecast numbers are often adjusted based on corporate targets. In the STL process, the STL is usually adjusted to capacity and availability of product- in essence it becomes a supply plan rather than reflecting the sales forecasts from the field.
9. In addition, there is no defined forecasting hierarchy that allows for input at multiple levels of the forecast, and subsequent reconciliation to all levels. This lack of defined forecasting hierarchy prevents reconciling forecasts at the SKU level and each subsequent level of product grouping for the forecast, the business plan, and financial targets.
10. Finally, forecasting is not seen as part of developing the business plan (forecasting is viewed solely as a tactical function). At RS, forecasting is viewed solely as a tactical function, primarily for use in matching short-term supply to immediate demand. Additionally, forecasts are highly impacted by the financial plan. Often, sales quotas and goals drive the forecast. Thus, the forecast is dependent upon, rather than interdependent with, the financial plan. The forecast does not appear to be a factor in long term planning or decision-making.

• Another characteristic of the forecasting process is the limited statistical analysis of historical demand being performed. While some individuals may look at historical trends and make projections using “intuition” or historical averages, no true statistical analysis that factors in such variables as trend, seasonality, or regression are applied.
• In terms of managing forecast data, separate marketing, sales, forecasting, and downstream planning systems require manual transfer of data from one system to another. The lack of electronically linked systems creates manual transfer of data from one system to another. Instead of electronic linkages between the systems, information is primarily manually re-keyed into Excel to transfer data between individuals. This creates “islands of analysis, “ in which independent systems provide alternative sources of forecast data, support separate approaches to analysis, and maintain distinct data files.

Performance Measurement
• You discover that RS does not measure forecast accuracy, nor is forecasting performance evaluated in any way at RS. As a result, you have discovered through conversations with functional managers within each region that each functional area criticizes other areas for their lack of accountability.
• Since forecasts originate in sales, this group bears the major burden for this perceived lack of accountability. Many consider the market development forecast, for example, to be inextricably intertwined with sales quotas, and perceive that sales consistently “sandbags” the forecast to influence their targets. Since accuracy is not measured, there is no way to document whether this is true or not. However, the perception results in a perceived lack of accountability in the quality of the forecasts.

Working with Data
• You also gather some data of actual demand for this product over the past 26 weeks (Table 1). You decide to experiment with forecasting models to determine which should be used as you teach RS managers about forecasting. You decide to try several simple models: (a) simple five month moving average (b) three month weighted moving average (0.50, 0.30. 0.20) and (c) exponential smoothing (alpha of 0.2 and 0.9, and a starting forecast of 150 units for period 21) for five periods into the future.
• For the history, use periods 17-21. Forecast the future for periods 22-26. Use the actuals in the table below as they occur.
• Assess forecast performance of each technique using two simple and straightforward metrics: (a) percent error (bias) and (b) MAPE.
• You then decide to try to forecast demand at an aggregate level (this means all regions added together) using the same techniques and measurement assessments and time periods to try to understand the advantages and disadvantages of aggregating demand from a forecasting view.

• Step one is do the calculations as described above.
• Then write a five page recommendation report based on the current situation. Your report needs to address both strategic issues (e.g., role of forecasting, importance of forecasting, forecasting processes, etc) as well as more operational issues (such as which forecasting technique to use, and why). In our actual forecast audits done by the University of Tennessee, we evaluate the company in four areas:
 Organization and functional integration issues
 Basic forecasting and demand management process and approach
 Systems and tools
 Metrics and performance measurement
• These four sections would be a good way to organize your report. An excellent report will provide recommendations to improve their processes, along with the rationale for those recommendations. It will also show and compare forecasts using moving averages, weighted moving averages, and exponential smoothing.
• Make sure you show the forecast analysis.
• There is much that the company can do to improve its forecasting; your job is to help them focus on what’s most important. You want to convince RS management to adopt your ideas!
• Five pages of text (cover page doesn’t count). Appendices or additional pages (figures, tables, charts) can be added, but each appendix included must be referenced in the main report.
• Use sub headings and bullets to organize the various sections in your report, and make it easy and fast to read for an executive.
• 1.5 spacing; 12 point font
• Due date: See syllabus.

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