Case: redstone foods m&m wholesale case study sales forecasting

  

Case Issue:

Redstone Foods has been acquiring cases of M&Ms based on Model #1: 2020 Forecast 3% Growth Over 2019 Only forecast model (Column H). During the second quarter of 2020, the COVID-19 situation significantly impacted demand.

Material to Include in Your Current Assessment:

You should answer the questions and address the items in your current assessment document. You do not need to answer these questions in a separate document. 

· Analyze Demand Patterns

o Using The M&M Sales Trends (Cases Sold) graph, determine any consistent patterns that demand has followed from 2017 – 2019. These patterns could indicate some seasonality in demand (or predictable demand changes). Include a summary of the trends you have found in your report. Consider what causes these patterns, such as holidays, etc.

o Analyze 2020 demand and consider whether or not demand is following the patterns you identified in 2020.

· Analyze Current Inventory 

o The company has been purchasing cases of M&Ms based on the 3% over growth forecast for January 2020 through September 2020. This process means that the company purchases in advance the number of cases required to cover each month as listed in column H (Model #1). For example, the company purchased 59,062 cases to sell in January of 2020, 59,983 cases to sell in February 2020, and so on. Calculate the total number of cases of M&Ms purchased from January through September of 2020 and the number of cases of M&Ms sold from January through September of 2020. What is the difference between total cases purchased in the period and total cases sold in the period? 

o If you continue to purchase cases of M&Ms based on the forecast, how will your end of year inventory be impacted? Include this assessment in your report.

· Analyze labor and productivity impact

o The original forecast indicates that you will have needed a total of 791,940 cases for 2020. You have labor hired to accommodate this amount of order processing at an expected productivity rate of 95.18 cases per hour based on four employees working a total of 8,320 hours during the year. 

o Assuming the current demand and sales, how is productivity currently impacted, and how will it be affected through the year’s remainder? (Hint calculate the productivity rate for the sales to date (end of September) using the following data:

§ The total number of cases sold January through September of 2020

§ You have not furloughed or laid off any workers for the present, so they are all reporting to work and working. Labor hours for January through September are 1,560 hours per employee for a total of 6,240 hours. 

o Provide a summary assessment of the labor and productivity situation and recommend a plan for moving forward. Your plan should be supported with evidence from your demand forecast analysis. (Hint – if you layoff any employees, consider what the company’s activities will be if and when demand picks up). 

Forecasting

Your next task is to develop a recommendation for forecasting the remainder of the year. Let’s look at the forecasting models. 

Complete the following tasks in the Excel document. The questions will help you collect data and draw conclusions for your report.

1. Confirm that the following pre-set existing values are present. If they are different, then key in the values below:

· Apply Seasonality = “No” (Cell B21)

· Forecast Growth Rate = 3.00% (Cell B22)

· Forecast Weights = 20% (Cell B23); 30% (Cell C23); 50% (Cell D23)

2. Determine which of the five forecast models performed the best based on MAD, MSE, and MAPE. Think about why the particular model performed the best across the first nine months of the year. 

3. We use seasonality adjustment factors to adjust forecasts for the presence of regular movements that relate to recurring events such as holidays. Your team has calculated seasonality adjustment factors in column AC of the Data Section in the spreadsheet.  

a. Change the value of Cell (B21) from “No” to “Yes” to have the four new forecasting models take seasonality into account

4. Now determine which of the five forecast models performed the best based on MAD, MSE, and MAPE. Think about why the particular model performed the best across the first nine months of the year and include this assessment in your report as part of your recommendation. 

5. Create a graph showing MAPE for all five forecasting models across the four specific periods, 2020 YTD, 1st Quarter, 2nd Quarter, and 3rd Quarter. Include the figure in your report. Full credit for a chart is contingent on including all of the following, a chart title, legend, x-axis title, and y-axis title.  One possible example is the clustered column chart below. Charts are embedded within the text by right-clicking on the chart and selecting the option indicated below

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