Case Study 1: Forecasting Your line manager, Ms Lionheart, has collected and scrutinised performance data for the company. She was concerned that the inventories were high for certain products which had resulted in significant price reductions and losses for the company. At the same time, the company had run out of stock for other items early in the season resulting in unsatisfied customers and lost sales. Ms Lionheart has concluded that the problem was not with the specific products carried in the stock, but with the quantities ordered by the procurement department for two popular products; Dog Ball Launcher and Lightweight Dog Lead. Dog Ball Launcher is a product carried by the company for the past four years. Quarterly demand data for the past four years are shown in table 1. Last year the company seemed to always be out of stock for this item. The model used by the procurement department to forecast demand for this product over the last two years has been Multiplicative Seasonal model based on seasonal indices developed using data from 2018‐2019 with predicted annual demand increase of 6 units per year since 2019. Lightweight Dog Lead is a new product that the company has been selling for the past two years. When the product was introduced in 2020, it was expected to have a large increasing demand trend. The procurement team has been using Trend Projection with least‐squares method developed using data from 2020 to forecast the demand for this item. However, they now seem to have too much inventory for this product. Monthly demand data for the past two years for this item is shown in table 2. Ms Lionheart now wonders if right forecasting models were being applied to these two products. She wants you to analyse the available data and produce a short report (1,000 words ± 10%) with all relevant tables and figures, to address the highlighted issues. In your report you need to consider the following for both sets of data: 1. Analyse and explain the data. 2. Is appropriate forecasting model being applied? Explain. 3. What forecasting model would you use? Explain why you selected your approach.
Case Study 1:
Your line manager, Ms Lionheart, has collected and scrutinised performance data for the
company. She was concerned that the inventories were high for certain products which had
resulted in significant price reductions and losses for the company. At the same time, the
company had run out of stock for other items early in the season resulting in unsatisfied
customers and lost sales. Ms Lionheart has concluded that the problem was not with the
specific products carried in the stock, but with the quantities ordered by the procurement
department for two popular products; Dog Ball Launcher and Lightweight Dog Lead.
Dog Ball Launcher is a product carried by the company for the past four years. Quarterly
demand data for the past four years are shown in table 1. Last year the company seemed to
always be out of stock for this item. The model used by the procurement department to
forecast demand for this product over the last two years has been Multiplicative Seasonal
model based on seasonal indices developed using data from 2018‐2019 with predicted
annual demand increase of 6 units per year since 2019.
Lightweight Dog Lead is a new product that the company has been selling for the past two
years. When the product was introduced in 2020, it was expected to have a large increasing
demand trend. The procurement team has been using Trend Projection with least‐squares
method developed using data from 2020 to forecast the demand for this item. However,
they now seem to have too much inventory for this product. Monthly demand data for the
past two years for this item is shown in table 2.
Ms Lionheart now wonders if right forecasting models were being applied to these two
products. She wants you to analyse the available data and produce a short report (1,000
words ± 10%) with all relevant tables and figures, to address the highlighted issues.
In your report you need to consider the following for both sets of data:
1. Analyse and explain the data.
2. Is appropriate forecasting model being applied? Explain.
3. What forecasting model would you use? Explain why you selected your approach.
4. Generate the forecast for each period in 2020 and 2021 using the current model and
the model you selected. Analyse the results.
5. Determine the forecast error for the current model and the model you selected.
Explain your method and findings.
6. Provide three key recommendations for the company.
Table 1: Demand for Dog Ball Launcher 2018‐2021
Quarter 2018 Demand 2019 Demand 2020 Demand 2021 Demand
Q1 10 14 20 30
Q2 29 31 26 31
Q3 26 29 28 33
Q4 15 18 30 35
Table 2: Demand for Lightweight Dog Lead 2020‐2021
Month 2020 Demand 2021 Demand
January 36 98
February 42 101
March 56 97
April 75 99
May 85 100
June 94 95
July 101 107
August 108 104
September 105 98
October 114 104
November 111 100
December 110 102
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