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Chapter 8: Time Series Analysis and Forecasting: Case Problem 2 Forecasting Lost Sales Book Title: Business Analytics Printed By: Jigar Jitendrak Patel ([email protected]) © 2021 Cengage Learning, Cengage Learning

Chapter Review

Case Problem 2 Forecasting Lost Sales

The Carlson Department Store suffered heavy damage when a hurricane struck on August 31. The store was closed for four months (September through December), and Carlson is now involved in a dispute with its insurance company about the amount of lost sales during the time the store was closed. Two key issues must be resolved: (1) the amount of sales Carlson would have made if the hurricane had not struck and (2) whether Carlson is entitled to any compensation for excess sales due to increased business activity after the storm. More than $8 billion in federal disaster relief and insurance money came into the county, resulting in increased sales at department stores and numerous other businesses.

The following two tables give (1) Carlson’s sales data for the 48 months preceding the storm and (2) the total sales for the 48 months preceding the storm for all department stores in the county, as well as the total sales in the county for the four months the Carlson Department Store was closed. Carlson’s managers asked you to analyze these data and develop estimates of the lost sales at the Carlson Department Store for the months of September through December. They also asked you to determine whether a case can be made for excess storm-related sales during the same period. If such a case can be made, Carlson is entitled to compensation for excess sales it would have earned in addition to ordinary sales.

Managerial Report

Prepare a report for the managers of the Carlson Department Store that summarizes your findings, forecasts, and recommendations. Include the following:

1. An estimate of sales for Carlson Department Store had there been no hurricane.

2. An estimate of countywide department store sales had there been no hurricane.

3. An estimate of lost sales for the Carlson Department Store for September through December.

In addition, use the countywide actual department stores sales for September through December and the estimate in part (2) to make a case for or against excess stormrelated

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sales.

Sales for Carlson Department Store ($ Millions)

Month Year 1 Year 2 Year 3 Year 4 Year 5

January 1.45 2.31 2.31 2.56

February 1.80 1.89 1.99 2.28

March 2.03 2.02 2.42 2.69

April 1.99 2.23 2.45 2.48

May 2.32 2.39 2.57 2.73

June 2.20 2.14 2.42 2.37

July 2.13 2.27 2.40 2.31

August 2.43 2.21 2.50 2.23

September 1.71 1.90 1.89 2.09

October 1.90 2.13 2.29 2.54

November 2.74 2.56 2.83 2.97

December 4.20 4.16 4.04 4.35

Table 17.27

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Department Store Sales for the County ($ Millions)

Month Year 1 Year 2 Year 3 Year 4 Year 5

January 46.80 46.80 43.80 48.00

February 48.00 48.60 45.60 51.60

March 60.00 59.40 57.60 57.60

April 57.60 58.20 53.40 58.20

May 61.80 60.60 56.40 60.00

June 58.20 55.20 52.80 57.00

July 56.40 51.00 54.00 57.60

August 63.00 58.80 60.60 61.80

September 55.80 57.60 49.80 47.40 69.00

October 56.40 53.40 54.60 54.60 75.00

November 71.40 71.40 65.40 67.80 85.20

December 117.60 114.00 102.00 100.20 121.80

Chapter 8: Time Series Analysis and Forecasting: Case Problem 2 Forecasting Lost Sales Book Title: Business Analytics Printed By: Jigar Jitendrak Patel ([email protected]) © 2021 Cengage Learning, Cengage Learning

© 2022 Cengage Learning Inc. All rights reserved. No part of this work may by reproduced or used in any form or by any means – graphic, electronic, or mechanical, or in any other manner – without the written permission of the copyright holder.

USEFUL NOTES FOR:

Forecasting Lost Sales

Introduction

You’ve likely heard the expression “the best-laid plans of mice and men often go awry.” This is especially true in business, where the unexpected can happen at any time. It’s important to be prepared for those times—and that includes forecasting lost sales. In this post, we’ll explore what lost sales are and why they’re so important to your bottom line. Then we’ll look at some methods for forecasting lost sales so that you can be ready when they occur

What is Lost Sales?

Lost sales are a common occurrence in the retail industry. They can be caused by a number of factors, including external factors such as bad weather or economic conditions. For example, if you have a store located in an area where there are frequent hurricanes and other natural disasters, then it may be difficult for customers to travel to your store during these periods. If this is the case and you have been unable to predict these events accurately enough to protect yourself from lost sales due to them (and therefore unable to make accurate forecasts), then you will likely experience significant losses over time until things return back into normalcy again!

Why is it important to forecast lost sales?

Forecasting lost sales is an important part of business planning and helps you make better decisions. It’s also a great way to reduce the risk of losing money and increase the chance of making it.

Forecasting lost sales allows you to be more efficient, proactive and proactive in other ways as well:

Can we forecast Lost Sales?

It’s easy to say that you can’t forecast lost sales, but it is possible. The best way to do this is by learning the skills required for forecasting lost sales. Forecasting is not a science or an exact art; it’s more like an art form than anything else. If you’re looking for something simple and straightforward, then look elsewhere because forecasting will never be easy!

Forecasting Lost Sales

Use a forecasting tool.

Use historical data.

Combine the two.

Methods for Forecasting Lost Sales

There are several methods for forecasting lost sales.

Historical data: The most common method used in forecasting lost sales is historical data analysis. This means that you look at past sales trends, and try to predict what your future sales will be based on them. For example, if you have been selling a product for five years, then your best guess about how many units of this product will sell each year is based on looking at how many units sold last year compared to previous years.

Trend analysis: Another way to forecast lost sales is by using trend analysis and regression analysis on historical data from similar products or industries (such as clothing retailers). You can also use causal modeling—a process where you identify the causes behind changes in demand over time—to predict future purchasers’ preferences based on past purchasing behaviors of similar customers who bought those products before them (or even those who bought them before those consumers!).

You can’t predict everything, but good forecasting means you’ll be ready when the unexpected happens.

Forecasting means you’ll be ready when the unexpected happens.

You can’t predict everything, but good forecasting means you’ll be ready when the unexpected happens.

Conclusion

Lost sales are an important topic to address. If you’re worried about how much your company will lose this year, or if it already has, then now is the time to start working on a plan. There are lots of different ways to forecast lost sales and one way may not work for everyone. However, having a basic understanding of what causes lost sales and how they happen can help put things into perspective so that when something does happen it doesn’t cause as much damage in your company’s bottom line.