Case Study- Furnmart Inc.

Furnmart Inc is a medium size Ontario Company, which supplies office furniture to different shops in the GTA.

The company does not have the financial resources to hold high levels of inventory, and therefore manufactures its furniture per sale orders. This strategy causes long delivery times (4-5 weeks) and other operational inefficiencies, including the cancellation of sales orders by their most loyal customers. Other furniture manufacturers, mainly from the USA have started penetrating into the GTA market, and the business future for Furnmart looks bleak.

John Smart the founder and CEO, decided to change the corporate strategy and to manufacture products for stock. In that way its delivery times will decrease to less than one week, and the company will be able to keep their market share and even increase it.

In order to minimize stock (inventory) levels and their very high financial cost, Smart asked the Operations Manager- Dave Butcher, to develop a forecasting method that will achieve this goal.

You just got your business degree from Humber, and on Monday afternoon you were assigned the job of developing a forecasting method.

It was decided to develop a prototype forecast, with only one product, and then to develop forecasting methods for all the other products of the company, if the prototype was successful.

Dave Butcher gave you the actual demand for one of the products- Single Computer Desk (SCD), which was as follows:

He asked you to make a report for his Wednesday morning meeting with John Smart. You should investigate three forecasting methods: Simple Moving Average, Weighted Moving Average and Exponential Smoothing. Then, test each of them on the actual demand of years 2021/2022/2023. The more successful method will be used for the real forecasting in year 2024. Dave added that he had heard vaguely about a method ("they did not teach it when I was a student"), which uses trend, seasonality and "some other staff", and maybe you should look into it as well. -Please help me and show manually and through excel.

6. Using the exponential smoothing method with α = 0.8, calculate the forecast for each possible month of year 2021/2022/2023. Calculate the MAD, MSE and MAPE for this method. Assume that the forecast for January 2021 was 50. Show one manual calculation of a forecast of this method, for any month you choose.

7. Draw a new graph, which includes the demand graph you did in Point 3 and three additional forecast graphs- one with the simple moving average figures (You calculated in Point 4), one with the weighted moving average figures (You calculated in Point 5) and one with exponential smoothing method (You calculated in Point 6).

8. Which method (SMA, WMA, EXM) is more suitable for the SCD, according to each forecast error (MAD, MSE and MAPE)? Explain.

9. Use trend and seasonality components of demand to develop a better forecast (than the methods you used so far in this assignment) for each month of 2024.