Case Study: Demand Forecast, Inventory Management and Capacity design at Pharma.Tech

Pharma.Tech is a company specialized in manufacturing and selling paramedical equipment varying from needles to medical uniforms worn by medical or laboratory staff. Currently and under the effect of the coronavirus outbreak, the company is planning to adjust its annual production of medical face masks to the drastically changed market demand. While the World Health Organization is recommending everyone to wear medical-grade masks in public, such masks are difficult and expensive to make. Therefore, the company should accurately predict the total market demand of these type of masks. Moreover, the prices for such items are highly regulated and knowing that the storage cost of the medical face masks is about 60 to 80% from their total cost, the company should manage its inventory while keeping its total inventory cost at minimum. As the operations manager of Parma.Tech you are responsible forproviding a plan to accurately forecast the future demand of medical face masks, then manage optimally the company stock level and propose direction regarding the company capacity design in order to improve its productivity.

Part I: Demand Forecast

As the company operations manager, you want to be certain that enough drivers and vehicles are available to deliver orders promptly and that they have adequate inventory of face masks in stock. Therefore, you want to be able to forecast the demand for deliveries during the next period (period 15). From the records of previous orders, management has accumulated demand data for the past 14 years.

Questions:

1. From the data in your table and your knowledge regarding the pandemic situation that affected the world, can you guess which forecasting method is the most reliable? Justify your guess.

2. Using the MAD Criterion, which forecasting method is the most accurate.

data
Period	Year	Demand
1	2007	3457
2	2008	3912
3	2009	2565
4	2010	6474
5	2011	8010
6	2012	8075
7	2013	8826
8	2014	8506
9	2015	6129
10	2016	3999
11	2017	3961
12	2018	6184
13	2019	2400
14	2020	88260