Steelworks: Inventory and Average Monthly Demand
To: Kirk Callow, Steelworks Inc.
After a careful analysis of the Durabend (DB) and Duraflex (DF) lines it has become evident that there is volatile demand for specialty products, given that both DB and DF have high coefficients of variation of 1.08, 1.18 respectively (Exhibit 1). In the case of DB, the volatility can be attributed to the lack of high-volume customers (194/195), and the abundance of low-volume purchasers (Exhibit 2). To solve this problem it will be necessary for Steelworks to more accurately forecast expected inventory for low-volume products, as well as produce products twice a period.
In order to create a solution for Steelworks inventory problem it is necessary to make the following assumptions. First, we assumed a 92% service level taking into account the large percentage of canceled orders (up to 30%), and allows for high customer retention. To estimate lead time we looked to the average lot size, and then compared it to average demand, which gave us a lead time 0.25/Month (1 week). Finally, we calculated R based on the monthly production cycle associated with the Steelworks factory. The above noted assumptions allowed us to estimate monthly expected inventory per month, and compare it to the actual inventory held per month (Exhibit 3).
As depicted in Exhibit 4, Steelworks is inaccurately matching expected and actual inventor based on demand, with the most notable inventory differences being within the DF R15, DF R10 with 61% and 75% excess inventory respectively. This lead us to the idea to re-adjust the forecast of expected inventory by decreasing R, therefore producing inventory twice a month, thus reducing cycle stock by half and decreasing overall inventory levels and associated costs (Exhibit 5). Additionally, the process will have improved flexibility due to the multiple periods of production.
Once cycle stock is cut in half, it will be beneficial to pool the product lines of all common items in a centralized warehouse. The
After a careful analysis of the Durabend (DB) and Duraflex (DF) lines it has become evident that there is volatile demand for specialty products, given that both DB and DF have high coefficients of variation of 1.08, 1.18 respectively (Exhibit 1). In the case of DB, the volatility can be attributed to the lack of high-volume customers (194/195), and the abundance of low-volume purchasers (Exhibit 2). To solve this problem it will be necessary for Steelworks to more accurately forecast expected inventory for low-volume products, as well as produce products twice a period.
In order to create a solution for Steelworks inventory problem it is necessary to make the following assumptions. First, we assumed a 92% service level taking into account the large percentage of canceled orders (up to 30%), and allows for high customer retention. To estimate lead time we looked to the average lot size, and then compared it to average demand, which gave us a lead time 0.25/Month (1 week). Finally, we calculated R based on the monthly production cycle associated with the Steelworks factory. The above noted assumptions allowed us to estimate monthly expected inventory per month, and compare it to the actual inventory held per month (Exhibit 3).
As depicted in Exhibit 4, Steelworks is inaccurately matching expected and actual inventor based on demand, with the most notable inventory differences being within the DF R15, DF R10 with 61% and 75% excess inventory respectively. This lead us to the idea to re-adjust the forecast of expected inventory by decreasing R, therefore producing inventory twice a month, thus reducing cycle stock by half and decreasing overall inventory levels and associated costs (Exhibit 5). Additionally, the process will have improved flexibility due to the multiple periods of production.
Once cycle stock is cut in half, it will be beneficial to pool the product lines of all common items in a centralized warehouse. The