Buffering against uncertainty in a divergent process flow shop

Virtually all manufacturing environments are subject to both internal and external types of uncertainties. Internally, processing times vary between similar jobs, or units may be scrapped due to poor process capability. Externally, manufacturing operations are directly affected by both customers and vendors. Vendors may supply raw materials in the wrong quantities or off the planned delivery schedule. Customer demands can be unpredictable in terms of both order quantities and demand due dates thus necessitating forecasting.; To combat the adverse effects of uncertainties, operations managers utilize various buffering strategies. Such strategies include, but are not limited to, finished goods and work-in-process inventory, safety lead time, slack capacity, flexible labor and flexible equipment.; The identification of an appropriate buffering strategy given various uncertainty types has yet to be determined. In this dissertation, we study this issue in a divergent process flow shop, e.g. a V-plant, (Umble and Srikanth, 1990). A V-plant is characterized by divergent points, or split-off points, throughout the process flow. At each divergent point, a single item can be processed into multiple items.; We study the effects of four types of uncertainty, as well as the benefits of six different buffering strategies. Throughout this study, we determine the required buffer size needed to achieve a managerially comparable level of service of approximately 95%. The buffering strategies are compared across eight different cost structures reflecting varying levels of material and labor content, and holding cost ratios. Results indicate that the preferred buffering strategies are dependent on the nature of the uncertainty studied. Selected sensitivity analyses further demonstrate how various buffering strategies can be combined or more effectively utilized.