During the summer of 2016 I had an internship in the Fab Materials Planning group (FMP) at Intel Corporation. FMP generates long-range (6-24 months) forecasts for chemical and gas materials used in the chip fabrication process. These forecasts are sent to Commodity Mangers (CMs) in a separate department where they communicate the forecast and any constraints to Intel suppliers. The intern manager of the group, Scott Keithley, created a prototype of a model to redefine how FMP determines which materials require a forecast update (forecasting cadence). However, the model prototype was complex to use, not intuitive, and did not receive positive feedback from the rest of the team or external stakeholders. This thesis will detail the steps I took in identifying the main problem the model was intended to address, how I approached the problem, and some of the major iterations I took to modify the model. It will also go over the final model dashboard and the results of the model use and integration. An improvement analysis and the intended and unintended consequences of the model will also be included. The results of this model demonstrate that statistical process control, a traditionally operational analysis, can be used to generate a forecasting cadence. It will also verify that an intuitive user interface is vital to the end user adoption and integration of an analytics based model into an established process flow. This model will generate an estimated time savings of 900 hours per year as well as giving FMP the ability to be more proactive in its forecasting approach.
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