By a Flexible Semiconductor Supply Chain Model
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Balancing inventory and equipment contingencies by a flexible semiconductor supply chain model 15.09.2015 Annemiek Adriaansen i | P a g e Project initiator Infineon Technologies Am Campeon 1-12 85579 Neubiberg Germany www.infineon.com Project title Balancing inventory and equipment contingencies by a flexible semiconductor supply chain model. Author Anna M.A. (Annemiek) Adriaansen Master Industrial Engineering and Management Specialisation: Production and Logistics Management Supervisory committee Supervisors University of Twente Dr. Ir. Ahmad Al Hanbali Dr. Ir. Marco Schutten Supervisor Infineon Technologies Sebastian Eirich, MSc. ii | P a g e iii | P a g e Preface After six months of hard work, I am proud to present you my master thesis. With this thesis I finalise my master in Industrial Engineering and Management and with that, my student days are over. Working-life is next and I am ready to discover what this new part in life will bring, in which I will be able to apply all the things I learned during the past 21 years in school, during internships, and, above all, in university. I would not have been able to write this thesis without the support of many people, to whom I am very grateful. First of all, I thank Hans Ehm, Thomas Ponsignon, Stefan Degel, and Sebastian Eirich, for giving me the opportunity to write my thesis at Infineon Technologies on such an interesting topic, which made it also possible for me to stay in Munich (Germany) after my exchange semester. Furthermore, I am very grateful for the support of my company supervisor, Sebastian Eirich, and the endless time he took to support me with feedback. I could not have wished for better supervision. I also thank the Scenario & Flexibility Planning team of Stefan Degel for their warm welcome and the relaxing lunch talks and “digestion walks”. I always felt part of the team. My gratitude also extends to Ahmad Al Hanbali and Marco Schutten of the University of Twente, for their helpful discussions and feedbacks to make this thesis more readable and to bring its quality to a higher level. Even though we were not able to meet every month personally, we still made it work through Skype or Lync. Finally, I thank my family and friends who helped me get through the ups and downs during my entire studies, made sure I made time to relax from time to time, were always there for me when I needed them, and were understanding for the fact that I did not have that much time for them anymore during the last phase of my master thesis. I wish you all a pleasant time reading this thesis. Munich, 15th of September 2015. Annemiek (Anna M.A.) Adriaansen iv | P a g e v | P a g e Management summary Motivation A semiconductor supply chain is complex and faces numerous challenges, such as volatile and uncertain demand, globalisation, a rapid changing environment, and long lead times. This results in demand- and supply-side uncertainties. Infineon hedges against these uncertainties by adding inventory and capacity buffers (also called contingencies). The Scenario and Flexibility Planning team built a supply chain simulation model, representing the internal supply chain, to study and balance these contingencies. However, the existing simulation model is not flexible enough to adjust to changes in the environment and is not yet accurate enough to ensure reliable results. Research objective The objective of this research is to develop a simulation-based framework to balance inventory and equipment contingencies for Infineon. For this purpose, we should provide the existing supply chain simulation model with flexibility and increase its accuracy (maximal deviation of 5% from the real data). With that model we want to know how to achieve a specific inventory service level at lowest total costs (inventory and capacity), and how this service level and the costs depend on stock levels and utilisation. Supply chain simulation model We extend the existing supply chain simulation model to improve its flexibility and accuracy. We achieve flexibility by enabling to load other bottleneck equipment into the simulation model automatically on model start-up. To increase its accuracy we improve the non- bottleneck delay determination and lot size calculation, and introduce factors that make up for data discrepancies. These improvements lead to the desired overall accuracy for each Key Performance Indicator (KPI), except for Layer Out per Week. We explain this by the fact that wafer losses are currently not included in the simulation model. Our major achieved accuracy improvement is for the cycle time, which improved from -21.9% to 1.1%. Optimising the trade-off between inventory and equipment contingencies We represent the inventory contingencies by the die bank and distribution centre (DC) stock levels and the equipment contingency by the utilisation and vary these in our experiments. We assume that the simulated optimal utilisation (in combination with the inventory contingency) corresponds with the fraction of the uptime that Infineon should use for the planning, also called the Plan Load Limit. For each simulated factor level combination we vi | P a g e collect the following KPIs: different service level types (adjusted non-stock out probability, fill rate, and adjusted fill rate) and the total costs. We use a simplified demand scenario as input for the experiments to be able to understand and quantify the relationships. Balancing inventory and equipment contingencies Based on the outcomes of the experiments we identify the following relationships: - Lower costs do not always mean a lower service level. - Producing at a low utilisation has a big negative impact on the costs although this positively influences the service level. For example producing at 80% instead of at 75% utilisation you can reduce total costs with about 5.5% (depending on the stock levels). - Regardless of the service level type, a high inventory level is more important from a service level perspective, especially the inventory at stocking points close to the customer, because reaction time is the fastest there. DC stock is, however 2.6 times, more expensive than die bank stock in our case. - For each service level type different factor level combinations lead to the lowest costs. To achieve certain adjusted non-stock out probability, both a high die bank stock level and a high DC stock level are important. To achieve a specific fill rate at lowest costs, having a high die bank stock level and utilisation is more important than having a high DC stock level. We explain this by the fact that the fill rate does not consider backorders of previous weeks. To achieve a certain adjusted fill rate at lowest cost, it is more important to have a high DC stock level instead of a high die bank stock level. Recommendations We recommend utilising the available equipment as high as possible and buffer at the DCs and die banks to make up for being less flexible and slower due to an increased flow factor (represents the variability in the process). What “as high as possible” means needs to be studied with a more detailed model. For further research, we propose Infineon to compare the current simplified model to a more detailed model. We laid the foundation by making the modelled bottlenecks flexible. Furthermore, we suggest to increase the reusability of the simulation model and to document the way data in the databases are obtained (when, where, and what) and to set-up databases that contain data specifically for simulation purposes. Moreover, we propose to include a more detailed backend and to extend the frontend object in the simulation model. For further experiments, we suggest to use a more realistic demand scenario with a broader product mix and to use a lower increment when increasing the factor levels. Moreover, we propose to replace the simplified planning function in the simulation model by a more advanced object. vii | P a g e Table of contents Preface .................................................................................................................................. iv Management summary .......................................................................................................... vi Abbreviations ......................................................................................................................... x Symbols ................................................................................................................................ xi 1 Introduction .................................................................................................................... 1 1.1 Organisation ............................................................................................................ 1 1.2 Research motivation ................................................................................................ 4 1.3 Problem description ................................................................................................. 6 1.4 Research objective .................................................................................................. 7 1.5 Scope ...................................................................................................................... 7 1.6 Research approach ................................................................................................. 8 2 Analysis of current situation ...........................................................................................13 2.1 Inventory and equipment contingencies .................................................................13