Conclusions

As we state throughout this research, our main goal is to develop a new strategic capacity planning model for two of Sensata’s automotive departments. The need for such a model arose due to the simplicity of their current model and the limitations imposed by Excel. To structure our research, we define multiple research questions, which we previously introduce in Chapter 1. Throughout this research, we provide the answers for these questions and we conclude by summarizing the most relevant remarks here.

To achieve our goal, first we perform a detailed analysis of the current situation, specifically, the current capacity planning model used by the two departments. Through this analysis we identify the factors considered in their model, the simplifications incurred by the capacity managers and the relevant factors which were excluded due to such simplifications. In terms of existing factors, we identify the following: demand, capacity related aspects (number of working days, number of hours available for production, buffer and OEE), and processing times.

Considering the simplifications incurred by the capacity managers, these vary from using average values for parameters such as processing times and OEE to excluding relevant factors such as machine capabilities or releases. As a result of using averages for processing times and OEE, their model is not capable of reflecting the differences between multiple machines of the same type. Such an example is the difference on processing times, for the same product, present between two machines of two different generations. Moreover, when analyzing the current situation, we also investigate what decisions can the capacity managers make in order to keep up with the constant increase in demand. As we state on multiple occasions, the three main decisions refer to building inventory, releasing existing machines or purchasing a new machine.

Besides obtaining a better understanding of the current situation, we perform a literature study focused on two main aspects. On one hand, we focus on defining a linear programming model to match Sensata’s manufacturing process, hence looking into various types of objective functions and constraints. As a result, we formulate the mixed-integer linear programming model we previously describe in Chapter 4 and use it for benchmarking the results we obtain from our heuristic. On the other hand, we look into the best algorithms, known in literature, which can be used for designing such a heuristic. We choose to have this heuristic serve as the main deliverable of this research and the basis of a capacity planning tool that Sensata can use in the future. As a result of the literature review, we perform comparisons between

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Simulated Annealing and Tabu Search for three different stages of our heuristic. These stages refer to optimizing the initial schedule, selecting the best machines to release for the products assigned on our ICM, and selecting the best products from this ICM for which a newly arriving machine should be released. To ensure that the MILP model reflects the real-life situation of Sensata’s manufacturing process, we use the inputs from their current model in our testing. Because aspects such as building inventory or releasing existing machines are not considered in the company’s model, we could only compare the two models in terms of the monthly overall loading.

When building our heuristic, we pick an iterative approach where we implement one step at a time. At each of the three optimization stages, we perform a comparison between two different local search algorithms. We select the parameters for each local search algorithm by combining the guidelines available in literature and the experimentation with different values. Based on the results, our final choices in terms of optimization methods are performing Tabu Search for all the three optimization stages.

In this research we create a heuristic to replace the current strategic capacity model used within

Sensata’s automotive departments. We compare the results with the optimal values, which we obtain by defining a MILP formulation matching their manufacturing environment.

Our method distinguishes itself from the ones we encounter during our literature research due to the approach we choose. Most of the models for strategic capacity planning rely on (MI)LP models modelling the constraints of the various manufacturing environment. Some authors choose a combination of such models and simulation techniques, while others combine it with combinatorial optimization techniques, such as Branch and Price. Given the resemblance of Sensata’s manufacturing process with a job shop, we opt for local search algorithms, such as Simulated Annealing and Tabu Search, in the design of our heuristic. According to our literature study, these methods are the best at solving scheduling models in a job shop manufacturing system.

Besides our approach, another difference comes from the factors we decide to consider in our model. Most of the strategic capacity planning models strictly consider aspects such as inventory build-up, capacity requirements and the number of resources required for production. In our research we choose to extend on the general models we encounter in literature and include additional constraints related to, for example, machine capabilities and releases, OEE components and lead times for the three main actions available to the capacity managers. We do this in the attempt of reflecting the real-life situation, as accurately as possible, in our model.

Considering the applicability of our algorithm, we believe that it can be adjusted to fit similar manufacturing systems in the make-to-order environment.

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