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This simulation study considers a low-volume manufacturing system, which produces complex, customized electronics. Modeling demand as a renewal-reward process, the simulation, inspired by the production system and available data from a Canadian company, examine the performance of alternative inventory policies and operating procedures. Performance indicators that measure the responsiveness and inventory on hand show trade-offs between them in order to supply relevant information to decision makers. Experiments compare make-to-order and make-to-stock scenarios with various inventory parameters as well as introducing variability to examine the model’s robustness under uncertainty. The system under consideration consists of three main processes to manufacture a finished product from raw materials. The first process fabricates metal and electrical components from raw materials. Second, a worker assembles components into a semi-finished product. The third requires information from the customer in order to customize the product according to their needs, and test the unit to ensure its quality. The company, known for their well-designed products and exceptional customer service, wants to improve the accuracy of their leadtime promising. The current MRP control system assumes a completely make-to-order environment where every piece of WIP has a customer order attached to it. However, a forecast of orders likely to materialize from the sales quotes allows production to initiate jobs before the actual order arrives. The approach taken to analyzing this system involves studying the make-to-stock, make-to-order decision at two stock points, components and semi-finished units. The operating procedures examine four possible stocking strategies: holding no inventory, holding only component or semi-finished inventory, and holding both components and semi-finished units. Simulation experiments determine the trade-off between holding inventory and the responsiveness to the customer for each operating procedure. Sources of randomness introduced to processing time, capacity, and demand, show how they respond to added variability. The simulation experiments indicate that holding no inventory, and waiting for a customer order to initiate jobs, results in unstable performance. In order to achieve a stable make-to-order system, it would be necessary to have a fifty percent reduction in demand or product cycle time, a capacity expansion, or forecasting method. In the absence of an accurate forecast model, holding inventory is necessary for an acceptable level of performance. Component inventory is useful as many components are common among a number of products. Suitable component inventory can lead to customer orders typically fulfilled within two weeks. Adding semi-finished inventory can reduce the customer lead-time to under a week though requires stocking at least a few of each semi-finished unit. Holding semi-finished inventory without component stock is possible. However, it is necessary that the replenishment quantity be three or more units ordered at a time. Otherwise, the setup time for components exceeds the allowable limits and resource queues become unstable, much like the completely make-to-order scenario. Using an order-up-to parameter for semi-finished stock can further decrease the setup time incurred per unit. The model is robust to randomness in job times, though it is component stock, which provides an effective buffer to this variability. Machine breakdowns begin to affect responsiveness measures if the average time for repair is greater than a week. Reducing the capacity in the assembly and testing processes can provide the same level of service indicating the two resources are underutilized. The analysis of this system shows the current make-to-order model requires some forecast to function in steady state, which is difficult to model without information on the current forecasting processes. Expanding the simulation model to incorporate forecasting or some other means of analysis can improve its accuracy and credibility as a management decision tool.

Development of a tool for discrete event simulations in forest technology, dependent onspatial components, has successfully been initialized in this thesis project. These simulations may be used to optimize the way the forest is used and to evaluate new machine concepts in forestry. The Python library for discrete event simulation, SimPy, was chosen as the foundationfor the tool. The developed tool can handle spatial objects such as moving machines, trees and boulders. Support for continuous linear movements was also added, which has resulted in a model that partially overlaps continuous and discrete event simulations without any additional computational costs. The result is simulations of a forest with machines operating in it. Two pilot simulations,one of a thinning machine and one of a planting machine, were performed with useful results. The new simulation tool shows promising properties. Limitations and improvements arediscussed, with the conclusion that continued development is recommended. / I det här examensarbetet har ett verktyg för diskret event simuleringar inom skogsteknikutvecklats. Sådana simuleringar kan användas för att optimera arbete inom skogsbruk samtför att utvärdera nya maskintekniska koncept. Programmeringsspråket Pythons bibliotek för diskret event simulering, SimPy, valdessom grund för verktyget. Det utvecklade verktyget kan hantera spatiala objekt såsom träd,stenar och stubbar. Stöd för kontinuerliga rörelser har även utvecklats, vilket resulterat i enmodell som delvis överlappar kontinuerliga simuleringar och diskret event simuleringar.Resultatet är kvalitativa simuleringar av skog och skogsmaskiner. Två lyckade simule-ringsstudier har gjorts för Sveriges Lantbruksuniversitet, en simulering av en gallringsmaskinoch en simulering av en planteringsmaskin. Det nya simuleringsverktyget uppvisar lovande egenskaper, även om det har sina be-gränsningar. Fortsatt utveckling av verktyget rekommenderas starkt.

Existing multi-echelon inventory optimization models and formulas were studied to get an understanding of how safety stock levels are determined. Because of the restrictive distribution assumptions of the existing safety stock formula, which are not necessarily realistic in practice, a method to analyze the performance of this formula in a more realistic setting was desired. A SimPy simulation model was designed and implemented for a simple two-stage supply chain as a way to test the performance of the safety stock formula. This implementation produced results which led to the conclusion that the safety stock formula tends to underestimate the level of safety stock needed to provide a certain service level when predicted standard deviation of demand is underestimated and the assumptions of normally distributed demand and normally distributed lead times are not fulfilled.

Inventory control is important to ensuring sufficient quantities of items are available tomeet demands of customers. The Newsvendor problem is a model used in Operations Research to determine optimal inventory levels for fulfilling future demands. Our study extends the newsvendor problem to a food truck problem. We used simulation to show that the food truck does not reduce to a newsvendor problem if demand depends on exogenous factors such temperature, time etc. We formulate the food truck problem as a multi-product multi-period linear program and found the dual for a single item. We use Discrete Event Simulation to solve the stochastic version of the dual and found the optimal order to maximize the food vendors profit.