A factory cost simulator was developed for an injection molding factory. The production was characterized by high setup costs and times in the injection molding process. Another process characteristic was the centralized inspection area for final product inspection. The factory cost simulator was used for simulating the impact of internal transport frequency adjustments on total operative expenses. The factory layout was comprised of a separate production hall for injection molding lines, a central inspection area for final product inspection, and subsequently a central packaging, putaway, and relocation area in the warehouse building. At the other end of the warehouse, at the shipping area, there were stations for facilitating picking, labelling, foil wrapping, and dispatching through dock doors. For reference, see below example layout. The production environment was plan-based PUSH production, i.e. make to stock based on periodically forecasted net-demand.
Production output is collected in trays. Once a tray exceeds a target weight limit it is considered fully loaded and transferred to moveable internal transport trolleys located at the end of the production line. These trolleys circulate between central inspection and production, depending on the internal transport frequency. For reference, a similar trolley can be seen in below Youtube video.
A simulation study, applying the factory cost simulator specifically developed for this project, aimed at optimizing internal transport frequencies for overall cost reduction. The resulting cost-related transparency aided decision makers in change management, resulting in optimized internal transport lot-sizes and frequencies, and consequently reduced costs. The factory cost simulator was developed in Python using SimPy. You can find introductionary job shop simulation templates and related case studies in the SCDA shop here: https://www.supplychaindataanalytics.com/product-category/shop/
Longer internal transport cycles result in more scrap
Operators in the central inspection area inspect samples of parts in each tray, or at least within the first trays of a batch, and report any quality issues back to the production line. The longer the internal transport cycle, the longer the feedback delay between the inspection process and the production process. In worst case the production line produces scrap until the inspection team informs about detected quality issues. On average, this results in more scrap and higher associated scrap costs.
Shorter internal transport cycles result in higher internal transportation costs
The shorter the internal transport cycle, the more internal trolley movement and handling cycles take place. Trolley movement to and unloading at the inspection line is facilitated by operators, and the associated labor effort increases the more frequent internal transports take place. Thus, the shorter the internal transport cycle, the higher the associated labor cost for trolley movement and unloading.
Longer internal transport cycles increase buffer inventory carrying costs
The injection molding machines generally produce large batches that exceed the volume of a single transport trolley. If the internal transport frequency from production to inspection is increased, the trolleys are on average fuller and thus contain more inventory. This binds capital and increases calculated interest expenses on working capital. It may also increase the need for buffer storage surface area in the productive production area, or result in the need for larger and more expensive trolleys. This is why increases in internal transport frequency result in increased buffer inventory carrying costs.
Shorter internal transport cycles increase inspection and packaging costs
Trolleys transporting trays from the production lines are unloaded onto a sorting conveyor upstream of the inspection station. From there, trays are sorted based on their product number (material number), and are forwarded to inspection stations that inspect one inspection batch at the time. One inspection batch, in this case, refers to one set of trays with identical material number previously contained by the same trolley and then unloaded onto the sorting conveyor. Between each material number, i.e. batch, certain setups and changeovers are required at the inspection stations. As increased internal transport frequency leads to smaller inspection batch sizes, this increases the number of required changeovers. This ultimately increases inspection labor effort.
After the inspection process, tray content is emptied into a box. The box is then forwarded to packaging on a single centralized conveyor line. The packaging material depends on the product, i.e. material number, and the changeover from one packaging material to another is associated with defined changeover times and costs.
Shorter internal transport cycles increase putaway and relocation costs
Packaged boxes arrive on a single conveyor in the warehousing area. Here, they are prepared for putaway. If no half-full pallet with the same material number is available in the finished goods warehouse, all arriving boxes with that respective material number are stacked onto a new empty pallet and then either buffered in the putaway area (in case another batch is known to arrive later that day) or put away in the warehouse. If a half-full pallet with the same material number already exists in the warehouse it is picked and then delivered to the relocation area where the boxes have been buffered in the meanwhile. The pallet is then filled and put away. This is the so-called relocation process.
A shorter internal transport cycle results in smaller inspection batch sizes of the same material number, which translates to smaller batches of the same material number arriving at the putaway or relocation area. For each relocation process, a pallet must be retrieved by the forklift. This generates labor effort and forklift truck occupancy. Smaller batches result in more relocation processes, and thus increase labor effort. In the putaway area, smaller batches result in more half-full pallets, which in turn results in more relocation processes and thus ultimately higher labor effort for the relocation process.
Later, after storage and once a customer order has been received by the shipping team, a pallet with the respective material number is retrieved from the warehouse, the requested boxes are picked and prepared for shipping, and the resulting remaining half-full pallet is put away again in the warehouse.
Shorter internal transport cycles increase warehousing costs
As mentioned, shorter internal transport cycles, i.e. a higher internal transport frequency, result in more half-full pallets in putaway. This means that storage space is used less efficiently, unless material numbers are mixed. Mixed pallets would however increase picking and shipping effort. Less efficient storage usage, with a higher degree of half-full pure pallets, however translates into a higher demand for storage area. This increases warehousing costs.
Optimal internal transport frequency for overall cost efficiency
Below graph summarizes the various cost relationships and how they are influenced by the internal transport frequency. Using a SimPy simulation model the relevant factory processes were simulated with different internal transport frequencies. The resulting costs were analyzed, by category and as a whole.
As a result of the simulation study, transport frequency was increased, and additional incentives were designed to increase long-term production stability – targeting the increased scrap risk.
Concluding remarks and references
While the various departments may focus on their own objectives, the general manager, CFO, or CEO should focus on the overall cost optima – i.e. the overall efficient outcome. Using a simulation model, i.e. a digital copy of the real-world factory, allows managers to test adjustments to the real system and to analyze the outcome. All while avoiding the risks associated with real-world process intervention. Using the factory cost simulator management can confirm and communicate factory-wide cost implications resulting from process adjustments. This allows them to drive continuous improvement, while at the same time facilitating KPI-based change management.
If you are interested in applying simulation to your production or warehousing process, here are some additional tools, products, and articles that will take your further:
Data scientist focusing on simulation, optimization and modeling in R, SQL, VBA and Python
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