Reflective Report on Inventory Basics for Production Research

Question: Write about theReflective Report on Inventory Basics for Production Research. Answer: This paper covers my reflection concerning how I conducted my simulations while using replenishment of Adjustable Wrench and replenishment of Rock Salt to represent reality findings. Most importantly, I applied the inventory simulation model for a period of 12 weeks. I wanted to obtain order and reorder point as two critical strategies to minimize the total cost of holding inventory. The costs that mainly associate with inventories include costs such as ordering, holding, and even the lost opportunity (Cheong White, 2013). Irrespective of the size and the areas where a business operates, operational managers will also try to target and keep the above three types of costs at their minimal levels (Shahi Pulkki, 2015). Consequently, it is from this that a company can record high profit margins. The results presented in figure 1 below reveals that the findings from replenishment of Adjustable Wrench gives a smaller difference between the demand and the forecasted value. After several simulations, I obtained an average demand of 20.25. Of course, this was affected by what Coelho Laporte (2015) refers to as decision variables. They included reorder levels and order quantity. Furthermore, they also had two different probability elements in terms of ordering cost and reorder lead time respectively. Besides, even before I conducted a replenishment of Adjustable Wrench, I ensured that there was an opening inventory at the beginning of the week, reorder level, the quantity level, the cost to make an order, the cost per unit for folding an order, and the lost opportunity cost. Three types of costs that determined my strategy included those already mentioned above; the ordering, holding, and lost opportunity costs. Just as explained by Silva Gao (2013), is that there are always the costs that affect how businesses price their products and eventually, determine the consumer demand for the product. For example, two critical values include assumptions of future demand and past data. The two assisted me in forecasting the future reorder level. Besides, the fact that I had a fixed ordering cost irrespective of the amount of adjustable wrenches, the ordering cost remained constant at $6.30. I considered the economic order quantity as the only approach I could reduce the ordering cost. Therefore, I ordered a total of less than 80 units of adjustable wrenches every single week to reduce on ordering costs while at the same time, trying to increase on holding costs. This gave a holding cost of $0.04 per unit hence, translating to an economic order quantity of 80 uni ts. Figure 1: Simulation results on inventory Apart from the replenishment of Adjustable Wrench, I also discovered that after I had conducted my simulation from the replenishment of rock salt, the demand became 21 units with a standard deviation of more than 8. This was already higher than the previous approach. Hence, I now had to understand that indeed, two approached can yield different variations of demand. However, while understand the strategy to forecast on the customer future demand, while at the same time trying to reduce on inventory costs, one major problem that I experienced involved understanding the most optimal level that I could keep replenishing the stock. Of course, I was to also ensure that I do not incur the third type of costs- opportunity cost. This was evident from the time when I started running the simulation. For instance, I started to incur higher holding costs while taking caution to maintain the opportunity costs. I opted to apply a bell curve. According to Jalali Nieuwenhuyse (2015), the strategy shows the probability of stock within a given level. While at the same time, I ensured that I retain a lower economic ordering quantity, reorder level, and the safety stock. On the one hand, this strategy assisted me in retaining relatively lower ordering costs. While on the other hand, I increased the holding cost. The fact that the rock salt resulted in a relatively high standard deviation, this resulted in higher holding costs. Besides, I had to ensure that I keep on ordering because of shifts in customer demands. Therefore, a higher standard deviation led to a higher inventory costs. References Cheong, T., White, C. (2013). Inventory replenishment control under supply uncertainty. Annals of Operations Research, 208(1), 581-592. Coelho, L. C., Laporte, G. (2015). An optimised target-level inventory replenishment policy for vendor-managed inventory systems. International Journal of Production Research, 53(12), 3651-3660. Jalali, H., Nieuwenhuyse, I. V. (2015). Simulation optimization in inventory replenishment: a classification. IIE Transactions, 47(11), 1217-1235. Shahi, S., Pulkki, R. (2015). A simulation-based optimization approach to integrated inventory management of a sawlog supply chain with demand uncertainty. Canadian Journal of Forest Research, 45(10), 1313-1326. Silva, F., Gao, L. (2013). A Joint Replenishment Inventory-Location Model. Networks Spatial Economics, 13(1), 107-122