In the real – life manufacturing situations are inevitable. Defective items, warehouse capacity overused, run out in raw materials, lack in production capacity and inconstant demands are in some cases uncontrollable issues that can block the pipeline. It is impossible to assume an ideal or constant behavior in a “Real Supply Chain System”, when human, mechanical and logical resources are combined in a cycle which starts when a client places an order and finishes when the product is shipped to this client. Consequently, the model for an inventory is subject to constant and sometimes unpredictable changes than transform the model in a subjective decision.
Some companies have implemented in their facilities as an inventory system the Economic Order Quantity (EOQ). This model was first introduced several decades ago to describe the process of stocking inventory. It is still applied industry wide today to predict the optimal order quantity that minimizes overall inventory expenses using a simple mathematical modeling and analysis [1]. This model though, assume ideal conditions that in a real manufacturing process are not able to keep such as perfect quality items, deterministic and constant demand, zero lead times for each order and no shortages. All these ideal assumptions make the EOQ a subjective model that in some cases needs to be supported by an expert or at least somebody who has the enough experience to decide whether the number that it is showing fits with the real conditions that the company is facing at that point.
Companies need to realize that EOQ is a model based on non-real conditions that make this model a weak tool when manufacturing situations comes up or when it is used without the properly professional support. In a “Real Supply Chain” Companies cannot believe that EOQ is 100% reliable, it has a threshold that needs to be kept under control. Consequently, before implement any kind of Inventory System is necessary to analyze the real conditions at the facility and identify the mean and frequent production, supply and customers service problems. If a facility knows beforehand its weaknesses and constraints is easier to choose the proper model that could fit best in agreement with those conditions and be prepare for unexpected manufacturing situations. Furthermore, the facility can predict the situations where the Inventory System selected can fail or the situations where the system might not be accurate as should be.
[1] Singa Wang Chiu,Optimal replenishment policy for imperfect quality EMQ
model with rework and backlogging. 2007
3 comments:
This doesn't look like your original thoughts on the subject. Rather, you copied from some place else. It is good to share different resources with the audience only if you respect the source and cite it appropriately.
Dr, Keskin I am a Chemical Engineer I deal with this all the time when I worked in production processes. Specially when I was developing different products. I wrote this from my experience. I already cite the parts that I took from other source, sorry about that I forgot to do it, but the rest of it are my original thoughts.
I agree that EOQ is not in and of itself that useful in real world situations. It relies on assumptions that can usually not be met in the real world, as you stated. However, I think it is a good starting point for an inventory system. Like we went over in class, thinking about it like you have to order certain count packs, well if we have an eoq of 1500, and even tho we might not have to order in certain numbers of packages, it will be useful to know and we could order closer to that number than we would originally have. I think its useful as a basis for an inventory policy, not as a hard requirement. - Mark Brislin
Post a Comment