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A straightforward simulation project on the cost of inventory record inaccuracy.

by | Aug 27, 2022 | Python

 

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A straightforward simulation project on the cost of inventory record inaccuracy. At each replenishment, the inventory level is checked at the error is reset to zero, but inventory level errors accumulate in the meantime. Inventory errors can be people putting things back on the wrong supermarket shelf or, the cashier entering the wrong product code when selling. Formally the inventory error = actual physical inventory – reported inventory in ERP system.
*Here are some possible research questions: subsection{Research questions}
begin{enumerate} item What is the cost of inventory inaccuracy in a discrete-time system with a fixed order cost and linear holding and backorder costs? item How does the inventory cost calculated using inaccurate ERP stock levels differ from the true cost? item How does inventory inaccuracy bias affect costs? item If the (s,S) policy is maintained, do the optimal levels $s^*$ and $S^*$ change when the decision-maker knows inventory records are inaccurate?
end{enumerate}
*Need to explain the poisson distribution too, so you can do it in a similar way as you wanna accept. And you will get some idea how to write about these things. And when we set up an experiment design, decide what you’re going to check and if you’re going to do some optimization. Implement this in Python. You need to describe the Mean squared error model in the pieces and when do an experiment, so we set up a table with different combinations of. Please factors and random numbers. So make a table and you just run those experiments and maybe optimize S&S and record with different costs and maybe time between replenishment, maybe fill rate or service level. And you will get some insights after that. So which variable effects which result and how much money do we lose etc. And when you know that, we can write for conclusions of what’s interesting about this, what did we learn so forth. Answering your research questions.
Using poisson distribution with parameter Lambda. So this is called the Ss policy. More importantly the cost K per cycle is 64. And what you would need to do is. You can describe this with some basic equations and set up an experiment plan. You can do a lot of checks to see what’s the impact of different type of inventory errors. If we overestimate or underestimate our inventory randomly, how does that affect the costs and most importantly how to fix the gap between AvgCost in ERP system and the AvgImpliedCost?
Exhibits how the algorithm performs on test problems. To this end, chosen the example model in Veinott and Wagner with linear holding and backlogging costs, zero lead time and Poisson distribute done-period demands.The following parameters are common to all problems and identical to those used in Veinott and Wagner:
fixed set up cost K = 64; holding cost rate h = 1; penalty cost rate p = 9.
The excel attached is an example for reference. Literatures attached could be part of the foundation of this work.
For businesses, the stock has been the formulation of the number of important issues. If the portfolio stock, this could lead to customer complaints, then the loss of orders; if too much inventory of goods, will increase the financial pressure, so the company will have a higher risk of loss. Therefore, how to adapt the company planning the resources, then develop an appropriate service levels and safety stock, many companies are facing problems.
Consider a periodic-review Inventory assume that the managers use an Order-up-to base stock policy based on a critical fractile as the inventory strategy. If the random demand shocks have a temporary effect, and the potential demand trend will return to the long-term equilibrium state, choose to use the stationary inventory model to set the effective inventory Order-up-to level. Conversely, if the demand shock impact is permanent, the shock contain an element that represent a permanent departure from previous levels, and over time, the demand trend will gradually deviate from the average. Then in this state, have to choose an non-stationary inventory model to set the effective inventory level. These two different states of demand need to be different for the inventory level , so that it can respond to the demand during the lead time.
The demand process in each period is established through the autoregressive model. The parameter ? in the autoregressive model is the impact of the demand shock, and the Bayesian structure is used to update the parameter values of the autoregressive model with the new data. The setting of inventory level will be more accurate and can effectively reduce the cost of inventory.
In the past academic research, the main items for a single mathematical model is derived mainly inventory, while many items to consider safety stock inventory model can handle limited and items and not for finished goods for inventory improvement. According to our knowledge, the current global ERP system, no auxiliary functions to formulate the safety stock included in the standard ERP modules. Therefore, this study tried to existing product information in the ERP and sales records, data mining technology by the use of association rules to find out what kinds of products with sales of relevance, and further design algorithm to simultaneously adjust the service level of these products, and finally calculate the value of safety stock recommendations.
Applications in the system implementation, to prove the feasibility of the proposed method, the system will be the
data established by the Poisson Distribution for system simulation. From the simulation results can be found, such as the use of this research proposed scheme not only able to identify the products with high sales associate, and can also calculate the appropriate safety stock level, as the planners of reference.
In order to solve the problem, “rolling forecast” has been widely employed to cope with the problem, such that materials can be prepared in advance. This research started out with an investigation on the issue of rolling forecast applications in the industry. Subsequently, the computer simulation method was used to study the problem on hand.
Many scenarios were included in the simulation study, including forecast bias forecast error, out of stock penalty, and inventory replenishment strategies. In conclusion, found out that in different forecast error and forecast bias settings, the companies have to choose adaptable strategy and shorten the upstream lead time. Moreover, different industries have to adjust their replenishment strategies to improve their supply chain performance.

 

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