1 Background
In the classic inventory model presented in Lecture 7, it is assumed that the quality of all products is satisfactory, so that they can be sold to the market eventually. In practice however, product quality is an issue of not only economical, but also social, importance. For instance, during the launch period of PS4 console, Sony discovered that this new product has a 0.4% defect rate (LNS Research, 2013). In 2014, Toyota recalled 380,000 automobiles worldwide due to various defects and malfunctions (Fortune, 2014).
Despite the large impact of production defects on companies business performance, little is known about how it affects production and inventory dynamics. In this assignment, you are expected to
- Based on the linear inventory model, systematically investigate the relationship be- tween the level of defect and inventory system performance;
- Conceptually and mathematically extend the linear model, to incorporate more defect- related issues/characteristics;
- Through observation of output graphs and numerical assessments, compare and con- trast the difference in system performance, and analyse the effect of these extensions.
2 Linear inventory model with constant defect rate
The model is established for a single-product, single-echelon, periodic-review inventory system. For simplicity, all linear assumptions (free returns, backorder, etc.) are made in this model. Production lead-time is assumed to be constant and equal to 4 periods.
The following notations should be used in this assignment:
Dt Demand (consumption) in period t
Dˆt Demand forecast for period t
Ot Order quantity in period t
It Inventory level at the end of period t
Wt Work-in-progress (WIP) level at the end of period t β Coefficient of inventory feedback
λ Defect rate
ss Safety stock
L Production lead-time, L = 4
It is assumed that the defect rate in a single batch is constant. That is, for any period t, the number of defected products is λOt. These products are to be discarded and should not be used to replenish the inventory.
The company uses naive forecasting to generate demand forecast, which is
Dˆt = Dt-1
A proportional order-up-to policy is used to determine the order quantity:
Ot = Dˆt + β(ss + 3Dˆt - It - Wt)
The inspection of defected product can only be made when the production is complete. So the balance equation of WIP is not affected by defects; only inventory balance equation is.
Wt = Wt-1 + Ot-1 - Ot-4
It = It-1 + (1 - λ)Ot-4 - Dt
3 Simulation analysis
Based on the mathematical model above (and the extended model later), establish a simu- lation model with spreadsheet to investigate the following problem:
- (Q1) How do the feedback coefficient β and the defect rate λ affect the bullwhip ratio σO2/σD2 and the NSAmp ratio σI2 /σD2?
The demand follows a normal distribution with the expectation of 100 and standard deviation of 5.
4 Model evaluation and extension
It is essential to realize that any model is faulty from reality. More complex models are generally better at representing the real case. On the other hand, good models achieve better balance between model complexity and tractability.
For the model presented in §2, think about the following:
- What assumptions are made in this model with regard to defects?
- Are these assumptions valid? How do you judge validity?
- How do you extend the model to relax the assumption(s) which is/are deemed invalid?
5 Comparative analysis
Conduct the simulation analysis again based on the extended model. Compare the simulation results and answer the following question:
- Does the removal of assumptions change the answer to Q1? If so, to what degree?
6 Report
The following parts should be included in the final report.
1. Simulation analysis
You should provide evidence (e.g. sample screen-shots) of the simulation model and the procedure of analysis. Results in numerical and graphical forms (e.g. tables and figures) will greatly enhance the readability. You should also provide a summary to answer the question in $3.
2. Model evaluation and extension
Proper justification should be given when evaluating the assumptions in the basic model (10%). Reliable sources of information include (but are not limited to) published literature and case studies. Extended model needs to be presented in both conceptual (10%) and mathematical (10%) forms. New variables and parameters need to be properly defined before use.
3. Comparative analysis
Here you should carefully design the method of analysis and presentation of results. Again, numerical results and graphics (if designed properly) with the assistance of textual explanation is helpful.