BST333: Simulation Experiment On An Inventory Control System With Re-manufacturing - Logistics Modelling Assignment

Logistics Modelling Assignment:

In the classic inventory model presented in the lectures, it is assumed that all products consumed by the end customers will be discarded. However, in recent years, with the growing the concern of the environmental impact of discarded products and the potential economic benefits, an increasing number of companies have adopted a recycling and/or remanufacturing policy, where used products are returned to the manufacturing facility, where they are prefabricated or remanufactured and available for selling again. This is seen in many industries, such as automotive (Toffel, 2003), aerospace (Hashemi et al., 2014), furniture (Krystofik et al., 2017) and equipment manufacturing (Kerr and Ryan, 2001). In academia, there is also a growing interest of investigating the economic, social and environmental impact of remanufacturing using a modelling approach, in order to achieve higher public value, as defined by Brewer (2013). Following this trend, in this assignment, you are expected to • Using the base inventory model given in §2, systematically investigate the relationship between remanufacturing and inventory system performance; • Conceptually and mathematically extend the base model, to incorporate more characteristics related to remanufacturing; • Through observation of output graphs and numerical assessments, compare and contrast the difference in system performance, and analyse the effect of these extensions.

The base model: 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 and remanufacturing lead-times are assumed to be constant and equal to 4 periods and 8 periods respectively. 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 ?           Coefficient of inventory feedback ?           Yield rate ss          Safety stock Lp         Production lead-time, Lp = 4 Lr          Return and remanufacturing lead-time, Lr = 8 It is assumed that the yield rate, the proportion of sold products that are returned and remanufactured, is constant. That is, for any period t, the number of products that can be returned and remanufactured is ?Dt. Return and remanufacturing takes Lr periods from the initial customer purchase. After that these products are replenished in the inventory and are available to sell. 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 + ? [ss + (Lp ? 1)Dˆt ? It ? Wt] The balance equation of WIP is not affected by remanufacturing; in the inventory balance equation, there is an extra summand term that reflects the remanufactured products. Wt = Wt?1 + Ot?1 ? Ot?Lp It = It?1 + Ot?Lp ? Dt + ?Dt?Lr

Simulation analysis: Based on the mathematical base model above (and the extended model later), establish a simulation model with a spreadsheet to investigate the following problem: (Q1) How do the feedback coefficient ? and the remanufacturing rate ? affect the bullwhip ratio ?2O/?2 D and the NSAmp ratio ? 2 I /?2D? The demand follows a normal distribution with the expectation of 100 and standard deviation of 5.

Model evaluation and extension: It is essential to realize that any model is a simplification of reality. More complex models are generally better at representing the real case. On the other hand, good models achieve a better balance between model complexity and tractability. For the model presented in §2, think about the following: • (Q2) What assumptions are made in this model with regard to remanufacturing? • (Q3) Are these assumptions valid? How do you judge validity? • (Q4) How do you extend the model to relax the assumption(s) which is/are deemed invalid?

Comparative analysis Conduct the simulation analysis again based on the extended model. Compare the simulation results and answer the following question: • (Q5) Does the removal of assumptions change the answer to Q1? If so, to what degree? Compare the three modelling techniques that you have used in completing this assignment, and answer the following question: • (Q6) What relative benefits can you derive from the model in the forms of conceptual, mathematical and simulation?

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 of any assumptions in the base model should be given and evidence provided by the model’s verification and validation (10%). Reliable sources of information include (but are not limited to) published literature, including previous simulation studies and case studies. The 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 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.