In this final chapter, conclusions of the findings in this study are provided together with recommendations for further research.
Conclusion
Based on the purpose of this study, was compared the behavior of the push, push-pull and pull supply chain system design with the first 5 weeks of disruption and the following recovery time. The tool used to analyze the systems is the AGI Distribution Software developed by Abraham Goldratt Institute.
The results were compared based on performance metrics as recovery inventory and system inventory, quantity of missed to clients, missed to market, return products and the net profit of the manufacturing company. With these results provided by simulations, the pull supply chain system design simulated in Scenario 4, on average presented shorter inventory and system inventory recovery time, less amount of products missed to market, to clients and retuned items compared with the rest of scenarios; also the net profit at the end of the year was higher in the pull system scenario.
The final data also shows that scenario push-pull system represented in Scenario 2 compared with pull system presents shorter system recovery time (Pull system an average of 33.10 weeks and push-pull system 26.93 weeks) and the net profit is slightly the same (Pull system an average of $52,919 and push-pull $50,081).
But with a detailed analysis the performance of Scenario 2 is not better than Scenario 4 because Scenario 2 until week 52 still have missed to market and clients which shows that the demand is not been covered even though the inventory level is the same as initially ( the products are storage in the wrong place).
41 Limitations
Despite all the efforts made in this research, limitations still exist in the overall research approach, in the method used, and in the data and tools utilized.
In this study the limitations presented were in the software utilized, since was not flexible to make changes in the parameters were needed to only analyze under a fixed parameters. Besides, changes to improve the system cannot be implemented, and then only could be applied one solution of the problem which was increase the production batch size.
Also the software under an oscillating market in the pull system doesn’t allow a disruption of 5 weeks, only until 4 weeks so it couldn’t analyze the recovery time if the market behavior changes because will not compare the recovery time after 4 and 5 weeks of disruption.
Extensions to Future Research
Carried more inventory involve cost of holding those pieces and also affect the lifetime of products if they are stock for longer. When the production batch size is increased to recover from an event in the simulations that size it continues throughout the entire analysis period, this will be planned to analyze fairly the performance of all scenarios. This research in order to get results under same condition was used the same production quantity until week 52, despite the system will recover o not the initial inventory level. But it can be seen in Scenarios 2, 3 and 4 after recovery time the inventory levels goes up to the initial level because the production is the same while where after disruption. A recommendation for future studies about recovery time is at the moment the initial level is recovered change the production batch size to the one utilized initially (when no disruption appear), with this it can should get better results of net profit and returned products.
Additionally for future research, it can be analyzed the performance of the scenarios while a disruption appears on a market with oscillated demand to see how it affects the parameters analyzed.
42
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