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Service Parts Logistics
- 5. IBM Global Chief Supply Chain Officer Study 2010
- 11. Model Overview System Considered
- 12. System Diagram Inventory Control Point for Service Parts Flow of Usable Spare Parts Flow of Unrepaired Spare Parts Regular Transport Emergency Transport Plant n Retailer 1 Retailer 2 Retailer m DC 1 DC 2 Plant 1 Retailer 3 DC i : : :
- 13. Level 0 Part Level 1 Part Level 2 Part Multi – Indenture Products System
- 14. Level 0 Part Level 1 Part Level 2 Part Level 0 Part Level 1 Part Level 2 Part Level 0 Part Level 1 Part Level 2 Part Multi – Echelon Inventory Equation Development Retailer DC Plant
- 15. Solution Methodology Excel-based Genetic Algorithm
- 16. Excel Template
- 17. Coding the Mathematical Model
- 18. VBA for Genetic Algorithm
- 19. Genetic Algorithm Parents Selection
- 22. Analysing Genetic Algorithm Behavior Over Time
- 23. Results
- 30. Results of Risk Runs Risk Scenario 1 st best Strategy 2 nd best Strategy 3 rd best strategy High Demand Variability Increased Responsiveness Increased Inventory in all Sites Increased Inventory at Plant and Retailer Inventory Limit Increased Responsiveness Increased Inventory at the DC Increased Inventory at the Plant or at both Plant and DC Extreme Demand Values Increased Responsiveness Increased Inventory in all Sites Increased Inventory at DC and Plant Inventory Cost Increased Responsiveness Increased Inventory in all Sites Increased Inventory at Plant and Retailer Facility Cost Increased Responsiveness Increased Inventory in all Sites Increased Inventory at Plant and Retailer Emergency Shipment Cost Increased Responsiveness Increased Inventory in all Sites Increased Inventory at Plant and Retailer
- 31. Total Average Cost Across Scenarios
Editor's Notes
- 400 Billion Dollars-market value worldwide of spare parts 178 Billion Dollars per year- after-sales service for automobile industry 40 Billion Dollars per year-global cost of spare parts for telecommunications 80%-Inventory Space taken up by spare parts in computer industry $6-8 Billion per year-Revenue gained by four US industries from spare parts and after sales service 39%-Percentage of total profit for European car company for spare parts only DAVID
- Spare parts differ from typical products in a number of ways. These include large variety, slow usage, high criticality and the obsolescence factor. Because of these characteristics, spare parts management is subjected to a lot of challenges, such as high demand uncertainty, increase in prices for individual parts, higher service requirements and potential for financial loss due to stock-out. JULI
- Aside from considering spare parts, we also considered short lifecycle products. The challenges in managing short lifecycle products are their components and spare parts have long lead times and have uncertainty in their prices, and they are subjected to unpredictable market response. These challenges make them more susceptible to a variety of supply chain risk, such as inventory, procurement, capacity, forecast, disruptions, and competitive risk. JULI
- Our review of over 150 articles in supply chain management, spare parts and risk literature revealed two major themes. First, we have found out that a number of risk minimization models do not incorporate multi-echelon inventory equations. One major reason is because the inclusion of multi-echelon equations make risk minimization models more difficult to solve with multiple scenarios and with nonlinearities. Also, we have found out that a number of spare parts studies have not attempted to study the effect of risks and uncertainty on spare parts supply systems. A possible reason is that a number of spare parts studies focus on modelling and solving, and delve less into the analysis of the system behavior to uncertainty. ALVIN
- ALVIN
- -Due to the simplicity of the model and ease of extending it into multiple time periods, echelons and indentures using the methodology that we documented, then it can readily be used by other potential researchers and modified as necessary. -The development of the genetic algorithm detaches ourselves from standards commercial solvers, therefore the model is not subjected to their limitations. In modelling an OR problem, it should be noted that solving process should be emphasized, and through the Excel program, non-linearities are tackled easily through the capability of Excel. DAVID
- Universal and General Applicability of Principles and Recommendations Obtained-can be applied to any similar system, as long as similar structure, guidelines will apply Global Application Applications are Boundless DAVID
- The first step in our research involved selecting a topic. This was followed by an extensive literature review that covered 200 articles on supply chain management, spare parts and risk analysis. From the literature review, the research gap was identified and defined. A model was formulated to resolve the research gap, and we used a number of previous spare parts researches as basis for our modeling. The output of the modeling process is a mixed integer nonlinear programming problem. The nonlinearities present in the model made the use of commercially available software difficult for solving the model. Hence, to be able to craft out a solution from the model, the group resorted to designing a genetic algorithm and programmed it in Excel VBA. Afterwards, in order to validate the model, 3 response surface methodology experiments were conducted. The first one focused on analyzing the spare parts system behavior, whereas the second and third experiments dealt with understanding the behavior of the model to changes in demand and cost parameters, and sought to find factor settings that would ensure 1) low total cost and 2) low variability around the total cost as much as possible. On top of the validation experiments, risks scenario analysis were also conducted to determine the best supply chain strategies to adopt to hedge against particular supply chain risks. Finally, the output of the research is 1) a set of general guidelines for spare parts supply chain design and 2) the final list of risk mitigation strategies that can be adopted by the supply system to hedge against risks.
- There are three echelons in the model, but the model can be extended into four echelons and above. The flow of spare parts are represented by the arrows. There is a forward flow of new and refurbished spare parts from the plant to the distribution center to the retailers. Also, there is a backward flow of broken spare parts from the retailer to the plants. All of the facilities in the supply chain are capable of holding inventory. The inventory control policy adopted by each of the facilities is the (s, s-1) inventory control policy, which is the most common policy used in spare parts management. Finally, different modes of transportation were also considered in the mode but these modes were only differentiated by their transportation time
- Multi-indenture items were considered in the model. In particular, the spare parts modeled are 3-level multi-indenture parts composed of 1 Level 0 spare part assembly and any number of Level 1 and Level 2 component modules. Examples of 3-level multi-indenture parts are shown in the figure below Product structure is important because it influences the formulation of demand equations. Demand equations should be able to capture the commonality of lower-indenture parts across different higher-indenture items.
- The system considered is also a multi-echelon inventory system, in other words, the model is able to capture the inventory dependencies across different echelons. From the diagram, it is also clear that the inventory dependencies captured by the model is not only across echelons, as what is common among many multi-echelon papers, but also across different part indentures
- By concentrating the repairs at the retailers, the demand for spare parts are spread across all the retailers so service level requirements can be Lower repair times are desirable because these would cause fewer delays at the repair sites and thus minimize backorders. This may be implemented by the company by training the workers, which will make them more skilled which would make them more efficient and faster in repairing items. The company cannot do this without market information or demand information. The suggested course of action is after product launch, the company should quickly identify from initial repairs which parts are critical or not. Once done so, they can then begin stocking high criticality parts at the lower echelons of the supply chain.
- When facing demand variability, the only difference is to reduce delays at ALL facilities of the supply chain, even those which receive little demand.
- When facing cost variability, on the other hand, it is the same with the general rules which suggest that delays must be minimized at the facilities with high demand.
- Our results and recommendations are consistent with literature such as the following.
- The model was subjected to various risk scenarios and different risk mitigation strategies were used to counter them. It can be seen that increased responsiveness or an agile supply chain is the best strategy to use for all scenarios. The second best strategy is an increased inventory level at all sites, except for the inventory limit risk scenario where the second best strategy is increased inventory at the depot.
- As it can be seen from the graph, increased responsiveness is definitely the best strategy since it gives an average cost of around 40,000. Second best strategy of inventory at all sites is also clearly seen at an average cost of 150000. The worst strategy is pooled demand, with the highest average cost of around 260000.
- Short lifecycle products are really best served by increased responsiveness or an agile supply chain, according to the findings of other studies, such as of Cohen (2006). This is because it dramatically reduces penalty and transportation cost, due to the quicker response time. A company can implement an agile supply chain by outsourcing transportation and distribution to a third-party logistics provider that emphasizes speed. They can then use express deliveries and route optimization to have a lower transportation time. The company can also conduct training to their staff to repair the products faster and thus minimize delays.
- These are the recommendations of the study: -A C++program can be designed to solve the model faster, since it takes 4-5 hours to run the genetic algorithm on Excel. -A full stochastic programming analysis can be done since due to time constraint, only a limited number of scenarios were run. -An alternative solution methodology can also be used to solve, since only the genetic algorithm metaheuristic was applied. Other metaheuristics can also be used like Tabu search and ant colony optimization. -Mathematical functions can be designed to better approximate the pipeline inventory, since here it was only based on the model of Sherbrooke. Through a better design, the equations and formulas for pipeline can be more accurate.