Modeling Economic Consequences of Supply Chain Disruptions (original) (raw)
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Volume 4: 19th Design for Manufacturing and the Life Cycle Conference; 8th International Conference on Micro- and Nanosystems, 2014
Due to the nature of the manufacturing and support activities associated with long life cycle products, the parts that products required need to be dependably and consistently available. However, the parts that comprise long lifetime products are susceptible to a variety of supply chain disruptions. In order to minimize the impact of these unavoidable disruptions to production, manufacturers can implement proactive mitigation strategies. Two mitigation strategies in particular have been proven to decrease the penalty costs associated with disruptions: second sourcing and buffering. Second sourcing involves selecting two distinct suppliers from which to purchase parts over the life of the part's use within a product or organization. Second sourcing reduces the probability of part unavailability (and its associated penalties), but at the expense of qualification and support costs for multiple suppliers. An alternative disruption mitigation strategy is buffering (also referred to as hoarding). Buffering involves stocking enough parts in inventory to satisfy the forecasted part demand (for both manufacturing and maintenance requirements) for a fixed future time period so as to offset the impact of disruptions. Careful selection of the mitigation strategy (second sourcing, buffering, or a combination of the two) is key, as it can dramatically impact a part's total cost of ownership. This paper studies the effectiveness of traditional analytical models compared to a simulation-based approach for the selection of an optimal disruption mitigation strategy. A verification case study was performed to check the accuracy and applicability of the simulation-based model. The case study results show that the simulation model is capable of replicating results from operations research models, and overcomes significant scenario restrictions that limit the usefulness of analytical models as decision-making tools. Four assumptions, in particular, severely limit the realism of most analytical models but do not constrain the simulationbased model. These limiting assumptions are: 1) no fixed costs associated with part orders, 2) infinite-horizon, 3) perfectly reliable backup supplier, and 4) disruptions lasting full ordering periods (as opposed to fractional periods).
IOP Conference Series: Materials Science and Engineering, 2019
In this paper, a system dynamics approach is used to simulate a three stages supply chain system experiencing supply disruption. The supply chain system consists of single supplier, manufacturer and retailer. The model is developed suggesting backlogged and inventory as the primary performance measure. The model is tested under three conditions, which are normal condition, disruption condition and disruption with a mitigation strategy. The main findings from the study are lead time changes on the entire system directly impact the inventory of the entire stages in the system. Furthermore, the disruption occurence produces an adverse effect on supply chain performance for a period longer than the actual period of the disruption and likewise, influence system performance. The study further identifies that by incorporating safety stock as a mitigation strategy in the event of a supply disruption, will significantly reduce the disruption impact. The proposed simulation model and experime...
Most of the literature in supply chain management either considers only a part of the supply chain or the total supply chain in some simplified manner. This thesis considers most of the strategic and tactical decisions faced by a real world supply chain consisting of all the four stages found in a typical industry, namely suppliers, manufacturing plants, warehouses and distribution centres. The modeling decisions included the supplier selection process integrated into the supply chain network of the Nigerian Bottling Company with the production amounts, inventory levels, stock-outs, product allocations and facility locations as well as transportation types. The thesis presented an integrated mathematical programming model for supply chain management. It is a single objective, multi-period, deterministic, centralized supply chain model. The objective of the model is to minimize the total cost involved in running the supply chain. This results in a mixed integer linear programme. The model is tested with a set of data and the optimal results are obtained. Risk analysis, such as supplier price increase, unavailability of a type of transportation, is performed over the optimized model and the effects of uncertainty are studied. Also, a renewed optimized setting was achieved with the addition of extra facilities into the supply chain network which further the total reduced cost by 63.6% from N 8,006, 901, 000 to N 2, 914, 400,000.
Mitigating disruptions in a multi-echelon supply chain using adaptive ordering
Supply chains often experience significant economic losses from disruptions such as facility breakdowns, transportation mishaps, natural calamities, and intentional attacks. To help respond and recover from a disruption, we investigate adjustments in order activity across four echelons including assembly. Simulation experiments reveal that the impact of a disruption depends on its location, with costlier and longer lasting impacts occurring from disruptions at echelons close to ultimate consumption. Cost functions based on system inventory and service can be quite ill-behaved in these complex problem settings. Expediting, an adaptive ordering approach often used to mitigate disruptions, can trigger unintended bullwhip effects, and hurt rather than help overall performance. As an alternative to expediting interventions, dynamic order-up-to policies show promise as an adaptive mitigation tool. We also find benefits in the dynamic policies from incorporating a metaheuristic parameter search over multiple echelons, yielding significantly better solution quality than embedded unimodal search.
Supply Chain Disruptions Preparedness Measures Using a Dynamic Model
Supply Chain Risk Management
Supply chain risk management has recently seen extensive research efforts, but questions such as "How should a firm plan for each type of disruption?" and "What are the strategies and the total cost incurred by the firm if a disruption occurs?" continue to deserve attention. This chapter analyzes different disruption cases by considering the impacts of disruptions at a supplier, a firm's warehouse, and at the firm's production facility. The firm can prepare for each type of disruption by buying from an alternate supplier, holding more inventory, or holding inventory at a different warehouse. The Wagner-Whitin model is used to solve the optimal ordering strategy for each type of disruption. Since the type of disruption is uncertain, we assign probabilities for each disruption and use the Wagner-Whitin model to find the order policy that minimizes the firm's expected cost.
Industrial Engineering and Management, 2019
However, there is a lot of capital and plenty of manpower in the auto spare part industry, the enterprises and supply chains of this industry do not perform well in our country. This research models a three-level supply chain with multiple manufacturers, distribution centers and retailers, to minimize the total cost by taking into account various disruptions. The database of two active car spare parts companies for five strategic products in one year has been used. Then, the mathematical model is analyzed by considering disruptions based on three different sales policies: back orders, lost sales and outsourcing. Besides, to evaluate the performance of the model some numerical examples are used and analyzed to determine that algorithm works. Model solved efficiently by MATLAB software. The results show that the proposed algorithm of this research can neutralize the effect of the disruptions and cause a significant reduction in total cost of the system. The model is useful for helping...
Optimization to manage supply chain disruptions using the NSGA-II
… and Applications of Fuzzy Logic and …, 2007
Disruption on a supply chain provokes lost that should be minimized looking for alternative suppliers. This solution involves a strategy to manage the impact of the disruption and thus to recuperate the supply chain. Difficulty of the management is the diversity of involved factors such that turns complex to provide or choice a solution among the possible ones. Depending on the objective(s) to optimize are the strategy to follow and the solution to choice. In this work the Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization NSGA-II is used as the strategy to generate and optimize (minimize) solutions (lost) in front of a disruption. The included objectives are cost, risk and the place of facilities supporting the supply chain recuperation. These objectives are combined to generate possible solutions and to choice one that provides a proposal to minimize the disruption impact on a delimited period of time. Advantage of NSGA-II utilization is the provision of a practical formal and computational tool to analyze different scenarios without simplifies the complexity of a standard real supply chain. The illustrative exercise presents recovery scenarios for a crude oil refinery supply chain.
International Journal of Management Science and Engineering Management, 2018
Effective response and recovery from disruptions are vital to achieving the supply chain objectives. This study aims to formulate a quantitative model for mitigating disruptions in a supply chain. An inventory model has been developed for a manufacturer with one supplier and one retailer by considering random capacity of the supplier and random availability of both the supplier and the retailer assuming zero delivery lead time. Backorders are allowed and it has two parts-unit dependent and both unit and time-dependent. This study suggests an optimal order quantity and a reordering point so that the average cost per cycle gets minimized. A genetic algorithm is used to solve the proposed inventory model. The applicability of the proposed model has been tested using a numerical example. Finally, sensitivity analysis is performed to examine the robustness of the model.
Supply chain disruption and risk management
Transportation Research Part B: Methodological, 2011
Supply chain disruption and risk management This special issue was created to recognize the very substantial overlap between transportation research and supply chain research. In this special issue, we have compiled an assortment of papers that illustrate a wide range of modeling styles associated with determining the performance of supply chains and the risks associated with them. The papers are, for the most part, theoretical and do not consider real data sets.