This paper examines the robustness of lead time demand models for the continuous review (
r,
Q) inventory policy. A number of classic distributions, (e.g. normal, lognormal, gamma, Poisson and ...negative binomial) as well as distribution selection rules are examined under a wide variety of demand conditions. First, the models are compared to each other by assuming a known demand process and evaluating the errors associated with using a different model. Then, the models are examined using a large sample of simulated demand conditions. Approximation results of inventory performance measures—ready rate, expected number of backorders and on-hand inventory levels are reported. Results indicate that distribution selection rules have great potential for modeling the lead time demand.
►Robustness of lead time demand models is examined for the continuous review
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r
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Q
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inventory policy. ►Novel strategies for selecting the most appropriate lead time demand distribution are introduced. ►Analytical and simulation evaluation are performed to examine classic distributions and selection rules. ►We find that distribution selection rules have great potential for modeling the lead time demand.
Inventory record errors within a supply chain can lead to problems that cause low customer satisfaction and high operational costs. This paper presents a simulation model of a two-echelon inventory ...system consisting of a retailer, a distribution center, and a supplier that includes multiple item types and the use of cycle counting as the corrective action. An extensive set of cycle-counting configurations were examined while observing the trade-off between fill rates, accuracy, and system costs in order to investigate the best possible configuration of cycle counting for two set of experiments that examine high-demand–low-cost and low-demand–high-cost items. The results indicate that the correct application of cycle counting will increase record accuracy and provide significant amount of savings for the entire supply chain.
•We study an extension of the p-center problem that includes facility disruptions.•We consider both the pre- and post-disruption maximum transportation distances.•We present an algorithm for ...computing the Pareto-efficient set for these objectives.•Ignoring the post-disruption distance produced a vulnerable system.•Ignoring the pre-disruption distance was not nearly as consequential.
In this paper we consider a generalization of the p-center problem called the r-all-neighbor p-center problem (RANPCP). The objective of the RANPCP is to minimize the maximum distance from a demand point to its rth-closest located facility. The RANPCP is applicable to facility location with disruptions because it considers the maximum transportation distance after (r-1) facilities are disrupted. While this problem has been studied from a single-objective perspective, this paper studies two bi-objective versions. The main contributions of this paper are (1) algorithms for computing the Pareto-efficient sets for two pairs of objectives (closest distance vs rth-closest distance and cost vs. rth-closest distance) and (2) an empirical analysis that gives several useful insights into the RANPCP. Based on the empirical results, the RANPCP produces solutions that not only minimize vulnerability but also perform reasonably well when disruptions do not occur. In contrast, if disruptions are not considered when locating facilities, the consequence due to facility disruptions is much higher, on average, than if disruptions had been considered. Thus, our results show the importance of optimizing for vulnerability. Therefore, we recommend a bi-objective analysis.
•A classification framework for performance-based, multi-item, and multiechelon, inventory control systems is constructed.•Utilizing inventory control policy-driven-classification-criteria ...outperforms the known classification criteria in the literature.•The new classification approach proposes a dimension-reduction ranking solution, which is implementable as an ABC classification solution within spreadsheet programs.•The new classification approach is capable of searching for a near-optimal partitioning solution utilizing problem aggregation and hill-climbing algorithm.•The new classification approach provides a flexible trade-off analysis for determining the proper number of classes.•The new classification approach proposes a new managerial implication of classification for large-scale inventory-optimization size-reduction in a case for efficiently executing comprehensive what-if analyses.
Inventory classification is a managerial method utilized to group items that share predetermined characteristics, with the intent of assigning group-specific controls and monitoring mechanisms to each established item group. In this paper, we develop a performance-based inventory classification (PBIC) method that finds a grouping solution for a multi-item, multi-echelon inventory system controlled by continuous review. We argue that instead of grouping items based on similarities in unit cost, demand rate, or leadtime, the most effective strategy is to group items based on the information contained in their control policy values and their performance-related parameter values. We introduce a new policy-driven approach for establishing the classification criteria used to group items. We also adopt a ranking method to control the multi-dimensionality of multi-echelon systems in order to determine a one-dimension score. To group items, we improve the Pareto-based (ABC) solution by developing a search-based partitioning solution, utilizing a novel aggregation process. Our findings indicate that the PBIC method significantly outperforms alternative classification methods. Also, the empirical results show that there is a negligible gap between the performance of the PBIC and the optimal (complete enumeration) grouping solution. Finally, we discuss our work in the context of managerial implications highlighting the use of classification for problem aggregation and size reduction, when managers need to perform efficient, yet extensive, and dependable what-if analyses related to inventory management.
•The inventory segmentation and stocking policy problems are solved simultaneously.•The solution method utilizes an innovative greedy search algorithm for solving an MINLP problem.•The segmentation ...method considers large-scale multi-echelon inventory structures.•The solution method utilizes the problem size reduction and sensitivity analysis.•The performance difference between the proposed approach and optimal bound is less than 1%.
Existing research in the area of multi-echelon spare part models have not adequately addressed how to take advantage of the useful concept of inventory segmentation. To fill this gap, we define a new formulation of the multi-echelon repairable parts stocking model for the purpose of finding the best inventory grouping solution. For solving this mixed-integer and nonlinear formulation, we develop a heuristic optimization model based on a greedy approach, which uses the idea that the items having more similar stocking policies should tend to group together. Our findings show that the proposed model provides a near optimal solution, which significantly outperforms the alternative classification and clustering methods in the field. In addition, we highlight the managerial implication of segmentation for problem size reduction, when managers need to perform an extensive sensitivity analysis under time limitations.
Commercial shopping districts offer challenges for emergency planners to plan for the evacuation of short-notice emergency events. This paper illustrates a simulation analysis of the evacuation a ...large commercial shopping district, which focuses on street and parking lot vehicle traffic. Microscopic simulation is used to track the behaviors of vehicles evacuating from parking lots to safe zones. Evacuation scenarios investigate evacuation strategies by varying factors involving the occupancy rate of parking lots, inbound traffic control, and destination assignment policy. The performance of the evacuating vehicles is measured by an evacuation risk profile including the most problematic parking lots in terms of evacuation time. A trade-off analysis illustrates the effectiveness of the evacuation strategies in terms of costs, time, and risk. The simulation results indicate that an optimized destination assignment policy can alleviate traffic congestion and reduce total evacuation time.
Purpose
This paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes ...creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.
Design/methodology/approach
The approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.
Findings
Risk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.
Originality/value
To better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.
This paper examines the robustness of a standard model of multi-echelon inventory systems, specifically the models discussed in Axsäter (Oper. Res. 48(5) (2000) 686). A simulation model was developed ...to explore the model's ability to predict system performance for a two-echelon one-warehouse, multiple retailer system using (
R,
Q) inventory policies under conditions that violate the model's fundamental modeling assumptions. In particular, the impact of non-stationary demand on this stationary demand inventory model was examined. The model performs well at the low demand and large retailer order batch size situations, but our testing of the model indicated that care must be taken when applying this model to situations that violate its fundamental assumption. These results should help practitioners to better understand the assumptions of these models and to determine when or when not to apply these models in practice.
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