Inventory Decisions Research Articles

Joint inventory, promotion and preservation decisions for deteriorating items with maximum lifetime and stochastic demand under two-level partial trade credit

This study models a joint inventory, promotional effort and preservation decision-making problem for deteriorating items with maximum lifetime under a two-level partial trade credit and allowable shortages. This paper considers a supplier-retailer-customer supply chain model which allows: 1) for settling the cost of purchasing, the supplier offers a partial trade credit to the retailer and at the same time retailer offers a partial trade credit to the customer; 2) the upstream(supplier-retailer) credit period increases sales of the supplier and revenue of the retailer, the downstream (retailer-customer) credit period not only lifts demand but also the opportunity cost; 3) the deteriorating product not only deteriorate continuously, but also have maximum lifetime and to reduce the deterioration rate we use the preservation technology; 4) price and promotional effort are dependent on random demand; 5) shortages are considered. The objective is to find the optimal promotional effort, preservation technology investment, length of time for the inventory level reaches zero and replenishment cycle strategies while maximising the total profit per unit time. Numerical examples are included to illustrate the algorithmic procedure and the effect of key parameters is studied to analyse the behaviour of the model.

Capacitated Stochastic Lot-sizing and Production Planning Problem Under Demand Uncertainty

This paper proposes two multi-period, multi-item capacitated stochastic lot-sizing problems under demand uncertainty. We model uncertainty via a scenario tree. The first model considers production, inventory, backlogging, line status, and worker group assignment decisions, where inventory and backlogging decisions have wait-and-see structure. The second model converts line status and worker group assignment decisions to the wait-and-see structure. Also, the second model enables us to take corrective extra-ordering decisions using scenario-based wait-and-see decisions. Numerical results compare the optimality and CPU time performances of two models and solution approaches using a data set inspired by a real-life electronics company.

Joint Inventory and Transshipment Decisions with Consumer Behavioral Heterogeneity

Joint Inventory and Transshipment Decisions with Consumer Behavioral Heterogeneity

Blood supply chain: the impact of facility location and inventory allocation decisions for non-blood inventories - analytical case study

Blood supply chain: the impact of facility location and inventory allocation decisions for non-blood inventories - analytical case study

Inventory decision for products with deterioration and expiration dates for pollution-based supply chain model in fuzzy environments

The proposed study addresses a two-echelon sustainable supply chain (SC) model with a single-vendor and a single-buyer by considering the detrimental impacts of environmental pollution due to production. Moreover, an estimation function of pollution measure due to production is developed through a separate modelling. In the entire supply chain, we assume the deterioration rate increases with time and it also depends on the product’s expiration date. On the other hand, the demand for deteriorating items at the buyer’s end is assumed to be the dense fuzzy number because of learning effect. The model is developed by defining the exact profit functions for the vendor, the buyer and the entire supply chain and solved by classical method. These lead to the determination of individual optimal policies, as well as the optimal policy for the joint integrated supply chain. Fuzzifying the final objective function via dense fuzzy rule, we have employed extended ranking procedure for its defuzzification. A comparative study on numerical illustration of the proposed objective function under centralized and decentralized policies in both crisp and dense fuzzy environment has also been studied to validate the model. Finally graphical illustrations and sensitivity analysis have been made for its global justifications.

Optimization of an EPQ model in an imprecise environment with defuzzification by the centroid method under inflation

The awareness of making decisions in an imprecise environment has resulted in considering the inventory system under a fuzzy approach. The effects of uncertain demand have been finding increased application in many inventory systems. Un-certainty creates complicated situations for the manufacturer in making decisions. Markets have become more competitive as a result of technological advancements. The effect of inflation on the overall cost of the inventory system is useful in providing a tool for the analysis of inventory decisions. This study intended to estimate the effect of different fuzzy numbers on a manufacturer's annual joint expected total cost. The comparative study of this proposed model has been considered for two different fuzzy numbers with the defuzzification technique as the centroid method. The optimization technique has been used to minimize the producer’s joint expected total cost under the condition mentioned earlier, and the model is validated numerically.

Sustainable Pricing-Production-Workforce-Routing Problem for Perishable Products by Considering Demand Uncertainty; A Case Study from the Dairy Industry

AbstractThe production routing problem seeks to simultaneously optimize production, routing, and inventory decisions for the plant and the suppliers. In this article an integrated multi-objective sustainable pricing-production-workforce-routing problem is presented for perishable products. Total profit, workforce planning, and vehicle fuel consumption are considered as objective functions due to the importance of operational performance, social, and environmental concerns. The application of the proposed approach is investigated using real case data from a dairy product supply chain. Furthermore, a new solution approach, called Fuzzy Domination Self-Learning Non-Dominated Sorting Algorithm (FDSL-NSGA-II), is developed to solve the problem. The results show that the Pareto solutions of FDSL-NSGA-II outperform those of the classic NSGA-II. Moreover, the findings show that the proposed model can create a surpassing tradeoff between the various aspects of a supply chain, including production, distribution, and workforce planning. In addition, it concurrently optimizes the selling price and protects the environment from the negative impacts of greenhouse gas emissions (GHGs). A comprehensive analysis of the results reveals several managerial insights for decision makers in the logistics industry.

Optimization of the Location, Inventory and Routing of Capacity Vehicles with Interval Uncertainty

In today’s industrial world, manufacturing units are trying to reduce their costs by properly locating the warehouses they need as well as routing vehicles to transport manufactured goods to these warehouses. In fact, the inventory-routing location model is an integrated supply chain design model that simultaneously optimizes location, inventory, and routing decisions. The purpose of this paper is to provide an integrated model for locating warehouses, allocating stores to warehouses, and finding inventory at the end of the course and routing decisions such as determining the routes of vehicles starting from a distribution center opened to serve customers. Eventually return to the same distribution center; in such a way that the total system costs are minimized. The model is formulated as a mixed integer linear programming model (MILP) and a robust optimization approach has been used to optimize the problem under uncertainty conditions. To solve the model and demonstrate its feasibility, GAMS software and to compare the results of the software MATLAB is used.

A Model for Integrated Inventory and Assortment Planning

Integrating inventory and assortment planning decisions is a challenging task that requires comparing the value of demand expansion through broader choice for consumers with the value of higher in-stock availability. We develop a stockout-based substitution model for trading off these values in a setting with inventory replenishment, a feature missing in the literature. Using the closed form solution for the single-product case, we develop an accurate approximation for the multiproduct case. This approximated formulation allows us to optimize inventory decisions by solving a fractional integer program with a fixed point equation constraint. When products have equal margins, we solve the integer program exactly by bisection over a one-dimensional parameter. In contrast, when products have different margins, we propose a fractional relaxation that we can also solve by bisection and that results in near-optimal solutions. Overall, our approach provides solutions within 0.1% of the optimal policy and finds the optimal solution in 80% of the random instances we generate. This paper was accepted by David Simchi-Levi, optimization.

Construction Planning and Operation of Battery Swapping Stations for Electric Vehicles: A Literature Review

The popularity of electric vehicles has been limited by factors such as range, long charging times and fast power failure in winter. In order to overcome these challenges, battery swapping stations (BSS) have been constructed and greatly promoted in recent years. In this paper, the related literature on electric vehicle service is reviewed and the co-occurrence of keywords is analyzed using CiteSpace. The literature is classified according to clustering results and recurring themes, such as the location of BSS, inventory decisions, charging strategies and BSS assignment. In each topic, typical optimization models and algorithms proposed in previous studies are summarized. Then, this paper gives a case about the business model and revenue capacity calculation of BSS. Finally, it points out the future research direction of battery swapping stations for electric vehicles.

Optimization of resource flows across the whole supply chain. Application to a case study in the dairy industry

This paper deals with the problem of optimally planning the flows of raw materials and products across a linear multiproduct supply chain. Given a known series of products demands along a planning period, the problem consists of optimizing procurement, inventory, production, and distribution decisions for each time-period of the planning horizon. A decomposition procedure is developed to solve the optimization problem in reasonable computational times. The proposal first generates both a set of pickup routes for raw material collection and a set of delivery routes for product distribution through the Column Generation method. Then, these routes are feed into a Mixed-Integer Linear Program modelling the integrated problem. The scope of the optimization approach covers the entire supply chain from sources of raw materials to the end-customers, providing a useful computational tool for assessment of linear supply chains. The aim is to maximize the profit of the company that fabricates and distributes the products. The usefulness and effectiveness of the proposed solution strategy is demonstrated by solving an extensive set of realistic instances for a dairy industry.

Demand forecasting in supply chains: a review of aggregation and hierarchical approaches

Demand forecasts are the basis of most decisions in supply chain management. The granularity of these decisions lead to different forecast requirements. For example, inventory replenishment decisions require forecasts at the individual SKU level over lead time, whereas forecasts at higher levels, over longer horizons, are required for supply chain strategic decisions. The most accurate forecasts are not always obtained from data at the 'natural' level of aggregation. In some cases, forecast accuracy may be improved by aggregating data or forecasts at lower levels, or disaggregating data or forecasts at higher levels, or by combining forecasts at multiple levels of aggregation. Temporal and cross-sectional aggregation approaches are well established in the literature. More recently, it has been argued that these two approaches do not make the fullest use of data available at the different hierarchical levels of the supply chain. Therefore, consideration of forecasting hierarchies (over time and other dimensions), and combinations of forecasts across hierarchical levels, have been recommended. This paper provides a comprehensive review of research dealing with aggregation and hierarchical forecasting in supply chains, based on a systematic search. The review enables the identification of major research gaps and the presentation of an agenda for further research.

Which strategy is better for managing multi-product demand uncertainty: Inventory substitution or probabilistic selling?

Firms generally sell multiple products whose demands are uncertain. In order to manage product demand uncertainty, some firms use inventory substitution, while others use probabilistic selling. Which strategy should firms adopt is an intriguing issue. Addressing this issue, we consider a retailer that sells two substitutable products whose demands are uncertain. Based on the newsboy model, we analyze the optimal pricing and inventory decisions under the two strategies in three total market size situations, namely deterministic, discrete random, and general random. We derive closed-form optimal solutions for the former two situations and numerically find the optimal solution for the latter situation. We then compare the optimal profits under the two strategies in the three situations. We show that probabilistic selling always performs better than inventory substitution and brings more benefits to the retailer. These findings can explain that more and more firms adopt probabilistic selling in reality.

Analytics for multiperiod risk‐averse newsvendor under nonstationary demands

AbstractBased on the practice of a Chinese food manufacturer, this article develops a predictive and prescriptive analytics research on a multiperiod risk‐averse newsvendor problem with nonstationary demands. We start with a predictive analysis that aims to transform the nonstationary demand series into a predictable stationary one, and then develop inventory models for deriving prescriptive decisions based on transformed stationary series. When transforming the nonstationary demand series, we consider three commonly used methods—detrending (DET), differencing (DIT), and percentage change transformations (PCT)—which all effectively convert nonstationary demand series into stationary ones. These methods not only have desired simplicity and interpretability but also provide better predictive performance than the autoregressive integrated moving (ARIMA) process. Moreover, we develop an ensemble of the three transformation methods following the model averaging approach, which provides comparable predictions as the machine learning approaches. When developing prescriptive inventory decisions, we construct dynamic risk‐averse newsvendor models for the three methods having different structures, and find that the optimal order quantities under DET and DIT monotonically change as the newsvendor becomes more risk‐averse, but the optimal order quantity under PCT may not. Similarly, we also develop a heuristic ensemble of the inventory decisions under the three methods, which can lead to better profit performance. An extensive numerical simulation based on the manufacturer's historical data set shows that the heuristic ensemble inventory decision outperforms the sole decision generated by every transformation method and achieves an average performance improvement up to 92.25%. Several extensions are also considered to confirm the robustness of our findings.

Optimal Pricing, Advertising, Production, Inventory and Investing Policies in a Multi-Stage Sustainable Supply Chain

In this paper, the study of a sustainable production–inventory model with price and advertisement dependent on demand considering carbon emission reduction technology is investigated. The aim of this paper is to determine the optimal appropriate pricing, advertising, production, inventory, and capital investment decisions under various carbon emission policies to maximize the joint total profit of a multi-stage supply chain system. Various theoretical results and an algorithm are provided to verify and obtain the optimal solution of the problem. Further, the model is verified by numerical examples, and the robustness check of parameter variation is also analyzed. Finally, some management implications for decision makers are drawn from numerical examples. In summary, this study puts forward more realistic modeling hypothesis, which is beneficial to the academic research, and the research results can provide relevant decision makers with a model for managing a sustainable supply chain.

Integrated Transportation – Inventory Model for Outbound Logistics Optimization Hajariyah Agustina and Budi Santosa Departemen of Management Technology, Institut Teknologi Sepuluh Nopember (ITS), Surabaya e-mail: hajariyah.agustina@gmail.com of Cement

In the cement industry, supply chain management has an important role in determining competitiveness from inbound logistics to outbound logistics. PT X is the largest cement company in Indonesia with market share about 53% in 2019. The Company’s key strategy is supply chain optimization to achieve logistics and transportation cost efficiency and also prioritizing service level to meet customer needs. Outbound logistics optimization in PT X can be approached with the Integrated Transportation-Inventory (ITI) model. ITI is a model that integrates transportation and inventory decisions. In addition, ITI can also determine inventory levels, production quantities and backorder decisions. The application of ITI at PT X case study is categorized as multiple to multiple supply chain configurations, transshipment, multi-period, multi-product, allowing for back orders, and deterministic demand. The results of simulations of ITI models can be used as a basis sales planning and production. In this research, the ITI model solved with simplex solver method on Lingo 18, which has features to improve computational processes of a large scale cases. The development of the ITI model in optimizing outbound logistics at PT X provides efficiency and effectiveness of distribution strategy to meet customer demand. Simulation results show that optimization of outbound logistics using the ITI model is able to save costs by 1.86% (worth 117 billion) compared to existing conditions.

Design and Development of Intelligent Logistics Tracking System Based on Computer Algorithm

With the development of social economy, the logistics industry continues to flourish. This article attempts to build an intelligent logistics tracking system based on computer technology, focuses on the intelligent inventory decision support system, introduces the detection algorithm based on the integrity of goods packaging and the detection algorithm of goods behavior and posture based on the three-dimensional acceleration sensor, and conducts its work flow. The detailed design is finally carried out in the actual cabin environment to verify the experiment and evaluate the performance of the system.

Retailer’s credit and inventory decisions for imperfect quality and deteriorating items under two-level trade credit

The classical inventory model considers that products that are produced meet specified standards. However, some products in a lot may not meet defined standards but can be sold at a discount. The variation in the quality of products may arise due to randomness in production systems. The present research explores the effects of deterioration and trade credit policy on inventory control of imperfect quality items. In the current study, the authors develop a two-warehouse based inventory control model that studies deterioration in quality and two-level trade credit. The authors analytically find the lot size that optimizes total profit per cycle. Further, the differential based calculus method is used to determine the optimal solution. To further study the behaviors and real-life applications of the proposed inventory model, a numerical case problem has been solved and performed a comprehensive analysis. The result for presented examples indicates that combining trade credit policies with imperfect quality items in the presence of deterioration leads to savings in the supply chain. The suggested inventory control model is a generalized framework as it involves several existing inventory models.

Investigating the efficacy of inventory policy implementation in selected state-owned enterprises in the Gauteng province: A qualitative study

Background: The formulation, implementation and evaluation of inventory policies are vital for adequate management of inventory in organisations to ensure seamless flow of inventory and customer satisfaction. Inventory policies offer the required guidelines on how inventory should be managed in organisations to attain inventory management goals. However, it is unclear whether these guidelines are efficaciously implemented to enhance the inventory management performance of Eskom and Transnet in the Gauteng Province.Objective: This study sought to investigate the efficacy of inventory policy implementation at each of the selected SOEs in the Gauteng Province.Method: The study employed a qualitative research design with a sample of twenty-four inventory staff, of which fifteen were from SOE1 and nine from SOE2. The sampled population (inventory staff) were selected purposefully at each SOE in the Gauteng Province. Data were mainly gathered by means of semi-structured interviews and were thematically analysed.Results: The study generated four themes that reveal the extent of inventory policy implementation and its associated impact on inventory costs at each SOE. These themes included adherence to inventory policy, inventory policy violations, implementation extent of inventory policy, inventory growth rate and the associated increase in inventory carrying costs and lack of inventory planners’ planning skills.Conclusion: Inventory policies were not fully implemented at each SOE. Poor inventory purchasing decision and violation of inventory policies contributed to inventory growth rate and excess inventory carrying and its associated costs. This therefore negatively affects the inventory management performance of SOE 1 and SOE 2.

A three-objective stochastic location-inventory-routing model for agricultural waste-based biofuel supply chain

The utilization of agricultural wastes has visibly emerged as a promising policy towards enhancing the fragile global energy system characterized by a limited fossil fuel reserve. To this aim, this paper proposes a multi-objective model for the design of agricultural waste-based biofuel production with integrated formulation of location, inventory and routing decisions. Our model allows the decision makers to determine the number and location of residue gathering centers and biorefineries, the routes by which a heterogeneous fleet of vehicles collects the different agricultural waste, and the adequate material flow to meet the biofuel demand. Instead of assuming a fixed facility setting along the supply chain planning horizon, we construct a comprehensive mathematical model that includes: (1) the opening of a certain facility at any time period; (2) plant capacity expansion within the planning horizon; and (3) the facility closing not necessarily at last time period. The original model formulation is a three-objective stochastic Mixed-Integer Non-linear Programming. However, we propose a linearization strategy to efficiently convert our model into a MILP formulation. Since the proposed optimization model belongs to the class of NP-hard problems, a two-phase heuristic method is utilized to solve the formulated model. The constructive phase of our heuristic provides an initial solution that is further enhanced by a Simulated Annealing algorithm. We tested the proposed heuristic method for small, medium and large-scale problem instances. The computational results demonstrate that our heuristic is running-time efficient and highly competitive in terms of solution quality, compared to the exact method outcomes.