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This paper presents a profit optimization model for substitute products in a competitive, time-sensitive market with scarcity and shifting user preferences. The model maximizes profit, considering production costs and inventory maintenance. It uses a discrete choice model to represent demand, sensitivity to price, availability, and changing preferences. A two-phase PSO-type metaheuristic solution tackles the nonlinear, recursive model, efficiently managing inventories and evolving consumer preferences. The model integrates production decisions, inventories, and sales prices, considering scarcity conditions and user preferences. It uses a multinomial logit for the consumers' demand function with soft exogenous constraints, which influence utility and change consumption preferences and choices. This research offers a tool for companies to manage stock, production, and pricing in a context where goods are substitutes, providing a new perspective on business strategy. [ABSTRACT FROM AUTHOR]

The primary objective of this article is to develop a mathematical model and determine the optimal policies of an inventory system involving power demand and controlled deterioration through preservation technology. This model comes in handy in a power demand-oriented inventory system with demand high at the end of the period. The model incorporates backlogged shortages and linear holding cost. The triangular neutrosophic numbers (TNN's) are used for a nuanced representation of uncertain and imprecise inventory-related expenses. An efficient algorithm is constructed to minimize the total cost, and obtain optimal positive inventory time, optimum cycle time and minimum preservation technology investment. Few numerical examples are used to illustrate and validate the model. The comparative study conducted between models with and without preservation technology investment reveals a significant reduction in total inventory costs facilitated by the preservation facility. Also, the numerical results obtained in crisp and neutrosophic environment are compared. Specific previously obtained results are discussed to illustrate the theoretical findings. Sensitivity analysis of the model provides managerial insights replicating reality. [ABSTRACT FROM AUTHOR]

In this paper, we develop a two-level supply chain model with low carbon emissions for defective deteriorating items under learning in fuzzy environment by using the double inspection process. Carbon emissions are a major issue for the environment and human life when they come from many sources like different kinds of factories, firms, and industries. The burning of diesel and petrol during the supply of items through transportation is also responsible for carbon emissions. When any company, firm, or industry supplies their items through a supply chain by using of transportation in the regular mode, then a lot of carbon units are emitted from the burning of petrol and diesel, etc., which affects the supply chain. Carbon emissions can be controlled by using different kinds of policies issued by the government of a country, and lots of companies have implemented these policies to control carbon emissions. When a seller delivers a demanded lot size to the buyer, as per demand, and the lot size has some defective items, as per consideration, the demand rate is uncertain in nature. The buyer inspects the received whole lot and divides it into two categories of defective and no defective deteriorating items, as well as immediately selling at different price. The fuzzy concept nullifies the uncertain nature of the demand rate. This paper covers two models, assuming two conditions of quality screening under learning in fuzzy environment: (i) the buyer shows the quality screening and (ii) the quality inspection becomes the seller's responsibility. The carbon footprint from the transporting and warehousing the deteriorating items is also assumed. The aim of this study is to minimize the whole inventory cost for supply chains with respect to lot size and the number of orders per production cycle. Jointly optimizing the delivery lot size and number of orders per production cycle will minimize the whole fuzzy inventory cost for the supply chain and also reduce the carbon emissions. We take two numerical approaches with authentic data (from the literature reviews) for the justification of the proposed model 1 and model 2. Sensitivity observations, managerial insights, applications of these proposed models, and future scope are also included in this paper, which is more beneficial for firms, the industrial sector, and especially for online markets. The impact of the most effective parameters, like learning effect, fuzzy parameter, carbon emissions parameter, and inventory cost are shown in this study and had a positive effect on the total inventory cost for the supply chain. [ABSTRACT FROM AUTHOR]

In today's environment, organizations utilise Machine Learning based-models to keep stocks depending on the demand for a particular type of product. This article develops an inventory model considering the imperfect deteriorating product in a fuzzy environment. The shortages are allowed and partially backlogged. Since the deterioration rate and defective percentage in quantity in the received lot may not be predicted precisely because it depends on many uncertain situations, therefore both are considered fuzzy variables. This study aims to determine the optimal ordering quantity and replenishment period to optimize (minimize) the average overall cost with carbon emission cost. The defuzzification process is done using the sign distance approach method. A methodology based on Machine Learning is used to demand forecast seasonally. Some numerical examples are taken to validate the proposed mathematical model. The findings demonstrate the generation of direct month-wise predicted demand for deteriorating products based on the input of the month value, enabling organizations to optimize their inventory management according to forecasted demand. A comparative analysis is conducted between fixed and month-wise forecasted demand by highlighting the advantages of machine learning-based forecasting approaches. Sensitivity analysis performs to examine the behaviour of several parameters on an optimal solution and provides some managerial insights. [ABSTRACT FROM AUTHOR]

In this article, we present a novel methodology for inventory management in the pharmaceutical industry, considering the nature of its supply chain. Traditional inventory models often fail to capture the particularities of the pharmaceutical sector, characterized by limited storage space, product degradation, and trade credits. To address these particularities, using fuzzy logic, we propose models that are adaptable to real-world scenarios. The proposed models are designed to reduce total costs for both vendors and clients, a gap not explored in the existing literature. Our methodology employs pentagonal fuzzy number (PFN) arithmetic and Kuhn–Tucker optimization. Additionally, the integration of the naive Bayes (NB) classifier and the use of the Weka artificial intelligence suite increase the effectiveness of our model in complex decision-making environments. A key finding is the high classification accuracy of the model, with the NB classifier correctly categorizing approximately 95.9% of the scenarios, indicating an operational efficiency. This finding is complemented by the model capability to determine the optimal production quantity, considering cost factors related to manufacturing and transportation, which is essential in minimizing overall inventory costs. Our methodology, based on machine learning and fuzzy logic, enhances the inventory management in dynamic sectors like the pharmaceutical industry. While our focus is on a single-product scenario between suppliers and buyers, future research hopes to extend this focus to wider contexts, as epidemic conditions and other applications. [ABSTRACT FROM AUTHOR]

In this paper, an Economic Order Quantity (EOQ) model of a fractional order with time-varying holding cost is discussed in detail with the help of numerical computations. As is well known, financial variables possess memories, which makes it appropriate for developing fractional modeling to describe the effect of memory in the financial system. This paper is concerned with the issues of deterioration of the items of the inventory system where the importance of time-varying holding cost on the memory-dependent inventory model has been studied. For the inclusion of the memory effect, fractional order derivative and fractional order integration have been used. Illustrative numerical examples and graphical presentations are exploited to verify the efficiency of the effect of fractional rate of change of inventory model and memory-affected costs. The numerical result shows that the output of memory-affected systems is different from the output of memory-less and low-memory-affected systems. Sensitivity analysis has been carried out in long memory effect as well as low memory effect to highlight the critical parameters of the two different systems. Sensitivity analysis clears that in the long memory effect, a small change in deterioration rate can disturb the system but in the low memory effect, such type of disturbances does not arises. At last, some conclusions are recommended. [ABSTRACT FROM AUTHOR]

Effective inventory management is crucial for businesses to balance minimizing holding costs while optimizing ordering strategies. Monthly or sporadic orders over time may lead to high ordering or holding costs, respectively. In this study, we introduce two novel algorithms designed to optimize ordering replenishment quantities, minimizing total replenishment, and holding costs over a planning horizon for both partially loaded and fully loaded trucks. The novelty of the first algorithm is that it extends the classical Wagner–Whitin approach by incorporating various additional cost elements, stock retention considerations, and warehouse capacity constraints, making it more suitable for real-world problems. The second algorithm presented in this study is a variation of the first algorithm, with its contribution being that it incorporates the requirement of several suppliers to receive order quantities that regard only fully loaded trucks. These two algorithms are implemented in Python, creating the software tool called "Inventory Cost Minimizing tool" (ICM). This tool takes relevant data inputs and outputs optimal order timing and quantities, minimizing total costs. This research offers practical and novel solutions for businesses seeking to streamline their inventory management processes and reduce overall expenses. [ABSTRACT FROM AUTHOR]

Obsolescence of critical manufacturing inputs is a major supply chain risk and represents a growing problem for procurement professionals that must be handled appropriately to profitably meet customers' needs. Supply side obsolescence occurs when suppliers end production of components and has been defined as "the loss or impending loss of original manufacturers of items or suppliers of items or raw materials." Supply side obsolescence may radically increase costs to an organization, including high inventory holding costs, paying high prices for components on the gray market, and lost sales or lost market share due to an inability to complete products. This research utilized a grounded theory approach and discovered: (1) the risks of supply side obsolescence, (2) the impacts of supply side obsolescence on focal firms, and (3) how leading firms utilize obsolescence procurement teams to combat this potentially disastrous problem. Of specific interest to managers, obsolescence procurement teams are identified as formal teams that focus on dealing with obsolescence proactively and work to prevent the negative effects of supply side obsolescence. Finally, the study provides propositions to guide future research and concludes with suggestions for managers regarding mitigating supply side obsolescence, which will only become more urgent as the rate of technological change continues to increase. [ABSTRACT FROM AUTHOR]

• A new dynamic lot-sizing problem was defined for remanufacturing systems. • The previous studies were extended by considering multi-level structured products with components commonality. • A mixed integer programming model was developed to represent the problem mathematically. • Two types of bi-directional move based heuristics, individual-and cluster-moves, were proposed. • The cluster-move based heuristic outperformed the other and gave near optimal solutions for small-sized test instances. This study addresses dynamic lot-sizing for remanufacturing systems in which end-of-use/life products are disassembled into their components on a disassembly workstation, each recoverable part is reprocessed on one of parallel reprocessing workstations and the reprocessed and newly purchased ones, if required, are reassembled into remanufactured products on a reassembly workstation. The problem is to determine disassembly, reprocessing, reassembly lot-sizes as well as the reprocessing workstation to reprocess each recoverable part while satisfying dynamic remanufactured product demands and workstation capacities in each period of a planning horizon. As an extension of the previous studies, multi-level structured products with components commonality are considered for the objective of minimizing the sum of setup, operation and inventory costs. A mixed integer programming model is developed, and due to the problem complexity, two types of heuristics are proposed that obtain an initial solution using a greedy-type heuristic and improve it by two types of bi-directional moves with various priority rules for forward and backward moves, i.e. individual-moves and cluster-moves. Computational results show that the cluster-move based approach that considers components commonality explicitly outperforms the individual-move based one significantly and gave fast near optimal solutions for small sized test instances. Finally, a sensitivity analysis on different multi-level product structures were done and the results are reported. [ABSTRACT FROM AUTHOR]

The paper addresses the pervasive issue of shelf holes in the retail sector and their negative impact on customer satisfaction. Empirical research reveals key contributors to shelf holes, including the inventory management deficiencies, supply chain coordination issues, and inefficient restocking processes. To address these challenges, an innovative solution is proposed by integrating artificial intelligence (AI), particularly ChatGPT, into the Theory of Constraints (TOC) Thinking Process (TP) framework. Integrating ChatGPT into the TOC TP framework significantly improves the identification of causal relationships, prioritizes outcomes, mitigates bias, and streamlines the development of the Future Reality Trees (FRTs), a core TP tool for providing a solution. ChatGPT proves to be a valuable tool in effectively addressing the root causes of shelf holes. The practical implications of the study are significant for the retail industry. Effective addressing shelf holes through AI integration enhances customer satisfaction, increases sales, reduces inventory costs, and streamlines supply-chain operations. Moreover, the study introduces an innovative approach to resolving shelf holes. The integration of ChatGPT into the TOC TP framework provides a novel application of AI in retail operations, demonstrating its originality and value. The study opens new avenues for leveraging the AI technologies in retail operations and problem-solving processes, thus paving the way to an improved overall organizational performance. [ABSTRACT FROM AUTHOR]

By matching the geocoded data of firm-level productivity and county-level expressway networks in China's Yangtze River Delta region, we establish a difference-in-differences model to investigate the impact of expressways on manufacturing firm productivity. The results show that expressways boost manufacturing firms' total factor productivity, with firms closer to expressway services achieving more significant productivity increases compared to those farther away. The results remain robust after controlling for the endogeneity problem using an instrumental variable approach. Expressways promote firm productivity by reducing inventory costs and increasing firm profitability and regional industrial concentration, whereas their effect on product innovation is insignificant. We also find heterogeneity in the impact of expressways on firm productivity, with firms in peripheral counties and in labor-intensive industries receiving a weak productivity-enhancing effect, while firms in central regions and those with high transport dependency benefit more. [ABSTRACT FROM AUTHOR]

Inventory optimization focuses on stabilizing the stockpile management in the company with the expenditure involved in a successful volatile system. The major challenge is to meet the customer's demand in a well-organized way for profitable management. The nonlinear programming problem (NLP) from fixed order inventory systems, was solved by using the geometric programming method with fuzzification executed by decagonal fuzzy numbers and graded-mean representation as a defuzzification method. In this paper, we had minimized the total cost for an inventory model to obtain the optimal quantity and compared it with the fuzzy inventory model and illustrated with a numerically. [ABSTRACT FROM AUTHOR]

Spare parts management has gained significant attention in recent years due to the considerable costs associated with backorders or excess inventory. This article addresses the challenge of determining the optimal number of spare parts to stock, assuming that the parts can be repaired. When an item fails, it is promptly sent for repair in a workshop. The time between failures and the repair time are assumed to follow an exponential distribution, although it should be noted that the results could be adapted to other distributions as well. This study introduces a heuristic method to find the optimal inventory level that minimizes the total cost, considering holding inventory, backorder, and repair costs. The research offers a valuable decision-making framework for determining the number of spare parts needed to minimize inventory costs, based on just two parameters: (1) the ratio of time to repair and time to failure, and (2) the ratio of the inventory holding cost of a spare part per day to the daily cost of an idle machine. To the best of our knowledge, there are no similar methodologies in the existing literature. The proposed method is straightforward to implement, employing graphs and simple computations. Therefore, it is anticipated to be highly beneficial for practitioners seeking a quick and reliable estimator of the optimal number of spare parts to stock for critical components. [ABSTRACT FROM AUTHOR]

It is a challenging task to develop appropriate realistic production inventory models due to the presence of imprecision in the data available in the current market situation. In addition, increasing environmental concerns around the world have led to a shift toward green products. There are subsidy programs implemented by governments in several countries for green products. In a real-life production system, the various inventory cost and carbon emission parameters are imprecise in nature. Interval number theory is an efficient tool for handling such impreciseness. This study investigates the effects of a simultaneous investment in greening innovation (GI) and emission reduction technologies (ERTs) in a green production inventory model where the various inventory cost components and the carbon emission parameters are interval-valued. Using the cap-and-trade carbon regulation policy, the optimal inventory decision is investigated based on a price and greenness level sensitive demand. Two models are developed depending on whether the manufacturer wishes or not to invest in ERTs. Both models consider the chances of imperfect production and their reworking process. The resulting interval optimization problems are solved using the quantum-behaved particle swarm optimization technique to derive the interval-valued optimum profit. Numerical illustrations for both models are presented. Several managerial insights are identified through a sensitivity analysis over the optimal solution regarding the main inventory parameters. The result shows that the greenness level of the product increases with the intensity of the subsidy offered by the government. Again, it is found that the manufacturing company of the green product as well as our environment benefit from joint investment in GI and ERT. [ABSTRACT FROM AUTHOR]

This study addresses the optimization of price and production quantity using game theory in the coffee supply chain in the city of Isfahan. The presented model focuses on the competition among members of the chain in a probabilistic environment and is based on discrete selection models. This model examines the impact of inventory and routing costs on the pricing model at the same time. To effectively solve real-world problems, the method of normal distribution approximation is used instead of the binomial distribution. The results of the model indicate that the computational time of the applied approximate model does not change with increasing the number of customers, and the model can determine the optimal price and production quantity. The advantage of the proposed model over other classic models is that it considers the inventory costs that have not yet been considered in price competition models with discrete selection. [ABSTRACT FROM AUTHOR]

This paper studied the integrated production and distribution scheduling problem in assembly flexible Job-Shop under distributed manufacturing environment. Combined with actual production situations, taking into account the inventory costs with production and distribution under the supply chain, this paper proposed an improved whale algorithm with the objective of minimizing the total cost of production and distribution as the joint scheduling optimization. For the multi-stage scheduling process of joint scheduling, this paper developed a five-level coding strategy based on process, product, plant, machine and vehicle. According to the characteristics of each stage, this algorithm designed corresponding hybrid population initialization strategies to improve the quality of the solution. To enhance the global exploration and local search capability of the algorithm, it improved a search operation of whale foraging and proposed four neighborhood structures with the orientation of strengthening the connection between individual leader whale and ordinary whale individuals. Finally, this paper compared multiple algorithms in related research areas to verify the advantages of the proposed algorithm in terms of convergence speed and solution quality, and compared joint scheduling with phased scheduling to verify the superiority of joint scheduling through simulation experiments. [ABSTRACT FROM AUTHOR]

Recycling, remanufacturing and repair processes increase the profitability of companies by providing less energy use and less waste generation, and have a positive effect on the total cost of the supply chain. In the present study, an integrated inventory model, which considers economic and environmental objectives together, was developed for a two-level supply chain system consisting of a single manufacturer and a single retailer located in different countries. The problem is the simultaneous calculation of the manufacturer's number of shipments and the buyer's shipment sizes, that is, the parameters of the integrated production-inventory policy, to minimize the total cost of the system. Each batch the retailer received contains both good quality products and defective products. Some of the defective products are scrapped and sent back to the manufacturer to enter the recycling/remanufacturing process, while the other part is added to the inventory after being repaired at a local company. The carbon emission costs of transporting products from manufacturer to retailer and defective products from retailer to manufacturer and repair center were included in the model. Our aim was to model the joint effects of outsourcing repair, recycling/remanufacturing, and carbon emissions on supply chain performance. Theoretical results were obtained with the help of a numerical example and sensitivity analysis was carried out. Numerical analysis showed that the repair process together with the recycling/remanufacturing process has a positive effect on the total inventory cost of the supply chain. [ABSTRACT FROM AUTHOR]

This study uses the discrete-event simulation approach to determine the reorder point of a retail store based on an acceptable service level under uncertainty in demand, lead time, and product damage. The proposed model is applied to A1–A12 products in a retail store under study where the owner requires a service level of at least 90 %. The model variables are both deterministic (i.e., order quantities, daily inventory cost per unit, purchase cost, and unit selling price) and probabilistic (i.e., client demand, lead time, and product damage). A simulation technique is used to describe the distribution of the probabilistic parameters and to produce them randomly. The profit and customer service simulation results determine the retail store's inventory policy. Since the retailer decided on the ROP based on the simulation results, it has an impact on 1) reducing the total daily inventory of A1–A10 by 23.0%, which would increase storage space for the additional products A11 and A12 to their sales, 2) increasing total daily profit of A1–A10 by 2.9 %, and 3) reducing total annual product damage numbers of A1–A10 by 22.2 %. [ABSTRACT FROM AUTHOR]

Demand fluctuations and uncertainty bring challenges to inventory management, and intermittent demand patterns increase the risk of inventory backlogs and raise inventory holding costs. In previous studies on inventory routing problems, different variants have been proposed to cope with complicated industrial scenarios. However, there are few studies on inventory routing problems with intermittent demand patterns. To solve this problem, we introduce a lateral transshipment strategy and build a single-product multi-period inventory routing mixed integer programming model to reduce customers' inventory backlogs, balance regional inventory, reduce inventory holding costs, and improve inventory management efficiency. Furthermore, we design an adaptive large-neighborhood search algorithm with new operators to improve the solving efficiency. The experimental results show that an appropriate transshipment price can reduce the share of distribution costs. Another finding is that higher-capacity vehicles lead to higher revenue. Our findings not only expand the scope of the IRP domain but also provide actionable management insights for business practitioners. [ABSTRACT FROM AUTHOR]

In this era of trying to get more information from data, demand forecasting plays a very important role for companies. Companies implement many of their plans based on these forecasts. However, forecasting demand is not always easy. Demand is divided into many different classes depending on its structure. One of them is the problem of intermittent demand. Estimating intermittent demand is a more difficult problem than estimating series with low variability and no null demand. When working with intermittent demands, either a good demand forecast or a good inventory policy is required. Determining an efficient inventory policy is important in terms of meeting customer demand and customer satisfaction. In this study, an inventory lower bound and upper bound are calculated to balance the inventory cost of intermittent demand and the lost sale cost. For this purpose, 7 different test data with intermittent demand structure were studied. A mathematical model is proposed to calculate the cost with the upper and lower inventories under intermittent demand. A feasible solution could not be obtained with the proposed model, and a fitness function for the relevant model was proposed. This function was run on test data using genetic algorithm (GA) and particle swarm optimization (PSO). The results and solution times of GA and PSO were compared. In this way, the variability of demand and the difference between arrival times of demands were met with minimal cost. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) promises considerable advantages over conventional manufacturing to meet the growing demand for customized products and faster delivery times. Consider a mobile mini-factory, that is, a vehicle equipped with an AM facility, which can simultaneously produce and transport the final products to the customers. The overlapping of production and transportation processes allows potential savings on customer delivery lead times and inventory holding costs, thereby facilitating on-demand fulfillment of the orders of intricate products. Based on this situation and motivated by a recent Amazon patent, we introduce a novel routing optimization problem called Simultaneous Production and Transportation Problem (SPTP) in this study. Given a set of customers and their respective orders with associated production time and delivery due dates, SPTP minimizes the trip time for the AM installed vehicle while meeting the customers' stipulated due dates for all deliveries. We formulate the problem using a mixed integer linear program, discuss several valid inequalities to strengthen the formulation, and discuss a cutting-plane-based exact solution approach. We also design a variable neighborhood search metaheuristic to solve larger instances of SPTP very efficiently. The effectiveness of the exact and heuristic solution approaches is demonstrated using extensive computational experiments. The study also explores the interaction between production and travel times in SPTP and how the problem compares with the traveling salesman problem and the single machine scheduling problem, each of which may be viewed as special cases of SPTP. Further, the problem involves a trade-off between the total trip time and the tardiness of the deliveries. Therefore, an extension of the proposed formulation is also proposed with interesting managerial insights on identifying appropriate trip time-tardiness combinations using an illustrative example. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.1195. [ABSTRACT FROM AUTHOR]

The most important strategic issue for several industries is where to find facilities so as to discover a transportation path for optimizing the objectives at the same time. This paper acquaints a streamlining model with incorporate the facility location problem, solid transportation problem, and inventory management under multi-objective environment. The aims of the stated formulation are multi-fold: (i) to seek the optimum locations for potential facilities in Euclidean plane; (ii) to find the amount of distributed commodities; and (iii) to reduce the overall transportation cost, transportation time, and inventory cost along with the carbon emission cost. Here, variable carbon emission cost is taken into consideration because of the variable locations of facilities and the amount of distributed products. After that, a new hybrid approach is introduced dependent on an alternating locate-allocate heuristic and the intuitionistic fuzzy programming to get the Pareto-optimal solution of the proposed formulation. In fact, the performances of our findings are discussed with two numerical examples. Sensitivity analysis is executed to check the resiliency of the parameters. Ultimately, managerial insights, conclusions and avenues of future studies are offered at the end of this study. [ABSTRACT FROM AUTHOR]

In this study, we consider a production lot-sizing and scheduling problem found in the fruit juice production industry from an enhanced inventory management perspective. The problem can be classified as a P2SMM (two-stage multi-machine lot-scheduling) problem. We extended the classical P2SMM problem by incorporating an additional inventory management aspect of finished products to reflect a possible real-life case problem, specifically regarding the shelf-life concept and limited warehouse capacity, with a possibility of outsourcing the warehousing demand to a third-party logistics company. We developed the mixed integer linear programming (MILP) model to fully represent the considered problem (due to the NP-hard nature of the problem, only small-scale instances could be solved to optimality), and the hybrid variable neighborhood search with linear programming (VNS/LP) model to solve both small and real-life large-scale problem instances. The goal of the developed models is to minimize total costs that consist of the cost of backordering, the cost of planned minimum and maximum stock level violation, the cost of warehouse capacity overflow, the costs of production setup time and unused available production time. The main idea of the VNS/LP model is to solve the scheduling segment of P2SMM (the production sequence) via a VNS heuristic, and the lot-sizing segment of P2SMM via the linear programming (LP) model. Based on the results from five variants of the problem setup, a potential decision maker can have an overview of the impact of different important input parameters (production time costs, warehouse capacity and costs, inventory related costs and production demand) on the total cost of a production process and improve its efficiency in changing conditions. [ABSTRACT FROM AUTHOR]

Uncertain demand information increases the difficulty of decision-making in supply chains, especially in the light of inventory costs. This article researches a two-echelon dual-channel fresh agricultural product (FAP) supply chain, and discusses optimal decision-making and the value of information sharing using the Stackelberg game. To eliminate the double marginalization effect between supply chain parties, a two-part linear tariff contract is introduced for coordination. The results show that information sharing is always profitable for the manufacturer, but not for the retailer. The inventory and shortage costs will reduce the manufacturer's optimal pricing and fresh-keeping effort level. When the manufacturer overestimates the market demand predicted by the retailer, asymmetric information will increase the manufacturer's inventory. The results provide important managerial implications for effective supply chain management. [ABSTRACT FROM AUTHOR]