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This paper aims to explore the effect of e‐marketplace on the supply chain's performance under the markdown policy. Profit and risk analyses are both conducted and channel coordination issues are examined.

The paper presents a markdown policy supply chain analytical model with e‐marketplace and examines the optimal markdown policy. The mean‐variance theory is employed to study both the risk and profit residing in the supply chain. Extensive numerical analysis is conducted. The paper investigates both the cases when e‐marketplace selling price is exogenous and endogenous.

The markdown policy can coordinate the supply chain as long as the parameters satisfy certain analytical conditions. The expected profit and risk in the supply chain are both increased when e‐marketplace is introduced. The retailer shares a larger portion of the increased expected profit but at the same time bears a higher risk.

In this study, similar to the mainstream literature in the related area, the supply chain consists of one manufacturer and one retailer, and there is one single selling season with one product. Despite being able to generate interesting analytical results, this model fails to capture the more complicated real world practices.

The existence of e‐marketplace can be beneficial to the whole supply chain in terms of expected profit improvement. When the expected profit increase brought about by e‐marketplace is large enough to compensate for both the operational cost of e‐marketplace and the increase of the risk, the retailer could consider introducing e‐marketplace to dispose of the excess inventory.

This paper is an original work. It is based on the reviewed literature and the model with markdown policy is new. This could be a reference for further research into optimal performance in the supply chain with e‐marketplace.

Retailers of seasonal merchandise often face the problem of selling a fixed inventory over a short selling season. For a small retailer, with a sunk investment in inventory, a major objective is revenue maximisation. This paper is concerned with the provision of decision support for the tactical problem of dynamically marking down the price of seasonal merchandise in order to increase/maintain revenue. A heuristic procedure is developed for quickly identifying, and highlighting for review, items which are “slow selling” and “economically viable for price markdown”. It is shown how a retailer can use such information in determining the timing and magnitude of price markdowns.

In a decentralized supply chain with one supplier and one retailer, a properly designed contract can lead to supply chain coordination. In this chapter, we model the selection of an appropriate coordinating contract from a menu of contracts including wholesale price, buyback, and markdown money, while allowing both the supplier and the retailer to assume the roles of Stackelberg leader and/or supply chain captain. This work extends previous literature that assumes that the supplier is both the Stackelberg leader and the supply chain captain. In our models, either agent can make stocking and pricing decisions. Our findings suggest that the feasibility of a coordinating contract depends on the addition of Pareto-improving, profit-sharing conditions that motivate agents to take part in the contract. Further, the selection of an optimal contract is based not only on which agent holds the overstock liquidation advantage, but also on the decision structure of the supply chain. For instance, when the supplier is the Stackelberg leader and the retailer is the supply chain captain, as well as holds the inventory liquidation advantage, and controls the stocking level, then a wholesale price contract can coordinate the supply chain under the proposed Pareto-improving profit sharing, termed Pareto-improving coordination. Additional results and managerial implications are presented in the chapter.

The purpose of this paper is to study the optimal pricing problem that retailers are challenged with when dealing with seasonal products. The friction between expected demand and realized demand creates a risk that supply during the season is not cleared, thus forcing the retailer to markdown overstocked supply.

The authors propose a framework based on a Cox regression analysis to determine optimal markdown paths. They illustrate this framework by a case study on a large department store.

The framework allows one to determine when and how much to markdown in order to optimize expected total profit given the available supply. When the law of demand holds at a disaggregated level, i.e. the individual retailer, it is also possible to optimize the markdown path.

This paper provides a framework for the complex dynamic pricing problem in retail using transactional data. The case study shows that significant revenues can be generated when applying this framework.

The influence of weather on business activities and human behaviour has been explored in several fields (e.g. finance and psychology), but little research about weather and retail sales is found in the retail or fashion literature. The purpose of the study is to analyse the relationship between temperature, one aspect of weather, and retail sales of seasonal garments.

The researchers collected sales data from a retailer of branded women's business wear in the Seoul‐Kyunggi area in South Korea. Along with the sales data for seasonal basic styles, corresponding daily and weekly average temperature data were collected and evaluated. The analysis for the study was drawn using descriptive statistics including graphical evaluations, correlation analysis and paired samples t‐test. Interviews with the retailer's merchandisers were used to supplement interpretation of the statistical data.

Results of this study provide strong evidence that fluctuations in temperature can impact sales of seasonal garments. During sales periods when drastic temperature changes occurred, more seasonal garments were sold. However, the temperature changes from day to day or week to week did not affect the number of garments sold for the whole season. Of the seasonal garments expected to sell within the same season, the selling periods of each product category differed depending on type of fabric and design. For some seasonal garments, the actual sales dates were one week to two weeks in variance from the merchandisers' forecasts.

Limitations in the sample (i.e. product category) and location of stores (i.e. geographic region) prevent the generalization of results to all seasonal garments or retailers. In spite of these limitations, this study can be a pilot study that supports the significant relationship between temperature and sales of seasonal basic products by quantifying the temperature effects on sales of particular products. Therefore, future studies are needed to establish generalized conclusions with a larger sample.

As little academic research is available about weather's effect on sales of garments, the present study contributes to the field of clothing and retail distribution by providing evidence of significant relationships between temperature and sales of seasonal clothing.

An uncertain product demand in online retailing leads to loss of opportunity cost and customer dissatisfaction due to instances of product unavailability. On the other hand, when e-retailers store excessive inventory of durable goods to fulfill uncertain demand, it results in significant inventory holding and obsolescence cost. In view of such overstocking/understocking situations, this study attempts to mitigate online demand risk by exploring novel e-retailing approaches considering the trade-offs between opportunity cost/customer dissatisfaction and inventory holding/obsolescence cost.

Four e-retailing approaches are introduced to mitigate uncertain demand and minimize the economic losses to e-retailer. Using three months of purchased history data of online consumers for durable goods, four proposed approaches are tested by developing product attribute based algorithm to calculate the economic loss to the e-retailer.

Mixed e-retailing method of selling unavailable products from collaborative e-retail partner and alternative product's suggestion from own e-retailing method is found to be best for mitigating uncertain demand as well as limiting customer dissatisfaction.

Limited numbers of risk factor have been considered in this study. In the future, others risk factors like fraudulent order of high demand products, long delivery time window risk, damage and return risk of popular products can be incorporated and handled to reduce the economic loss.

The analysis can minimize the economic losses to an e-retailer and also can maximize the profit of collaborative e-retailing partner.

The study proposes a retailer to retailer collaboration approach without sharing the forecasted products' demand information.

This paper aims to study the mixed after-sales service which simultaneously offers return and replacement services. The authors develop a model to propose what kind of after-sales service the firm should choose and how to make the after-sales service policy to improve the profit. The study aims to extend the literature on the mixed after-sales service and give some support to the managers to make decisions.

In this paper, the authors use the optimization modeling method to describe the situations of a firm offering two exclusive after-sales service policies and a mixed after-sales service policy, respectively. They compare the results in different cases and analyze the impact of different parameters on the boundary values and other results. Finally, the authors include three numerical examples to illustrate the major results.

The authors find that the mixed after-sales service can successfully segment the market, meet various customers’ distinct needs and differentiate the service prices to improve the total profit. Moreover, the authors find the boundary values which indicate the optimal interval for each service. Then, for a certain situation, they can clearly tell which after-sales service dominates and provides the optimal selling price, order quantity and total profit. Besides, the authors show the impact of different parameters on the boundary values and other results.

This paper combines after-sales service into traditional models and provides a new mixed service to segment the market and improve total revenue. It provides some managerial implications for the decision-makers.

The purpose of this paper is to investigate the manufacturer’s production, pricing and green technology investment decision problem when strategic customer behavior and carbon emissions-sensitive random demand is taken into consideration and discuss the impact of carbon emissions-sensitive demand on the manufacturer’s operation strategies, total carbon emissions and maximum expected profit.

The authors formulate a model to introduce carbon emissions-sensitive demand into the newsvendor framework with strategic customer behavior. The authors characterize the rational expectations equilibrium to derive the optimal solutions to the manufacturer. The authors analyze the effects of carbon emissions-sensitive demand on the manufacturer’s optimal strategies, total carbon emissions and maximum expected profit by comparative analysis.

The authors obtain the manufacturer’s optimal production, pricing and green technology investment strategies under rational expectations equilibrium in scenario of price-sensitive demand and that of carbon emissions-sensitive demand, respectively. The authors find that as customer demand changes from price-sensitive demand to carbon emissions-sensitive demand, the manufacturer’s optimal prices are the same but optimal production quantity, optimal unit carbon emissions and maximum expected profit go down. Though the total emissions decrease, the carbon emissions reduction would not increase as the demand is more carbon emissions-sensitive. Whether it increases or decreases depends on the model parameters.

Carbon emissions-sensitive demand and strategic customer behavior are considered simultaneously in an integrated model. The result can guide the manufacturer decision-making. The proposed model are hoped to shed light to the future works in the field of sustainable supply chain management.

The emergence of risk in today's business environment is affecting every managerial decision, majorly due to globalization, disruptions, poor infrastructure, forecasting errors and different uncertainties. The impact of such disruptive events is significantly high for perishable items due to their susceptibility toward economic loss. This paper aims to design and address an operational planning problem of a perishable food supply chain (SC).

The proposed model considers the simultaneous effect of disruption, random demand and deterioration of food items on business objectives under constrained conditions. The study describes this situation using a mixed-integer nonlinear program with a piecewise approximation algorithm. The proposed algorithm is easy to implement and competitive to handle stationary as well as nonstationary random variables in place of scenario techniques. The mathematical model includes a real-life case study from a kiwi fruit distribution industry.

The study quantifies the performance of SC in terms of SC cost and fill rate. Additionally, it investigates the effects of disruption due to suppliers, transport losses, product perishability and demand stochasticity. The model incorporates an incentive-based strategy to provide cost-cutting in the existing business plan considering the effect of deterioration. The study performs sensitivity analysis to show various “what-if” situations and derives implications for managerial insights.

The study contributes to the scant literature of quantitative modeling of food SC. The research work is original as it integrates a stochastic (uncertain) nature of SC simultaneously coupled with the effect of disruption, transport losses and product perishability. It incorporates proactive planning strategies to minimize the disruption impact and the concept of incremental quantity discounts on lot sizes at a destination node.

The purpose of this paper is to review and analyze the perishable inventory models along various dimensions such as its evolution, scope, demand, shelf life, replenishment policy, modeling techniques and research gaps.

In total, 418 relevant and scholarly articles of various researchers and practitioners during 1990-2016 were reviewed. They were critically analyzed along author profile, nature of perishability, research contributions of different countries, publication along time, research methodologies adopted, etc. to draw fruitful conclusions. The future research for perishable inventory modeling was also discussed and suggested.

There are plethora of perishable inventory studies with divergent objectives and scope. Besides demand and perishable rate in perishable inventory models, other factors such as price discount, allow shortage or not, inflation, time value of money and so on were found to be combined to make it more realistic. The modeling of inventory systems with two or more perishable items is limited. The multi-echelon inventory with centralized decision and information sharing is acquiring lot of importance because of supply chain integration in the competitive market.

Only peer-reviewed journals and conference papers were analyzed, whereas the manuals, reports, white papers and blood-related articles were excluded. Clustering of literature revealed that future studies should focus on stochastic modeling.

Stress had been laid to identify future research gaps that will help in developing realistic models. The present work will form a guideline to choose the appropriate methodology(s) and mathematical technique(s) in different situations with perishable inventory.

The current review analyzed 419 research papers available in the literature on perishable inventory modeling to summarize its current status and identify its potential future directions. Also the future research gaps were uncovered. This systemic review is strongly felt to fill the gap in the perishable inventory literature and help in formulating effective strategies to design of an effective and efficient inventory management system for perishable items.