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This paper proposes a new inventory model with inspection policy because in practice the received orders may contain non- conforming (NC) items. So, a buyer who receive an order from a supplier should use an inspection policy.

The inspection policy is assumed to be zero-defect single sampling. Under this policy a lot is accepted only if no defect has been identified in the inspected sample. The fraction of NC is assumed to be a random variable following a Binomial distribution and the number of NC items detected by inspection assumed to be a random variable, which follows a hypergeometric distribution. Order quantity and sample size are the two decision variables. A solution procedure is presented for the proposed model. The proposed procedure presents the optimal solution.

Numerical examples presented to illustrate the procedure outlined for the proposed model and its applicability. The results of numerical examples and comparing them with traditional EOQ model reveal that by the proposed model, the buyer could reduce total cost that shows the efficiency and validity of the proposed model.

The novelty of this paper is the new proposed model that considers inspection policy in inventory management. The proposed model determines sample size as well as order quantity to consider both subject of inventory management and quality control, simultaneously.

The purpose of this paper is to study joint inventory and pricing strategy for a continuous inventory review system. While dynamic pricing decisions are often studied in the literature along with inventory management, the authors' aim in this study is to obtain a single long‐run optimal price; also to gain insight about how to obtain the optimal price and inventory control variables simultaneously and then the benefits of joint optimization of the inventory and pricing decisions over the sequential optimization policy often followed in practice.

A general (R;Q) policy system with fixed cost of ordering is modelled and then the case where unsatisfied demand is lost is studied. General forms of both the additive and multiplicative demand models are used to obtain structural results.

By showing optimality conditions on the price and inventory decision variables, two algorithms on how to obtain optimal decision variables, one for additive and another for multiplicative demand‐price model are provided. Through extensive numerical analyses, the potential profit increases are reported if the price and inventory problem are solved simultaneously instead of sequentially. In addition, the sensitivities of optimal decision variables to system parameters are revealed.

Although there are several studies in the literature investigating emergency price change models, they use arbitrary exogenous prices menus. However, the value of a price change can be better appreciated if the long‐run price is optimal for the system.

Very few researchers have investigated constant price and inventory optimization, and while there are several past studies demonstrating the benefits of dynamic pricing over a static one, there still are not many findings on the benefit of joint price and inventory optimization.

The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period order-up-to level ((R, S)) inventory control policy and its different variants such as (R, βS) (R, γO) and (R, γO, βS) proposed by Jakšič and Rusjan, (2008) and Bandyopadhyay and Bhattacharya (2013).

A hybrid forecasting model has been developed by combining the feature of discrete wavelet transformation (DWT) and an intelligence technique, multi-gene genetic programming (MGGP), denoted as DWT-MGGP. Performance of DWT-MGGP model has been verified under (R, S) inventory control policy considering demand from three different manufacturing companies.

A comparison between DWT-MGGP model and autoregressive integrated moving average forecasting model has been done by estimating forecast error and BWE. Further, this study has been extended with analysing the behaviour of BWE considering different variants of (R, S) policy such as (R,βS) (R, γO) and (R,γO,βS) and found that BWE can be moderated by controlling the inventory smoothing (β) and order smoothing parameters (γ).

This study is limited to different variants of (R, S) inventory control policy. However, this study can be further extended to continuous review policy.

The proposed DWT-MGGP model can be used as a suitable demand forecasting model to control the BWE when (R, S), (R,βS) (R,γO) and (R,γO,βS)inventory control policies are followed for replenishment.

This study analyses the behavior of BWE through controlling the inventory smoothing (β) and order smoothing parameters (γ) when demand is predicted using DWT-MGGP forecasting model and order is estimated using (R, S), (R,βS) (R,γO) and (R,γO,βS) inventory control policies.

The purpose of this paper is to report the results of a national survey that examined the extent of implementation of Health Promoting Schools (HPS) in Ireland.

A quantitative research design was adopted. A questionnaire was administered to all post-primary schools in the country (n=704). Data were analysed with the support of the software packages, SPSS and MaxQDA.

A response rate of 56 per cent (n=394) was achieved. Over half of these schools (56 per cent) self-identified as health promoting. Schools reported success in the areas of environment and curriculum and learning, however, partnerships and policy and planning required more attention. Some models of good practice emerged from the data but these were in the minority. Many schools, when asked to describe health promotion in their school, placed emphasis on physical health (diet and exercise) and curriculum predominately rather than the broader whole school conceptualisation. Only 35 per cent of HPS schools had a team supporting HPS developments. Only 36 per cent identified the existence of a school policy to support HPS. This suggests that further coherence for sustained and comprehensive implementation of HPS is necessary.

The research was conducted with school staff, in the first instance who self-reported their school’s level of HPS engagement.

This paper offers the first national baseline data available in relation to engagement in HPS in Ireland. It provides a valuable starting point from which further research with schools in this field can be conducted.

The design of an effective inventory control system is difficult, particularly if the demand fluctuates widely and in a seasonal manner. Most traditional analytical models seem to be inadequate and over‐simplified. An alternative to established mathematical models is suggested and control policies for a specific industrial situation are presented. The case of paint containers for a large paint company is considered. Demand, which is highly seasonal, has been deseasonalised by a Census‐II package to produce a demand distribution pattern, used as an input to simulation exercises. Four policies have been tested and analysed. Simulation results show that the (r, Q) system with a variable ROP adjusted each month on three months′ moving average of seasonalities shows the best results.

The purpose of this paper is to investigate whether truthful information sharing can be achieved via informal cheap talk in a competitive setting, and how carbon emission constraint and information-sharing modes (no information sharing, partial information sharing and public information sharing) interact with each other under cap-and-trade regulation.

This paper establishes an emission-dependent supply chain consisting of a manufacturer, an incumbent retailer who has superior demand information and a new entrant retailer. The manufacturer abates carbon emissions under the pressures of government environmental regulation and consumers’ eco-friendly concern. The research formulates a multistage game to explore every party’s decision and the implications of information-sharing modes.

The results show that truthful information sharing can be achieved when the manufacturer decides both the wholesale price and carbon emission abatement. The results also show that the incumbent retailer’s information-sharing decision highly depends on the manufacturer’s capacity in abating carbon emissions and the demand uncertainty.

The research adds value to information management and sustainable production literature. This work emphasizes the interaction between the information flow and material flow. Not only it investigates the factors that affect information-sharing modes from a new point of view when considering carbon emission constraint, but also provides operational strategies for manufacturers to make more profit when facing asymmetric information and emission regulation.

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 investigate the total cost function of an inventory system with a reorder point/order quantity policy where the lead time is controllable based on the cost paid by the buyer for the service.

Cost functions are presented to investigate how the changes in lead time affect different components of inventory cost in the present of random demand. Two methods including an iteration technique and Simulated Annealing (SA) algorithm are presented to deal with the cost optimization issue. The application of proposed model is illustrated using numerical case scenarios.

The cost functions show that besides ordering cost, change in stochastic demand during lead time is the major factor that affects the other cost components such as holding and penalty costs. This finding is validated by numerical study. Results also show that performance of SA algorithm is highly similar to iteration methodology, while the former one is easier in application.

This paper develops less complex, more pragmatic methods, easily adoptable by logistics managers for cost minimization. This paper also analyzes and highlights the unique characteristics and features of these two approaches that can help practitioners in making the right choice when faced with the identified logistics issue.

This research explicitly investigate impacts of changing lead time on inventory cost components which enables informed decision making and inventory system planning for cost optimization by logistics practitioners. Two methodologies that can be easily used by practitioners without deep mathematical analysis and is cost effective are introduced to solve the optimization problem. Detailed roadmaps of how to implement proposed approaches have been illustrated by different case scenarios.

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.

This paper aims to propose a supply model of periodic review with joint replenishment for multi-products grouped by several variables with random and time dependence demand.

The products are grouped by multivariate cluster analysis. The stochastic inventory model describes the random demand of each product, considering the temporal dependency through a generalized autoregressive moving average model. Stochastic programming for the total cost of inventory is obtained considering the expected value of the demand per unit of time.

The total costs for the products grouped with the proposed model are 6% lower than for the individual inventory policy. The expected shortage units decrease significantly in the proposed grouped model with temporary dependence. In addition, the proposal with temporary dependency has lower costs than when the independent and identically distributed demand is considered.

The proposed policy is exemplified with real-world data from a Chilean hospital, where the products (drugs) are segmented by grouping variables, forming clusters of drugs with homogeneous behavior within the groups and heterogeneous behavior between groups.