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The existence of the advertising delay effect and its impact on supply chain operations have been demonstrated in the current study. Therefore, this study develops a timely inventory control strategy for the fresh produce supply chain to address the advertising delay effect in the fresh produce supply chain.

This study proposes a game model based on the Nerlove-Arrow time delay differential equation and Pontryagin's maximum principle. Through comparative analyses of the optimal equilibrium strategies, the authors compare the optimal equilibrium strategies, product goodwill and optimal inventory trajectories for suppliers and retailers under secondary replenishment decisions and decentralized decisions.

The authors find that (1) Only when the sales cycle meets certain conditions can the overall profit of the supply chain under the secondary replenishment decision be greater than that under the decentralized decision. As the price markup coefficient increases, the total profit of the supply chain first increases and then decreases. (2) With the increase in the delay time, the replenishment quantity during the initial period gradually decreases. After the delay time elapses, the inventory depletion rate under secondary replenishment decisions is faster than that under decentralized decision-making. (3) Although there is a continuously increasing maximum value of product goodwill with the increase in delay time, it becomes difficult to achieve this value for longer delays.

The authors’ findings provide a theoretical basis for supply chain members of fresh agricultural products to select replenishment and inventory control strategies when adopting different levels of delay in advertising marketing.

Firstly, this paper explains the impact of advertising delay effect on fresh produce supply chain from a dynamic perspective, and secondly, it provides guidance on advertising formulation and inventory replenishment for fresh produce retailers under the influence of advertising delay effect.

The purpose of this paper is to propose a multivariate-based method to classify products in replenishment categories based on principal component analysis (PCA) along with two classification algorithms, k-nearest neighbor (KNN) and linear discriminant analysis (LDA).

In the propositions, PCA is applied to data describing products’ features and demand behavior, and a variable importance index (VII) is derived based on PCA parameters. Next, products are allocated to inventory replenishment models applying the KNNs to all original variables; the classification accuracy is then assessed. The variable with the smallest VII is removed and a new classification is carried out; this iterative procedure is performed until a single variable is left. The subset yielding the maximum classification accuracy is recommended for future classification. The aforementioned procedure is repeated replacing the KNN by the LDA.

When applied to real data from a consulting company, the KNN classification technique led to higher performance levels than LDA, yielding 89.4 percent average accuracy and retaining about 80 percent of the original variables. On the other hand, LDA reached 87.1 percent average accuracy and retained 95 percent of the variables. Based on such results, the authors’ findings suggest that 14 out of the 24 variables are crucial in determining an inventory replenishment model for a product in a specific location replacement. Several of the retained variables were typically used in reorder point estimation or associated to market profile in specific locals.

The idea of this paper is to remove irrelevant and noisy market metrics that jeopardize the correct allocation of products to the most appropriate replenishment model.

This paper aims to discuss a simulation study for a multi‐product, two‐echelon inventory replenishment system. The paper compares a one‐warehouse N‐retailer replenishment system to a two‐warehouse, N‐retailer system with cost per unit of distribution and delivery lead‐times as the performance measures. The purpose is to demonstrate that under specific circumstances a two warehouse N‐retailer inventory replenishment system provides better customer service without significant changes in the cost.

Mathematical modeling and simulation methodology is used to test the performance of the proposed two warehouse N‐retailer system and statistical analysis is used to compare the performance of several scenarios.

The two warehouse replenishment system indeed reduces delivery lead‐times, used as a measure of customer service, under specific conditions such as controllable freight costs.

Caution should be exercised when interpreting these findings as the historical data used was from a single source. The paper did not investigate the effects of variable shipping costs from the manufacturing plant, warehouse and retailer. Future research could also consider multiple second level warehouses.

The findings provide a persuasive argument for manufacturers struggling with performance issues and channel relationships. Moreover, in addition to contributing to efficiency of distribution, two level systems can also enhance ability to adapt to local market conditions and to unexpected demand variations.

The model examined in this paper addressed a specific case for one company. While freight costs and warehousing costs will vary across companies, the cost represented here may be used as a gauge for evaluating systems with cost structures in the vicinity of those for the company represented in this paper. Additionally, the model is amenable to substitution of other firms' cost structures.

The purpose of this paper is to investigate the impact of backroom storage in supply chain replenishment decision parameters: the order quantity based on the well-established economic order quantity (EOQ) model.

The authors develop an EOQ-type model to investigate the operational cost impact of the order quantity with backroom storage. Because of the discrete and discontinuous nature of the problem, a modification of an existing algorithm is applied to obtain an optimal order quantity. Numerical experiments derived from a leading retailer in Thailand are used to study the cost impact of the backroom.

The paper shows that the backroom storage will significantly affect the decision regarding the order quantity. If its effect is ignored, the cost increase can be as high as 30 per cent. The costs and operations of additional shelf-refill trips from the backroom must be carefully analyzed and included in the decisions of replenishment operations.

The model is a simplified version of the actual replenishment process. Validation from a real-world setting should be used to confirm the results. There are many additional opportunities to further integrate other issues in this problem such as shelf space decisions or joint order quantity between vendors and retailers.

The insights gained from the model will help managers, both retailers and vendors or manufacturers, make better decisions with regard to the order quantity policy in the supply chain.

Problems with backroom storage have been qualitatively described in the literature in the past decade. This paper is an early attempt to develop a quantitative model to analytically study the cost impact of backroom on order quantity decisions.

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.

Aims to examine the value of manufacturer access to downstream demand information in managing product introductions and to Identify factors affecting this value.

Simulation based on actual data on 19 product introductions is used for comparing different types of demand information and their usefulness to a manufacturer. Two metrics are introduced. Bias indicates if there is a consistent difference between demand information from two sources. Delay in demand synchronization measures how long it takes for demand information from two sources to start conveying demand similarly in a transient situation.

Finds that, in the supply chain examined, demand variability is mainly induced by distributors, whereas bias and delay in demand synchronization are mainly induced by retail outlets, especially for products with large wholesale packages compared with their sales.

The simulation model is simple and does not enable realistic examination of how a manufacturer could best use downstream demand data in managing its operations. Further research including such mechanisms as forecasts and stock‐outs is needed.

Provides a means for manufacturers to assess when they should invest in gaining access to downstream demand information and to estimate when their traditional information sources start to accurately convey end‐customer demand for new products.

Although it has been suggested that the value of access to downstream demand information could be greatest in situations with transient or irregular demand, such as product introductions, this claim has not yet been thoroughly examined. This study is a first attempt at filling this gap in the theory.

The purpose of this paper is to examine how several aspects of supplier category management policy might affect category sales performance at Korean supermarkets.

Aggregated (market‐level) Nielsen time‐series category management and sales data for several variety enhancer categories in the Korean supermarket industry are analyzed.

Across both small and large supermarkets, both the number of brands and the forward inventory level had positive effects on sales while days of supply of a product had a significant negative effect. For large supermarkets, the out‐of‐stock rate also had a significant negative effect, while for small supermarkets the retail distribution rate had a significant positive effect.

Using data for the Korean supermarket industry, this study demonstrates which policies for product assortment, pricing, stocking, and product replenishment can affect category sales and how these effects may vary between large and small stores. However, the findings rest on market‐level aggregated data and may be limited in scope to variety enhancer (i.e. low purchase frequency and high penetration) categories. Future research could try to relax these limitations.

The value of this study lies in confirming findings such as how category sales are higher in categories with a relatively larger number of brands (as has been observed in the US supermarket industry), as well as in the surprising finding that category pricing policies do not have a significant effect on category sales even though variety enhancer categories are generally considered price sensitive.

This research aims to consider the impact of common inventory system inaccuracies that occur in retail outlets on the inventory levels, fill rate, and service level of those outlets by simulating daily customer demand and random error in the inventory system.

The simulation experiments vary the amount of inventory system error, the frequency of inventory record error correction, the size of the daily demand, the number of days in the replenishment system's review interval, and the replenishment system's customer service level.

Inventory system error and the frequency with which the error is corrected are statistically significant for fill rate and service level. Thus, inaccuracies in inventory levels affect a retail outlet's ability to service its customers, though at the single SKU level, the results do not indicate a practical impact due to countervailing effects.

Retail outlets must be aware that error exists and can influence the behavior of their replenishment systems, but the overall impact may not be as significant as it might appear.

This research extends prior work on the effects of inventory inaccuracies and clarifies the debate pertaining to their ultimate effects on retail performance outcomes.

Efficient consumer response (ECR) is a supply chain management strategy which attempts to address the inefficiencies that have led to excessive inventory and unnecessary costs at all levels within the grocery industry supply chain. Although originating in the USA, ECR has also attracted attention and interest in many other countries. This paper presents the results of an Australian study which was designed to assess the applicability of ECR within the Australian grocery industry. The results of the study indicate that the inefficient business practices of the US supply chain are also prevalent within the Australian grocery industry and that some Australian companies had already begun to engage in business activities related to ECR as early as 1996.

The purpose of this paper is to present a methodology to structure the problem of retail out of stock (OOS). This methodology allows investigating risk factors and barriers related to the main causes and consequences that lead to OOS occurring.

The proposed methodology to structure the OOS problem is based on the bow- ie tool, which allows better visualisation, understanding and analysis of a complete OOS scenario. This proposal comprises exploring the main causes and consequences of OOS, the barriers to prevention and mitigation, the escalation factors to control undesirable events and to define actions to eliminate or mitigate the OSS risk.

Several potential causal risk factors, related to technical, behavioural, cultural and organisational aspects, were identified with this methodology. With the analysis of the OOS scenario, it was observed that the factors that lead to the OOS risk are preventable. In order to improve existing barriers or implement new barriers, a set of actions can be recommended to reduce or eliminate OOS risk factors.

From a better understanding of hazard, the bow-tie methodology allows identifying crucial factors that could be acted upon to reduce the incidence of OSS. Thus, the value is to propose a methodology that allows establishing the preventive and protective barriers necessaries and the escalation factors related to each of these to help structure the problem and consequently reduce the OOS in retail organisations.