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The development of information technology has made it possible for companies to get access to information about their customers ' future demand. This paper outlines various approaches to utilize this kind of visibility when managing inventories of end products on an operative level. The purpose is to explain the consequences, for capital tied up in inventory, of sharing four different types of planning information (point-of-sales data, customer forecasts, stock-on-hand data, planned orders) when using re-order point (R,Q) inventory control methods in a distribution network.

A simulation study based on randomly generated demand data with a compound Poisson type of distribution is conducted.

The results show that the value of information sharing in operative inventory control varies widely depending on the type of information shared, and depending on whether the demand is stationary or not. Significantly higher value is achieved if the most appropriate types of information sharing are used, while other types of information sharing rather contribute to decreased value. Sharing stock-on-hand information is valuable with stationary demand. Customer forecast and planned order information are valuable with non-stationary demand. The value of information sharing increases when having fewer customers, and when the order quantities are large. Sharing point-of-sales data is not valuable, regardless of the demand type.

The use of simulation methodology is a limitation, because the study has to be limited to a specific model design, and because it is not based on primary empirical data. The study is especially limited to dyadic relationships in supply chains, and to distribution networks with a rather limited number of customers.

Guidance is given about what type of information should be appropriate to share when different types of demand patterns and distribution networks, and how order batch sizes and lead times affect the value of information sharing when using re-order point (R,Q) methods.

Very limited research providing specific assessments of potential inventory control consequences when sharing planning information in various contexts has been found in the literature. The findings and conclusions also question some previous research on information sharing.

The purpose of this paper is to present an operations model for retail replenishment collaboration and identifies its expected benefits and limitations for the members of a grocery supply chain.

A case study is conducted on a development project between a grocery wholesaler and two grocery product suppliers. Data are collected through semi-structured interviews with key respondents from four different companies.

Despite advances in collaborative practices in grocery supply chains, retail store replenishment management faces challenges. In particular, demand exceptions management is a challenge in the grocery industry. A replenishment model called Collaborative Buyer-Managed Forecasting (CBMF) creates a proactive planning approach and a platform for close collaboration in the supply chain. The centralised forecasting transforms retailer sales data into a plan which serves the whole supply chain by creating one-order forecast. The CBMF model facilitates efficient demand management, improves demand responsiveness and promotes better availability of products in retail stores.

CBMF provides a replenishment planning model for the whole supply chain. It is tested to a limited extent in one supply chain.

The study provides managers with a better understanding of the benefits of centralised forecasting and closer replenishment collaboration, especially during periods of exceptional demand.

A new approach for managing demand in grocery supply chains with centralised forecasting is provided.

To determine the extent to which long‐established technologies (such as barcoding and electronic data interchange (EDI)) have been applied across their supply chains, identify factors influencing implementation, discuss future trends for new technologies, and document the business benefits accrued.

Five case studies were conducted in manufacturing companies who are members of EAN Australia. As members of EAN Australia, the case study companies were using (to some extent at least) some aspects of the EAN system, and thus involved in using e‐commerce‐enabling technologies.

Indicates that the use of technology is largely restricted to the demand (in this study retail trading partners) side of the supply chain, despite a general understanding of the potential benefits of implementation backward through the supply side. There is general agreement that emerging technologies (such as the internet) will provide further opportunities, and at the same time increase pressure for extended use.

Being limited to organizations operating in the Australian fast moving consumer goods (FMCG) sector, the results therefore need to be read in this context. It would be useful if these findings could be compared with those from other countries and different industry sectors.

It is apparent that for manufacturers a critical loop is often not closed due to the limited availability of point of sale (POS) data from major trading partners, and that this has served to limit value capture (by the manufacturers) through extended application.

Highlights both the potential for business‐to‐business (B2B)‐enabling technologies to facilitate improvements in manufacturing operations, and the reality of their current limitations. An important emergent theme was that business benefits could be as much attributed to complementary methodologies (cooperative arrangements, cross‐functional involvement, etc.), as to the use of the technologies themselves. As such, the cases point to the critical nature of information in this area, and to the need for appropriate relationships between trading partners to underpin and enable data flows between trading partners.

The purpose of this paper is to propose a new methodology that handles the issue of the dynamic behavior of customers over time.

A new methodology is presented based on time series clustering to extract dominant behavioral patterns of customers over time. This methodology is implemented using bank customers’ transactions data which are in the form of time series data. The data include the recency (R), frequency (F) and monetary (M) attributes of businesses that are using the point-of-sale (POS) data of a bank. This data were obtained from the data analysis department of the bank.

After carrying out an empirical study on the acquired transaction data of 2,531 business customers that are using POS devices of the bank, the dominant trends of behavior are discovered using the proposed methodology. The obtained trends were analyzed from the marketing viewpoint. Based on the analysis of the monetary attribute, customers were divided into four main segments, including high-value growing customers, middle-value growing customers, prone to churn and churners. For each resulted group of customers with a distinctive trend, effective and practical marketing recommendations were devised to improve the bank relationship with that group. The prone-to-churn segment contains most of the customers; therefore, the bank should conduct interesting promotions to retain this segment.

The discovered trends of customer behavior and proposed marketing recommendations can be helpful for banks in devising segment-specific marketing strategies as they illustrate the dynamic behavior of customers over time. The obtained trends are visualized so that they can be easily interpreted and used by banks. This paper contributes to the literature on customer relationship management (CRM) as the proposed methodology can be effectively applied to different businesses to reveal trends in customer behavior.

In the current business condition, customer behavior is changing continually over time and customers are churning due to the reduced switching costs. Therefore, choosing an effective customer segmentation methodology which can consider the dynamic behaviors of customers is essential for every business. This paper proposes a new methodology to capture customer dynamic behavior using time series clustering on time-ordered data. This is an improvement over previous studies, in which static segmentation approaches have often been adopted. To the best of the authors’ knowledge, this is the first study that combines the recency, frequency, and monetary model and time series clustering to reveal trends in customer behavior.

The challenge faced by supplier companies in the grocery supply chain for implementing collaborative planning, forecasting and replenishment (CPFR) is how to get the retailer to forecast, especially when it has not been necessary before. In this paper a solution that would allow collaboration on a wide scale is presented. The forecasting approach is called “rank and share” and uses input from the retailer’s existing planning process – the category management process. The benefit of using category management as the basis is that the retailer can scale up collaboration with a large number of suppliers without increasing planning resources. For the supplier the benefit is point of sales forecasts at the time of the assortment decision. To support this collaborative forecasting process there is a need for more robust replenishment solutions, new measures to illustrate benefits, and for a distributed planning architecture and software. Potential solutions for these are also discussed in the paper.

Managing inventory continues to be a growing area of concern for many retailers due to the multitude of issues that arise from either an excess or shortage of inventory. This study aims to understand how a large-scale retail chain can improve its handling of excess seasonal inventory using three common strategies: information sharing, visibility, and collaboration.

This study has been designed utilizing a case study method focusing on one retail chain at three key levels: strategic (head office), warehouses, and retail stores. The data have been collected by conducting semi-structured interviews with senior-level employees at each of the three levels and employing a thematic analysis to examine the major themes.

The results show how three common strategies are being practiced by this retailer and how utilizing these strategies aids the retailer in improving its performance in regard to seasonal inventory. Among our research findings, some challenges were discovered in implementing the strategies, most notably: human errors, advanced forecasting deficiencies, and the handling of return merchandise authorizations.

This research takes a case study approach and focuses on one big-box retailer. The authors chose to study three levels (head office, warehouses, and retail stores) to gain a deeper understanding of the functions and processes of each level, and to understand the working relationships between them. Through the collection of primary data in a Canadian context, this study contributes to the literature by investigating supply chain strategies for managing inventory. The Canadian context is especially interesting due to the multi-cultural demographics of the country.

The purpose of this paper is to introduce a new approach for the identification of price thresholds, which enables learning true thresholds from previous buying decisions recorded in POS scanner data.

The methodology presented herein combines spline regression with generalized cross‐validation. Classical Chi‐square testing confirms the separation of regimes of the price response function by this methodology. Five propositions concerning the consumers' response to odd pricing in a Western‐type market are evaluated.

Despite the widespread retail practice odd prices are unlikely to flag the actual threshold in consumer response. The term odd price refers to prices with a non‐zero ending in the cent digit, e.g. .95, .98 or .99, which are commonly used in Western‐type markets. Moreover, the simple odd price effects are distinguished from odd‐ending prices with the first number left of the decimal point set to an odd number. The results show that even these prices not always flag a threshold in consumer response.

The discussion of the odd‐price effect is confused by conflicting empirical results and related interpretations of the underlying mechanisms. In contrast to many previous investigations – which are restricted to the consideration of very few price endings – this study covers all reasonable prices. Statistically significant odd‐price effects are found for some brands, but not for all within the same category.

One must argue for checking the thresholds for each brand individually rather than generalizing by applying misleading rules of thumb.

The paper provides researchers as well as practitioners with a methodology to evaluate price thresholds and outlines the shortcoming of contemporary retailers pricing practices in a detailed manner.

The purpose of this study is threefold: to use an innovative metafrontier‐to‐data‐envelopment analysis (MDEA) model incorporating multiple outputs and inputs – including the item revenue, gross profit, food costs, time‐driven labor costs, and other operating expenses (OOEs) – to distinguish four quadrants based on efficiency and profit to offer different strategies to the restaurateur under study; to compare the proficiency levels of the different meal categories of the à la carte and combo set menus using the metatechnology ratio (MTR) via the MDEA; and to use slack‐based analyses with simulation to improve the financial performance of a teppanyaki‐style restaurant.

Six months of point of sale (POS) data are obtained from a teppanyaki‐style restaurant. The proposed inputs are categorized into total food costs, total labor cost, the number of processes, and OOEs. Two outputs (total revenue and gross profit) are used to assess the efficiency of the menu items. The MTR is used to differentiate the proficiency level of the heterogeneous meal categories and to create four quadrants based on the efficiency index and financial performance.

The MTR is lower for the combo set category than for the à la carte category. Four quadrants are obtained based on the efficiency and financial performance to provide further menu suggestions. The MDEA analysis yields menu suggestions that could enhance the overall efficiency and profitability of the menu items. A simulation using these two models is conducted and shows that the restaurant profitability would be 22 percent greater using the MDEA than using the menu engineering model.

Because there are no publicly listed teppanyaki‐style restaurants in Taiwan and it is difficult to find the same menu in different restaurants, this study consists of only a single restaurant, and the results may not be generalizable to other types of restaurants.

This paper contributes to menu analysis by establishing an efficiency index and using financial performance as criteria for determining which menu items to improve in a teppanyaki‐style restaurant. The MTR of the metafrontier model can differentiate the proficiency level of the heterogeneous categories, such as à la carte and combo set menus. This paper offers empirical results pertaining to the classification of menu items and describes a slack‐based analysis for improving menu items.

Discusses an ongoing programme, the overall purpose of which is to develop an integrated set of stochastic simulation models of firms in the textile, apparel and retail industries. The models are to be used primarily for research, education and industrial problem solving in the areas of plant and company operations with emphasis on Quick Response (QR) methodologies driven by Point‐of‐Sale (POS) data. A second objective is that of developing interactive management information systems using high‐level decision‐surface models (simulation metamodels) of expected system performance as a function of key input parameters.

Documents two case studies conducted in Australian companies from the wholesaling sector. Both companies are importers and wholesalers supplying into the retail sector. The aim of the case studies was to establish to what extent each had adopted and implemented new technologies for the management of supply chain activities. Although both companies have approached implementation of established technologies such as barcoding and EDI from different perspectives some common themes emerge. The picture that emerges from the two cases is that although new technology may be adopted at varying levels, and with different objectives, the right technologies provide opportunities for all players. In both cases the services provided for retailers (either large or small) were important, and it was hard to see that they could be easily and efficiently replaced solely by applying new technology. At the same time, both companies saw technology as providing opportunities to grow their businesses, and to add value to the total supply chain.