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Increased demand variability in supply chains (the bullwhip effect) has been discussed in the literature. The practical measurement of this effect, however, entails some problems that have not received much attention in the literature and that have to do with the aggregation of data, incompleteness of data, the isolation of demand data for defined supply chains that are part of a greater supply web. This paper discusses these conceptual measurement problems and discusses experiences in dealing with some of these problems in an industrial project. Also presents empirical results of measurements of the bullwhip effect in two supply chains.

In a changing and more dynamic market of the 1980s, available mathematical programming algorithms on which production planning in process industries was based did not provide the required responsiveness. Although it is suggested in the literature that a variable cycle times policy will enable the system to react to short‐term demand fluctuations, proposes the use of a fixed cycle times policy. Presents simulation results which show a considerable improvement in service level at a high level of utilization. Presents a three‐tiered hierarchical model which is based on this fixed cycle times policy, which gives insight into the influence that the demand manager has upon the results in the production department. On the other hand, it also reflects the commercial interests. Suggests that for both departments which are this strongly intertwined, a common reference is necessary to improve the general results of the business.

Discusses the variety of production control situations within process industries. Following a literature review, a typology is introduced which discriminates between two extreme types of process industries: batch/mix and process/flow businesses. Reviews the research in production and inventory control in each of the extreme types. Identifies a control framework for operations management in process/flow businesses. Notes that although detailed scheduling approaches for batch/mix businesses exist, a control framework for the latter is missing. Concludes that operations management in batch process industries needs considerable research attention.

The purpose of this paper is to understand the implications of outsourcing at the operational planning level, i.e. how the operational planning function is complicated owing to the strategic outsourcing decisions that have been made in the past.

First, a literature review on outsourcing is conducted. Second, two case studies at three pharmaceutical companies are conducted to gather insights into the planning of outsourced operations.

The paper finds that nothing has been documented in the literature on outsourcing at the operational planning level. Moreover, a number of implications of outsourcing at the operational planning level are discussed. One of the main insights is that in an outsourcing relationship, the order process consists of different, hierarchically connected, decisions in time, hence requiring a richer and more developed communication and ordering pattern than is commonly assumed.

The results seem to be generalizable to the pharmaceutical industry. However, future research should determine whether these results replicate in other industries.

The literature on outsourcing has mainly focused on the strategic outsourcing decision. The paper contributes to a better understanding of the implications of outsourcing at the operational planning function, which has not been studied before.

Gives an overview of quantitative model‐based research in operations management, focusing on research methodology. Distinguishes between empirical and axiomatic research, and furthermore between descriptive and normative research. Presents guidelines for doing quantitative model‐based research in operations management. In constructing arguments, builds on learnings from operations research and operations management research from the past decades and on research from a selected number of other academic disciplines. Concludes that the methodology of quantitative model‐driven empirical research offers a great opportunity for operations management researchers to further advance theory.

This chapter explores how customer's attributes, shopping behavior, and preferences affect the retail choice in fiercely competitive retail environments of megacities from developing countries. We study how small, family-owned retailers (i.e., nanostores) compete against organized chains from the modern channel (i.e., convenience stores and supermarkets) at different socioeconomic levels in 9 out of 16 boroughs from Mexico City. Primary data were collected using a combination of instruments (i.e., observation, interviews, and surveys) that were applied to relevant stakeholders of the retail footprint where nanostores develop their operations. We analyze the data via statistical tools such as descriptive statistics and independent nonparametric tests to understand the significant factors of the competitive landscape in which nanostores are immersed. We supplement our research methodology by using causal loop diagrams to identify opportunities in the way suppliers, shopkeepers, competitors, and customers interact with each other and new business models for the nanostore supply chains. By breaking down our result analysis into low-, middle-, and high-income areas, we provide insightful recommendations to increase nanostores' survival, improve their operations, and grow them in Mexico City by addressing issues from the supply, store management, and customer service.

Information sharing practices such as vendor‐managed inventory (VMI) give manufacturers access to more accurate demand information, e.g. customer sales data, than before. The value of this type of information sharing has been established in many studies. However, most of the research has focused on the ideal situation of the manufacturer having access to information from all downstream parties. In practice, this is rarely the case. In this paper, discrete‐event simulation is used to examine how a manufacturer can combine traditional order data available from non‐VMI customers with sales data available from VMI customers in its production and inventory control and what impact this has on the manufacturer's operational efficiency. The simulation model is based on a real‐life VMI implementation and uses actual demand and product data. The key finding is that even for products with stable demand a partial improvement of demand visibility can improve production and inventory control efficiency, but that the value of visibility greatly depends on the target products’ replenishment frequencies and the production planning cycle employed by the manufacturer.

The purpose of this paper is to investigate the antecedents of close supply chain collaboration and to develop a multi‐variable conceptual model of factors that drive the need for close supply chain collaboration.

A multi‐variable conceptual model is developed based on literature and on a series of dyadic mini‐cases in the electronics, fashion and consumer‐packaged goods industry.

This paper confirms that close supply chain collaboration is influenced by a multitude of factors. It reveals a need to integrate findings from analytical and empirical disciplines that study supply chain collaboration. The results suggest that collaborative initiatives are predominantly initiated with suppliers and not with customers, and that close supply chain collaboration may lead to inertia in business relations.

This paper is based on dyadic case studies in three different make‐to‐stock industries; future research may include a large‐scale survey in more industries, including both make‐to‐order and make‐to‐stock environments.

Based on the findings, firms can make better choices in their collaborative initiatives; based on the conceptual model, firms can identify potential areas of close supply chain collaboration.

Findings from analytical and empirical literature are combined and such a combined perspective is deployed for the first time into a conceptual model of drivers of close supply chain collaboration.

This paper aims to identify customer behavior with regard to out‐of‐stocks (OOS) of perishable products (focused on bakery bread) and the resulting inventory performance for these perishable products.

Insights on how consumers behave when their preferred bread product is OOS are derived based on 3,800 customer interviews performed in three stores of a large Dutch grocery retail chain. Next to this, additional logistical information was measured on regular moments with respect to the shelf availability per stock‐keeping unit during the day and to waste at the end of the day.

The customer behavior with regard to perishables is observed to be different from that for the non‐perishable items. The key observation is that customers have a high willingness to substitute. The incorporation of the obtained knowledge of the observed consumer buying behavior into the existing automated store ordering (ASO) systems is discussed. In the current ASO systems, no distinction is made between perishable and non‐perishable products, as it is primarily designed and used for the non‐perishables. The authors show that the current ASO can be enriched and extended by taking into account some extra crucial parameters which are based on the observed consumer behavior.

One common factor in the research papers published so far is that they primarily looked into the customer behavior for non‐perishable items. The current paper on‐hand extends these works towards perishable items with a focus on bakery bread.

This special “Anbar Abstracts” issue of Industrial Management & Data Systems is split into six sections covering abstracts under the following headings: Manufacturing strategy and production; Business process management ; Research and development; Industrial design; Quality management; Information systems.