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We develop a model that jointly optimizes a retailer's decisions for product prices, display facing areas, display orientations and shelf-space locations in a product category. Unlike the existing shelf-space allocation models that typically consider only the width of display shelves, our model considers both the width and height of each shelf, allowing products to be stacked. Furthermore, as demand is influenced by each product's two-dimensional facing area, we consider multiple product orientations that capture three-dimensional product packaging characteristics. That enables our model to not only treat shelf locations as decision variables, but also retailers’ stacking patterns in terms of product display areas and multiple display orientations. Further, unlike the existing studies which consider a retailer's shelf-space allocation decisions independent of its product pricing decisions, our model allows joint decisions on both and captures cross-product interactions in demand through prices. We show how a branch-and-bound based MINLP algorithm can be used to implement our optimization model in a fast and practical way.

PurposeThe purpose of this paper is to achieve a collective understanding of the capabilities required for digital procurement transformation (DPT).Design/methodology/approachThe authors contextualize theory about dynamic capabilities (DCs) to integrate the fragmented body of literature on procurement digitalization by means of a systematic literature review (SLR). By extracting and clustering capabilities, as well as proven performance outcomes from existing literature in the field, the authors develop a conceptual model of the DCs required for DPT.FindingsThe authors first introduce and define DPT and the corresponding motivations that trigger firms to invest in advanced digital technologies. Second, by adopting the DC lens, the authors provide an overview of nine microfoundations required for DPT and highlight the strategic options procurement leaders can use when strategizing about adopting combinations of digital technologies. Third, the authors present a future research agenda on DCs for DPT.Research limitations/implicationsThe developed conceptual model must be verified and enhanced through further empirical research.Practical implicationsThe conceptual model can be used by procurement leaders as a starting point and framework when strategizing about digitally transforming the procurement organization.Originality/valueThe study is the first to synthesize previous research findings on procurement digitalization through an SLR in order to develop a fine-grained conceptual model that supports practitioners and researchers alike in better understanding the capabilities required for and potential performance outcomes of DPT.

Environmental consciousness has become increasingly important in everyday life and business practice. The effort to reduce the impact of business activities on the environment has been labelled as green supply chain management. Any major greening project would require efforts on the part of the entire supply chain. However, very few studies have addressed the issue of coordinating the green supply chain. We consider the problem of coordination of a manufacturer and a retailer in a vertical supply chain, who put in efforts for 'greening' their operations. We address some pertinent questions in this regard such as extent of effort in greening of operations by manufacturer or retailer, level of cooperation between the two parties, and how to coordinate their operations in a supply chain. The greening efforts by the manufacturer and retailer result in demand expansion at the retail end. The decision variables of the manufacturer are wholesale price and greening effort, while those of the retailer are retail price and its greening effort. We find that the ratio of the optimal greening efforts put in by the manufacturer and retailer is equal to the ratio of their green sensitivity ratios and greening cost ratios. Further, profits and efforts are higher in the integrated channel as compared to the case of the decentralized channel. Finally, a two-part tariff contract is found to produce channel coordination in this problem. A numerical example illustrates the results.

Human–machine interaction is an active area of research due to the rapid development of autonomous systems and the need for communication. This review provides further insight into the specific issue of the information flow between pedestrians and automated vehicles by evaluating recent advances in external human–machine interfaces (eHMI), which enable the transmission of state and intent information from the vehicle to the rest of the traffic participants. Recent developments will be explored and studies analyzing their effectiveness based on pedestrian feedback data will be presented and contextualized. As a result, we aim to draw a broad perspective on the current status and recent techniques for eHMI and some guidelines that will encourage future research and development of these systems.

Product categories are more than classification devices that organize markets; when reflecting market actors' purposes, they are also judgment devices. Taking stock of the literature on product categories and drawing on the distinction between the faculties of knowing and judging, we elaborate a framework that accounts for how and why market actors include or exclude normative attributes in a product category definition. Based on a field study of the development of socially responsible investment (SRI) funds in France, we describe the phases and conditions of a judgment framework for category definition for both established and nascent categories. We discuss implications for research on product categories and the workings of markets more broadly.

Purpose – The purpose of this paper is to investigate the effects of implementing enterprise resource planning (ERP) systems and the selection of ERP vendors on supply management performance for Fortune 500. Design/methodology/approach – The paper adopts the supply chain maturity model adopted by Gupta and Handfield (2011) and used publicly available information such as articles, research report, newspapers to develop objective maturity ratings for four key indicators – strategic sourcing, category management, and supplier relationship management. Findings – The analysis results suggest ERP users are more mature than non-ERP users in three key indicators: strategic sourcing, category management, and supplier relationship management. Moreover, SAP ERP users are more mature than non-ERP users in strategic sourcing, category management, and supplier relationship management. Research limitations/implications – This study does not account for the longitudinal performance of ERP systems, nor does it account for differences between organizational scope of ERP deployment, global reach, or implementation duration. The authors also did not include other measures of supply chain performance outside of the procurement area. These factors could provide further insights to supply chain performance, and will be an interesting topic for future research. Practical implications – This study provides an extensive analysis of how the deployment of ERP systems and the selection of ERP vendors can benefit a company’s supply chain performance. This information is valuable for companies that are considering adapting an ERP system. Originality/value – This paper uses innovative an maturity assessment rating approach with publicly available resources to measure supply management performance across different companies. This method is novel and provides valuable insights to how ERP systems and their vendors’ impact supply chain management performance.

Analysis of prevalent document management practices shows the popular use of categories (e.g., folders) to organize documents for subsequent searches and retrievals. The coherence and distinction of an existing document category can diminish considerably as influxes of new documents arrive over time. The complexity of and effort requirements for document-category management favor an automated approach that can be supported by appropriate document-clustering techniques. A review of the extant literature shows a predominant focus on document content analysis in automated document-category management, which cannot preserve the user's document-grouping preferences. This research develops two advanced evolution-based techniques for preserving user preferences in their management of document categories. The first technique (CE2), which supports the automated evolution of a set of flat (i.e., nonhierarchical) document categories, extends a promising evolution-based technique (category evolution, CE) by addressing its fundamental limitations inherent to the use of holistic measures. The second technique, category hierarchy evolution (CHE), is developed on the basis of CE2 to support scenarios where document categories are organized with a hierarchical structure. Empirical evaluations of the effectiveness of each technique in various category evolution scenarios created using two different document corpora (i.e., news documents from Reuters and research articles from the ACM digital library), as compared with those of associated salient techniques for benchmark purposes, show that CE2 and CHE outperform their respective benchmark techniques. Their performance is reasonably robust and appears more effective when the quality (coherence) of the previously created categories does not deteriorate excessively. According to our results, the evolution-based approach is viable, appealing, and capable of preserving user preferences in automatic reorganizations of document categories.

[Display omitted] •Machine learning performs comparably to velocity models for distribution and market share.•Weighted random forests accurately predict 83% of under- and overperforming SKUs (in the velocity model).•Reasons for under- or overperformance are suggested by the algorithm (the most important SKU features for prediction).•The two most important features for prediction are store size and parent brand performance. This research presents a retail analytics application which uses machine learning (ML) to identify and predict under- and overperforming consumer packaged goods (CPGs) using retail scanner data. Essential to measuring market performance at the SKU level is the relationship between distribution and market share (the velocity curve). We validate that ML can reproduce the velocity curve, and ML is further used to predict underperforming, in-line performing, and overperforming SKUs relative to the velocity curve, based on a range of variables (SKU features) at a point in time. Our ML approach can correctly predict 83% of SKUs as under-, in-line-, or overperforming based on their characteristics. The research analyzes 9,321 SKUs of 2,565 brands across seven product categories of CPGs which were sold in 8,117 stores from 49 different retail chains of five different retail channels located in the US states of California, New York, Texas, and Wisconsin. The retail stores comprise convenience stores, drug stores, food stores, liquor stores, and mass merchandise retail stores. The data is Nielsen retail store scanner data for the calendar year 2014. The relationship between distribution and market share is a market-wide proxy for the ratio of relative sales in a category to, for example, aggregate shelf space, a key retail productivity metric. We further find indications that the distribution of SKUs across different store sizes, the stores’ category specialization, the line length of the brands, the overall performance of the parent brand, and sales consistency are the most important characteristics for the prediction of market share performance beyond the velocity curve. The methods and results presented will help CPG marketers (suppliers and retailers) understand which SKUs are under-, in-line-, or overperforming and the potential factors contributing to that performance. Optimizing assortments and portfolios is essential to decrease failure rates of individual SKUs. ML approaches can evolve to complementary support tools for such management problems.

Assortment “variety” is a major factor influencing consumer attraction. Brands and firms use five assortment management strategies to improve consumers’ perception of variety (size, composition, arrangement, visual layout, and textual information). This paper breaks down Perceived Assortment Variety (PAV) into its constituent parts (numerosity and diversity) and investigates the effect of each strategy on each component of PAV through meta-regressions on experimental studies (61 papers, 135 studies, cumulative N  = 66,638). Assortment size increase has a linear positive effect on perceived numerosity, but a positive and marginally decreasing effect on perceived diversity. In contrast, arrangement and visual layout have a negative and marginally increasing effect on perceived diversity, and a stronger effect than assortment size increase on diversity perception above 38 and 58 items respectively. These findings have practical and theoretical implications for assortment management. A supplementary analysis on 44 non-experimental papers extends the results for PAV to behavioral outcomes.

Purpose This paper aims to conceptualize supply market orientation (SMO) for the purchasing and supply chain management function and discusses how SMO capabilities are developed and how their application differs within and across firms. This research can thus be used as a blueprint for the development of a SMO capability that accommodates a firm’s unique contextual antecedents’ profile. Design/methodology/approach The qualitative research design comprises five in-depth case studies with 43 semi-structured interviews with large manufacturing and service firms. Findings SMO is defined as the capability to exploit market intelligence to assess, integrate and reconfigure the heterogeneously dispersed resources in purchasing and supply chain management in a way that best reflects the peculiarities of a firm’s supply environment. The empirical analysis shows that although SMO capabilities are configured similarly, their application varies across and within firms depending on the characteristics of a firm’s purchasing categories and tasks. Hence, reactive versus proactive SMO application is contingent upon firm-level and purchasing category–level characteristics. Originality/value The study uses the dynamic capabilities view as a theoretical background and provides empirical evidence and theoretical reasoning to elaborate and endorse SMO as a dynamic capability that firms need to have to compete in a complex and dynamic environment. The study provides guidance for supply chain managers on how to successfully develop and deploy a SMO capability.