Dimitris A. Papakiriakopoulos

Collaborative performance measurement in supply chain

Purpose – The objective of this paper is to demonstrate the challenges when developing a common performance measurement system (PMS) in the context of a collaborative supply chain.Design/methodology/approach – The paper utilizes qualitative and quantitative data from a case study. The qualitative data refer to the assessment of collaborative performance measures based on interviews with experts, while the quantitative data demonstrate the use of two performance measures in a collaborative supply chain network.Findings – The development of a collaborative PMS is a challenging task. Through the systematic study of two significant performance measures for a supply chain, it was found that the one could not be supported due to reliability restrictions, while the other requires the development of a complex information system. Based on these, a discussion of specific challenges follows.Research limitations/implications – The paper has the general case study limitations.Practical implications – Companies operati...

Warehouse contextual factors affecting the impact of RFID

Purpose – Empowered by the possibility to automatically identify unique instances, radio frequency identification (RFID) is expected to revolutionize warehouse processes. However, every warehouse differs from each other in several ways. Given such dimensionality, a credible assessment of the true value of RFID requires that the contextual factors that differentiate one warehouse from another are taken into account. The same RFID implementation may generate high productivity in one warehouse but not in another, because the former warehouse may have characteristics that may influence the impact of RFID. As a result, the purpose of this paper is to provide a framework for identifying key contextual factors that appear to be contingent on the link between RFID and warehouse performance.Design/methodology/approach – The framework derived from a two‐phase research design. The first phase incorporated two case studies. This was an exploratory study and, therefore, there was a great deal of iteration between the ...

Classification Performance for Making Decisions about Products Missing from the Shelf

The out-of-shelf problem is among the most important retail problems. This work employs two different classification algorithms, C4.5 and naive Bayes, in order to build a mechanism that makes decisions about whether a product is available on a retail store shelf or not. Following the same classification methods and feature spaces, we examined the classification performance of the algorithms in four different retail chains and utilized ROC curves and the area under curve measure to compare the predictive accuracy. Based on the results obtained for the different retail chains, we identified certain approaches for the development and introduction of such a mechanism in different retail contexts.

Performance Measurement in Supply Chain Networks

In the area of Management Control Systems research efforts focus primarily on the development of performance measurement systems for a single organization. However, collaboration practices create common areas of interest among organizations. To this end, integrated performance measures are required. In a collaborative context facilitated by an IT infrastructure, organizations have the opportunity to develop a common performance measurement system. This chapter briefly presents some widely discussed theoretical issues in the area of performance measurement as well as some practical considerations. Based on case study research in the area of Fast Moving Consumer Goods, I present a concise method to realize which performance measures are available on the top of the IT infrastructure. At the same time, the chapter addresses the challenges and problems of a single performance measure, showing that the measurement method is important although sometimes complex.

Framing Customer Shopping Behavior Through Retail Data Analytics

Retailers have long recognized the importance of business analytics methods for facilitating more effective customer-related decisions that result in added-value customer service. We recommend a retail data-driven approach that reveals a wealth of consumer shopping behavioral insights. We employ clustering techniques to segment the customer visits. The available customer visit segmentation studies view shoppers and their visits just as associations of product items. On the contrary, we discover patterns in the shoppers’ shopping habits to comprehend the customers’ behavior behind their visits and sales. Our approach segments customer visits and translates them to customer shopping behavior. We demonstrate the utility of this approach by applying it to real data provided by a Fortune 500 specialty retailer of home improvement and construction products. Apart from its theoretical contribution, our approach extracts knowledge that may support several decisions ranging from marketing campaigns per customer segment, redesign of store’s layout to product recommendations.