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Open Access
Article
Publication date: 15 December 2023

Nicola Castellano, Roberto Del Gobbo and Lorenzo Leto

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on…

Abstract

Purpose

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

Design/methodology/approach

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

Findings

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

Practical implications

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

Originality/value

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Details

International Journal of Productivity and Performance Management, vol. 73 no. 11
Type: Research Article
ISSN: 1741-0401

Keywords

Article
Publication date: 9 May 2016

Chao-Lung Yang and Thi Phuong Quyen Nguyen

Class-based storage has been studied extensively and proved to be an efficient storage policy. However, few literature addressed how to cluster stuck items for class-based…

2509

Abstract

Purpose

Class-based storage has been studied extensively and proved to be an efficient storage policy. However, few literature addressed how to cluster stuck items for class-based storage. The purpose of this paper is to develop a constrained clustering method integrated with principal component analysis (PCA) to meet the need of clustering stored items with the consideration of practical storage constraints.

Design/methodology/approach

In order to consider item characteristic and the associated storage restrictions, the must-link and cannot-link constraints were constructed to meet the storage requirement. The cube-per-order index (COI) which has been used for location assignment in class-based warehouse was analyzed by PCA. The proposed constrained clustering method utilizes the principal component loadings as item sub-group features to identify COI distribution of item sub-groups. The clustering results are then used for allocating storage by using the heuristic assignment model based on COI.

Findings

The clustering result showed that the proposed method was able to provide better compactness among item clusters. The simulated result also shows the new location assignment by the proposed method was able to improve the retrieval efficiency by 33 percent.

Practical implications

While number of items in warehouse is tremendously large, the human intervention on revealing storage constraints is going to be impossible. The developed method can be easily fit in to solve the problem no matter what the size of the data is.

Originality/value

The case study demonstrated an example of practical location assignment problem with constraints. This paper also sheds a light on developing a data clustering method which can be directly applied on solving the practical data analysis issues.

Details

Industrial Management & Data Systems, vol. 116 no. 4
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 1 May 1998

Brenna O’Roarty, Stanley McGreal and Alastair Adair

This paper utilises cluster analytical techniques to examine the relationship between store space requirements, micro‐style property selection and retail function. Analysis of…

1607

Abstract

This paper utilises cluster analytical techniques to examine the relationship between store space requirements, micro‐style property selection and retail function. Analysis of survey data infers that retail function is the most important dimension in determining retailers’ behaviour with respect to store space requirements. Suggests that variation across a range of factors pertinent to the valuation of shop premises cannot be explained by store space requirements. Concludes that application of unit area values derived from the comparison of properties of different size and layout in any assessment of retail rental values is potentially flawed.

Details

Journal of Property Valuation and Investment, vol. 16 no. 2
Type: Research Article
ISSN: 0960-2712

Keywords

Article
Publication date: 23 October 2007

Mikael Hernant, Thomas Andersson and Olli‐Pekka Hilmola

The purpose of this study is to describe the determinants of profitability in terms of the strategic profitability model (the Du Pont model), depicting the “route” to high…

3513

Abstract

Purpose

The purpose of this study is to describe the determinants of profitability in terms of the strategic profitability model (the Du Pont model), depicting the “route” to high profitability in grocery retail stores located in market areas possessing dissimilar competitive conditions.

Design/methodology/approach

Different physical characteristics (e.g. store formats) have traditionally been used as control criteria, but it is argued in this paper that management principles in retail chains should be based on different clusters of stores, formed from local competitive conditions. The paper proposes a clustering method based on five indicators of local competition. The research results are derived from local competitive conditions and the performance of 168 supermarkets, located in Sweden, and controlled by one retail chain.

Findings

The paper identifies four clusters of local markets labeled monopoly, fleet market, venue, and duopoly, based on local competitive conditions. The findings show that the “route” to profitability significantly differs between the clusters. In monopoly the route to high profitability goes through high‐gross margin, while in fleet market the key figures are low cost, large number of shoppers per week, and high productivity. Venue and duopoly both gain from high‐average transactions per shopper.

Practical implications

Supermarkets under different competitive conditions have different critical success factors and would probably be better managed, supported and evaluated on a different basis, i.e. retail chains need to adjust their approach to their supermarkets depending on local competitive conditions.

Originality/value

Based on the findings the paper proposes unique management strategies for different clusters of local markets to further enhance current strength areas.

Details

International Journal of Retail & Distribution Management, vol. 35 no. 11
Type: Research Article
ISSN: 0959-0552

Keywords

Open Access
Article
Publication date: 14 March 2022

Luke McCully, Hung Cao, Monica Wachowicz, Stephanie Champion and Patricia A.H. Williams

A new research domain known as the Quantified Self has recently emerged and is described as gaining self-knowledge through using wearable technology to acquire information on…

Abstract

Purpose

A new research domain known as the Quantified Self has recently emerged and is described as gaining self-knowledge through using wearable technology to acquire information on self-monitoring activities and physical health related problems. However, very little is known about the impact of time window models on discovering self-quantified patterns that can yield new self-knowledge insights. This paper aims to discover the self-quantified patterns using multi-time window models.

Design/methodology/approach

This paper proposes a multi-time window analytical workflow developed to support the streaming k-means clustering algorithm, based on an online/offline approach that combines both sliding and damped time window models. An intervention experiment with 15 participants is used to gather Fitbit data logs and implement the proposed analytical workflow.

Findings

The clustering results reveal the impact of a time window model has on exploring the evolution of micro-clusters and the labelling of macro-clusters to accurately explain regular and irregular individual physical behaviour.

Originality/value

The preliminary results demonstrate the impact they have on finding meaningful patterns.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

Article
Publication date: 6 July 2020

Pei-Chun Lin, Chia-Jung Lin, Chung-Wei Shen and Jenhung Wang

The objectives of this study were to demonstrate that the high-density 7-Eleven c-stores in Taiwan benefit from economies of scale in distribution and can, therefore, leverage the…

Abstract

Purpose

The objectives of this study were to demonstrate that the high-density 7-Eleven c-stores in Taiwan benefit from economies of scale in distribution and can, therefore, leverage the logistics costs; and to decide the proper locations for the future inauguration of c-stores.

Design/methodology/approach

The study spatially analysed the c-stores located in Tainan, Taiwan and examines the influence of spatial configuration on c-store revenue. This study developed models to quantify the revenue and logistics costs that the 7-Eleven convenience store (c-store) chain encountered when adopting a high-density expansion strategy. The revenue models’ parameters were calibrated utilizing data collected from financial statements in 7-Eleven chains’ 2015 corporate annual reports and modelling was used to quantify the influence of agglomeration forces and the distance separating c-stores on revenue.

Findings

Positive agglomeration forces increased 7-Eleven’s company-wide sales and the average daily revenue of its individual c-stores, and decreased those of competitors. The study findings demonstrate the high-density 7-Eleven c-stores in Tainan benefit from economies of scale in distribution and can, therefore, leverage their logistics costs. The spatial analysis concluded that higher-density and higher-revenue c-stores were spatially clustered.

Originality/value

The study extends the use of analytical revenue and spatial models to decide the proper locations for the future inauguration of c-stores.

Details

International Journal of Retail & Distribution Management, vol. 48 no. 11
Type: Research Article
ISSN: 0959-0552

Keywords

Article
Publication date: 7 March 2018

Kalpana Sitaraman

This paper aims to provide marketers with a blueprint for designing winning point of sale (POS) strategies at the final moment of truth. It focuses on traditional trade (kirana…

Abstract

Purpose

This paper aims to provide marketers with a blueprint for designing winning point of sale (POS) strategies at the final moment of truth. It focuses on traditional trade (kirana stores), the most critical distribution channel in emerging markets such as India, and outlines a four-pillar strategy of shopper marketing for traditional trade.

Design/methodology/approach

The paper is based on and draws from experience and expertise built in the course of many years of specialized, on-field research in the area of shopper insights in India. It is supplemented with secondary research on retail and shopper trends in India and other developing markets.

Findings

The paper examines how traditional trade in India has evolved to meet the challenges of changing shopper preferences amidst the advent of modern trade. Keeping the common goal of serving customer and shopper needs effectively and efficiently, the paper details a four-pillar strategy of shopper marketing for traditional trade centered around segmenting store clusters; mining shopper insights at the store; leveraging the shopkeeper; and adopting a scientific, organized process of testing and redeploying POS initiatives.

Originality/value

This paper provides marketers and sales professionals with a structured and cohesive approach to designing POS strategies for traditional trade. While the paper is developed for the Indian market, the principles can also be easily adopted across other emerging markets where traditional trade is a dominant channel.

Details

Journal of Indian Business Research, vol. 10 no. 4
Type: Research Article
ISSN: 1755-4195

Keywords

Article
Publication date: 13 March 2017

Prem Chhetri, Booi Kam, Kwok Hung Lau, Brian Corbitt and France Cheong

The purpose of this paper is to explore how a retail distribution network can be rationalised from a spatial perspective to improve service responsiveness and delivery efficiency.

3153

Abstract

Purpose

The purpose of this paper is to explore how a retail distribution network can be rationalised from a spatial perspective to improve service responsiveness and delivery efficiency.

Design/methodology/approach

This paper applies spatial analytics to examine variability of demand, both spatially and from a service delivery perspective, for an auto-parts retail network. Spatial analytics are applied to map the location of stores and customers to represent demand and service delivery patterns and to delineate market areas.

Findings

Results show significant spatial clustering in customer demand; whilst the delivery of products to customers, in contrast, is spatially dispersed. There is a substantial gap between revenue generated and costs. Market area analysis shows significant overlap, whereby stores compete with each other for business. In total, 80 per cent of customers can be reached within a 15-minute-radius, whilst only 20 per cent lies outside the market areas. Segmentation analysis of customers, based on service delivery, also shows the prevalence of the Pareto principle or 80:20 rule whereby 80 per cent of the revenue is generated by 20 per cent of customers.

Practical implications

Spatially integrated strategies are suggested to improve the efficiency of the retail network. It is recommended that less accessible and unprofitable customers could be either charged extra delivery cost or outsourced without the risk of a substantial reduction in revenue or quality of service delivery.

Originality/value

Innovative application of spatial analytics is used to analyse and visualise unit-record sales data to generate practical solutions to improve retail network responsiveness and operational efficiency.

Details

International Journal of Retail & Distribution Management, vol. 45 no. 3
Type: Research Article
ISSN: 0959-0552

Keywords

Article
Publication date: 1 August 2016

Joanna Trafialek, Michal Zwolinski and Wojciech Kolanowski

– The purpose of this paper is to assess hygiene practices during fish selling in retail stores.

Abstract

Purpose

The purpose of this paper is to assess hygiene practices during fish selling in retail stores.

Design/methodology/approach

The data were collected by observations during inspections carried out in 100 randomly selected food retail stores, both independent and chain, selling fresh fish, fish products and other seafood. Stores were located in and around the area of Warsaw, Poland. The inspection check list consisted of 43 questions based on rigorist requirements of Commission Regulation (EC) 852, 853 and Codex Alimentarius. The question form was divided into three hygiene sectors: hygiene conditions of seafood departments; hygiene of fish selling process; personal hygiene of employees. Inspections were unannounced, and were conducted by discreet visual observations of employees work routine and selling procedures.

Findings

The level of hygiene compliances with inspection criteria was unexpectedly low. The highest percentage of compliance appeared in the hygiene of fish selling processes (in 44 percent of the stores compliance with evaluated criteria was found), less one compliance levels appeared in personal hygiene (18 percent) and hygiene of seafood department’s hygiene conditions (23 percent). Neither the size of the store, nor its location and type (independent and local or global chain) affected the compliance rate.

Research limitations/implications

The main research limitation is that assessment was done only by observation method. This is one of audit/inspection methods according to ISO 19011/2011, guidelines for auditing management systems. However, this kind of inspection cannot assess microbiological cleanliness or other like ATP or symptoms of diseases expect of only visible signs. The used inspection check list needs more testing and more analyses should be done for its reliability and validity.

Practical implications

Adequate hygiene practices are critical in preventing cross-contamination. However, none of the inspected stores ensured full implementation of all hygiene requirements during the sale of fish. The results indicated that a greater effort should be made to increase hygiene level both in small and large size retail stores. The designed inspection questionnaire proved to be a successful format for detailed evaluation of hygiene practices during the sale of fish. However, more work and analyses should be done for its reliability and validity.

Social implications

The findings bring some information for the consumers that in many retail stores the hygiene level during the fish sales might be insufficient.

Originality/value

The paper presents additional and detailed data on hygiene practices during fish selling, which are rarely pointed out by other authors. The applied evaluation method showed a low level of compliance with the rigorous hygienic criteria, adopted in this study, that may raise some food safety concerns.

Details

British Food Journal, vol. 118 no. 8
Type: Research Article
ISSN: 0007-070X

Keywords

Article
Publication date: 1 June 2003

Julian Bond and Leigh Morris

From an early age we categorise the myriad stimuli we are confronted with. This adaptive process continues throughout our lives, and finds a natural expression in marketers’…

2239

Abstract

From an early age we categorise the myriad stimuli we are confronted with. This adaptive process continues throughout our lives, and finds a natural expression in marketers’ desire to segment their consumers into different types. Argues that traditional attitudinal segmentations can prove disappointing when looking for differences between groups on behavioural or brand preference measures. Clusterwise regression, a form of latent class segmentation, offers an alternative approach to establishing meaningful market segments, and we present a case study using this technique within the health and beauty sector. Argues that the method’s emphasis on the importance of different attributes to different groups of people could be usefully incorporated into qualitative group methodology to produce meaningful and robust market segmentation.

Details

Qualitative Market Research: An International Journal, vol. 6 no. 2
Type: Research Article
ISSN: 1352-2752

Keywords

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