Measuring efficiency in retail planning

Marais, Kurt (2019-04)

Thesis (MCom)--Stellenbosch University, 2019.

Thesis

ENGLISH SUMMARY : Efficiency is the measure of how well a process performs, and businesses are constantly looking for ways to improve their productivity. Traditional performance measures are commonly used and applied to data, but often do not consider the effect that multiple inputs and outputs have on the performance of a service unit. Thus, it is important to measure efficiency within the current capabilities of service units. One way to measure the capabilities of efficiency is through benchmarking, which identifies best-practice service units and compares all service units to the best practices. The benchmarking tool used in this study that embodies this notion is known as data envelopment analysis. Data envelopment analysis (DEA) is a linear programming tool used to determine relative efficiency for a group of service units and provides a score on the level of efficiency relative to other service units. DEA is applied to the data of a prominent South African retailer, and multiple DEA models are applied to the data to provide insight into the efficiency of service units for the considered retailer. Numerous extensions and adaptations of DEA have been developed to provide deeper insights into the efficiency of service units, depending on the available data. The CCR model and the BCC model are the main DEA models used in this thesis. Multiple regression analysis is also performed on the efficiency scores of DEA and the information that the models require. Important components for DEA are the decision of inputs and outputs, as well as the number of service units considered at one time, all of which have an effect on the discriminatory power of the models. The data are grouped into categories and DEA is run on these groups to better understand the results that DEA provides. The efficiency scores from the different models are determined for each of the considered service units order for the retailer to make decisions on minimising resources or maximising its outputs in future. DEA is not only a diagnostic tool for determining where inefficiencies exist, but how these inefficiencies should be approached, relative to best-practice units. DEA results were applied to data of 1 207 stores over 26 weeks, and it was identified that new fashion products generally perform better than older products. Regression analysis used for productivity measurement, while better for statistical analysis when compared to DEA, is limited in its ability to calculate efficiency for multiple inputs and multiple outputs at once. The results also provide confirmation on the discriminatory power of the choice of components used in DEA, and that isolating one component as a measure of efficiency is not enough for service units, since performance is dependent on multiple factors. The overall result is that DEA be used in tandem with other performance measures to diagnose where inefficiencies occur, and use the information of DEA to move towards improved productivity.

AFRIKAANSE OPSOMMING : Doeltreffendheid is die mate van hoe goed ‘n proses verrig word, en besighede soek voortdurend maniere om hul produktiwiteit te verbeter. Tradisionele prestastiemaatsawwe word algemeen gebruik en toegepas op data, maar beskou dikwels nie die effek wat verskeie insette en uitsetter op die prestasie van ‘n diesneenheid het nie. Dit is dus belangrik om doeltreffendheid binne die huidige vermoëns van dienseenhede te meet. Een manier om die vermoëns van doeltreffendheid te meet, is deur middel van maatstafmetodes, wat beste dienseenhede identifiseer en alle dienseenhede vergelyk met die beste pratyke. Die maatstafmetode wat gebruik word in hierdie studie, staan bekend as data-omhullingsanalise. Data-omhullingsanalise (DEA) is ‘n lineêre programmeringsinstrument wat gebruik word om relatiewe doeltreffendheid vir ‘n groep dienseenhede te bepaal en bied ‘n telling op die vlak van doeltreffendheid relatief tot ander dienseenhede. DEA word toegepas op die data van ‘n prominente Suid-Afrikaanse kleinhandelaar en verskeie DEA-modelle word op die data toegepas om insig te gee in die doeltreffendheid van dienseenhede vir hierdie kleinhandelaar. Verskeie uitbreidings en aanpassings van DEA is ontwikkel om die doeltreffendheid van dienseenhede beter te verstaan, afhangende van die beskikbare data. Die CCR-model en die BCC-model is die hoof DEA-modelle wat in hierdie studie gebruik word. Meervoudige lineêre regressie analise word ook uitgevoer op die tellings en die inligting wat die modelle benodig. Belangrike komponente vir DEA is die besluit van insette en uitsette, sowel as die aantal dienseenhede wat op ‘n slag oorweeg word. Hierdie komponente het ‘n uitwerking op die diskriminerende krag van die modelle. Die data word in kategorieë gegroepeer en DEA word op hierdie groepe uitgevoer om die resultate beter te verstaan. Die tellings van die verskillende modelle word bepaal vir elkeen van die oorweegde dienseenhede sodat die handelaar besluite kan neem oor die vermindering van hulpbronne of die maksimering van sy uitsette in die toekoms. DEA is nie net ‘n diagnostiese hulpmiddel om te bepaal waar ondoeltreffendheid bestaan nie, maar ook hoe om hierdie ondoeltreffendheid te benader, in vergelyking met doeltreffende dienseenhede. DEA resultate is toegepas op data van 1 207 winkels oor 26 weke, en dit is bepaal dat nuwe modeprodukte oor die algemeen beter presteer as ouer produkte. Regressie-analise wat gebruik word vir produktiwiteitsmeting is beperk in die vermoë om effektiwiteit vir verskeie insette en veelvoudige uitsette gelyktydig te bereken, alhoewel dit beter is vir statistiese analise in vergelykig met DEA. Die resultate bied ook bevestiging van die diskriminerende krag van die keuse van komponente wat in die DEA gebruik word, en dat all komponente as ‘n mate van doeltreffendheid beskou moet word, aangesien prestasie afhanklik is van die verskeie komponente. Die algehele resultaat is dat DEA saam met ander prestasiemaatstawwe gebruik word om ondoeltreffendheid te indentifiseer, en om die inligting van DEA te gebruik om produktiwiteit te verbeter.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/105847
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