Real-Time Labour Allocation in Retail Stores
Aligning staff with changing customer and store needs is key to retail store operations management. Despite considerable research on workforce management at the planning stage, there is little research on the common phenomenon of Real-Time Labour Allocation (RTLA), where mismatches between workforce supply and demand are addressed by allocating potentially cross-trained employees in real time. Discussions with retail practitioners confirm that RTLA decisions lack analytical justification. Thus, this thesis aims to address the following research questions: (1) what is the current status of research on retail store operations? (2) what is the impact of RTLA practice on store performance? and (3) how can RTLA decisions be improved in retail stores? Three original papers are provided in the thesis. Paper I provides a comprehensive review of the literature on retail store operations. The paper reviews 255 articles on retail store operations from 32 operations research, management science, retailing, and general management journals from the period 2008–2016. The paper identifies several research gaps and proposes opportunities for advancing retail research expertise in the operations management community. Paper II provides a simulation-based study to quantify the impacts of RTLA decisions. This paper designs a generalisable stylised Retail Store Simulator (RSS) and instantiates the RSS using data from a gourmet supermarket. Simulation results show substantial long-term benefits to store performance from RTLA – a potential 6.6% increase in market share compared with No-RTLA. The RSS is also applied to answer a question from the collaborating retailer – “given the benefits of RTLA, how should we manage workforce flexibility?” Extensive “whatif” analysis in the case shows broadening employee skill range and deepening employee proficiency generate increased benefits for RTLA. Paper III presents a dynamic programming model that analyses RTLA decisions allocating store associates among departments (or store areas) in real time. Approximate Dynamic Programming (ADP) techniques are then employed to deal with the curse of dimensionality and provide forward-looking solutions. ADP-based solutions are compared with a Static policy (retaining the initial schedule), and a Myopic policy (maximising contributions stage by stage), using extensive computational experiments. The results show that ADP-based solutions increase store performance in many different settings. Overall, this thesis provides a deeper understanding of retail store operations as well as RTLA decision-making in retail stores.
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