Integrated Optimization Strategies for Enhanced Coffee Retail Store Efficiency with DEA Variants, Taguchi Signal to Noise, and Randomized Block Design
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Abstract
By integrating Data Envelopment Analysis (DEA) variants, Taguchi, and randomized block design techniques, this research addresses a substantial deficiency in the existing body of knowledge by evaluating the collective performance of coffee retail establishments situated on university campuses. The research considers undesirable output such as customer complaints by conducting an analysis of five operational inputs of OPEX, CAPEX, staff count, sitting capacity, and shop size with two desirable outputs of cup production and total income. DEA and its derivatives, super efficiency CCR and BCC were extremely efficient. The BCC infeasible efficiency score was improved by incorporating a modified super-efficiency BCC. The investigation further enhances the methodology for determining the optimal DecisionMaking Units (DMUs) among various DEA variations by incorporating Taguchi signal-to-noise and randomized block design as additional components. The most effective decision-making units (12, 9, and 5) demonstrated consistent and outstanding performance in all three DEA variants, as indicated by the results that succinctly outline the key results. Conversely, DMUs 2, 10, and 14 have been identified as prospective improvement areas.
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