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Hybrid method of Data Envelopment Analysis with Supervised Learning

Dear members of data science central,

I look forward for any suggestions from anyone, related to my paper about convenience store performance measurement.

Background Problems: Convenience stores have recently been a trend place of daily  necessities shopping for Indonesians. This condition boost the growth of convenience store’s numbers and encourage the management to improve its performance in order to face tight business competition, while the performance of convenience stores is actually determined by the efficiency of various product categories. In relation to this, the concept of benchmarking through Data Envelopment Analysis (DEA) is one of the well-known method used to measure company’s efficiency that can be utilized to measure firm performance. However, DEA has limitation in handling large amounts of data, but supervised learning technique can be used as an alternative method to overcome it.

Main Objectives: This study provide an integrated model that applies benchmarking concept and supervised learning technique to measure performance of convenience store by considering the efficiency of various product categories.

Novelty: This is the first study that  utilizes SVM algorithm besed on DEA for measuring performance of a local convenience store.

Research Methods: The proposed approach has several steps. First step, calculating efficiency score product categories using DEA method. Second step, using the effeciency score as class feature for the data set to train the SVM model through K-Fold 5 cross validation, then predicting the efficiency score based on the test set. Final step, evaluating the number of efficient and inefficient product categories to determine the performance of convenience store.

Conclusion: The proposed method has  been successfully established and proven valid in predicting efficiency of products category to measure convenience store performance. Furthermore, this present research indicates  that local convenience store has 39.4% inefficient product categories, while 60.6 % other product categories are efficient.

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