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      Inventory Management for Mobile Money Agents in the Developing World
      26 Jun 2017Working Paper Summaries

      Inventory Management for Mobile Money Agents in the Developing World

      by Karthik Balasubramanian, David F. Drake, and Douglas Fearing
      Mobile money agents in the developing world face a key inventory management challenge: How much cash and e-float should be held to minimize both stockouts and excess working capital? The authors develop two inventory models and show substantial inventory cost reduction with a large dataset of East African mobile money transactions.
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      Author Abstract

      Mobile money systems, platforms built and managed by mobile network operators to allow money to be stored as digital currency, have burgeoned in the developing world as a mechanism to transfer money electronically. Mobile money agents exchange cash for electronic value and vice versa, forming the backbone of an emerging electronic currency ecosystem that has potential to connect millions of poor and “unbanked" people to the formal financial system. Unfortunately, low service levels due to agent inventory management are a major impediment to the further development of these ecosystems. This paper describes models for the agent's inventory problem, unique in that sales of electronic value (cash) correspond to an equivalent increase in inventory of cash (electronic value). This paper presents a base inventory model and an analytical heuristic that are used to determine optimal stocking levels for cash and electronic value given an agent's historical demand. When tested with a large sample of transaction-level data provided by an East African mobile operator, both the base model and the heuristic improved agent profitability by reducing inventory costs (defined here as the sum of stockout losses and cost of capital associated with holding inventory). The heuristic increased estimated agent profits by 15% relative to profits realized through agents’ actual decisions, while also offering substantial computational advantages relative to the base model.

      Paper Information

      • Full Working Paper Text
      • Working Paper Publication Date: June 2017
      • HBS Working Paper Number: HBS Working Paper #17-109
      • Faculty Unit(s): Technology and Operations Management
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