Optimizing inventory׳s contribution to profitability in a regulated utility: The Averch-Johnson effect

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• 作者：Linda Li¹ ; ^{lli45@crimson.ua.edu" class="auth_mail" title="E-mail the corresponding author} ; David Miller ; ² ; ^{dmiller@cba.ua.edu" class="auth_mail" title="E-mail the corresponding author} ; Charles P. Schmidt³ ; ^{cschmidt@cba.ua.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Inventory management ; Utilities ; Public service commission (PSC) ; Efficiency ; Dynamic NDEA ; Averch&ndash ; Johnson effect
• 刊名：International Journal of Production Economics
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：175
• 期：Complete
• 页码：132-141
• 全文大小：717 K

文摘

The Averch–Johnson effect is a well-known economic phenomenon through which regulated utility companies prefer a capital resource input combination that differs from an efficient level. Through dynamic Network Data Envelopment Analysis (NDEA), this paper first investigates the existence of this effect on a dynamic input that is inventory. The analysis was performed for a group of public electric utilities from 2005 to 2014. The result implies that these firms procure a significant extra amount in inventory (materials and supplies) beyond their efficient levels. In order to explore the connection of this effect to profit motives, we construct an analytical model of the inventory policy controlling the extra buying of material and supplies that will be inventoried and added to the firm׳s rate base. The model is based on maximizing the net present value of total profits. In the deterministic case, two results are observed: i) the firm could achieve a finite optimum by adding a finite amount of inventory above the normal base level which assumes no impact of inventory on the rate hearing; and ii) the firm could theoretically achieve infinite optimum under the unconstrained model. The primary contribution of this paper is the special case of extending typical inventory replenishment models to including the impact on prices and in turn profits in a regulated supply chain.