Inventory rationing and sharing in pre-sell distribution with mobile communication technologies

The recent years show an ever increasing adoption of mobile communication technologies for pri- vate, public and business use. Their increasing acceptance by the companies has driven the diffusion of mobile business practices and the emergence of the enterprise mobility concept. The main advantages of the technology are commonly seen in gaining more process visibility and control, achieving process acceleration, and implementing a faster and less error-prone information processing.
Unlike much of the scholarly research on the m-business area, the presented work takes an op- erational research perspective to study economic implications of employing a mobile technology in business processes. The focus of the work is namely on the application of mobile technologies in pre-sell distribution—the method of goods distribution in which physical deliveries are executed first upon the delivery volumes become arranged with the customers by the traveling salesforce. Due to the ever increasing product proliferation, this distribution mode has gained in the recent decades more popularity, whereas salespeople are often committed to quote delivery dates and quantities to the customers. Mobile technology is considered an important enabler of the pre-sell method due to the capability of real-time information interchange between the salesmen and the back-office: firstly, the technology can facilitate a real-time visibility into corporate inventory from the field, and, secondly, provide the back-office with customer orders and other market data without delays. The main research questions addressed in the work are accordingly: How can the inventory be optimally allocated by the salesmen to the customers? What is the economic value of employing mobile technologies to fulfill this task, and how does it depend on the operating conditions of the company?
To this end, a model company is considered whose salesmen reveal customer demands in several geographic territories and allocate delivery volumes from the corporate stock, what may involve stock rationing: i.e., postponing to fill a particular customer demand—fully or partially, if at all—to some later point in time, in order to conserve the stock for meeting yet possible demands of a higher priority. Two operational settings are then introduced: the one without mobile technologies in use (non-mobile) and the one with them (mobile). In the non-mobile setting, the salesmen are not interconnected and can only work separately from each other. In the mobile setting, the salesmen and the back-office stay interconnected and can exchange demand observations/stock allocation data and so achieve a more efficient stock utilization. The problem of determining an optimal allocation policy is stated in either of the two settings as a multi-stage stochastic program. It is being formally shown that the mobile setting gains advantage over its non-mobile counterpart in terms of the company’s maximal expected performance. This is due to the following two important considerations. Firstly, the mobile setting enables stock sharing between the territories, so featuring the well-known effect of inventory pooling. Secondly, it gives rise to the effect of information sharing when customer demands are dependent across territories—by letting the salesmen implement more informed stock rationing.
Two different operating objectives are considered in the work: maximization of the customer- average product availability, and maximization of the sales profit subject to a service-level constraint. For each objective, a stochastic dynamic programming approach is adopted to develop a solution method that delivers the optimal allocation policies and expresses the expected performance as the function of the inventory level, in both mobile and non-mobile settings. This, in turn, leads to capturing the benefit of employing a mobile technology by the traveling salesforce—in terms of the respective performance measure—merely as a difference of two functions or their maximal values. Furthermore, under each of the two objectives, the form of the optimal allocation policies is established which turns out to be simple and easily interpretable, being characterized by a system of critical reserve levels.
A series of computational experiments run for selected configurations of distribution systems ex- hibits the composition of optimal policies and delivers insights into the drivers of benefits enabled by the mobile setting and the individual contributions of pooling and rationing to the overall benefit.