Alfred L. Guiffrida

This paper addresses the framing of a trip chaining product and details how an entity referred to as a third party (3P) tours n number of sites procuring client-desired products at each site on their behalf. An efficient and effective sequencing of site visits that meet customer needs and are within the 3P’s procuring resources is the product. Generally, the needs of more than one client and more than two site visits compose an itinerary. A scenario with n = 32 site visits and more than one touring vehicle is treated in detail. The presentation includes demonstrations of how marketing analytics assists the 3P in composing a trip chaining product with appealing features. Also addressed are confounding issues of procuring the desired items at sites whose inclusion in an itinerary would degrade the time and location availability for all fulfilled customer orders. Demonstration of perturbation analytics, trip chain bifurcation, alternative order availability sites for clients, and non-standard times and sites to begin and end the trip chaining is presented with real site data.

Cost–volume–profit (CVP) analysis is a widely used decision tool across many business disciplines. The current literature on stochastic applications of the CVP model is limited in that the model is studied under the restrictive forms of the Gaussian and Lognormal distributions. In this paper we introduce the Mellin Transform as a methodology to generalize stochastic modeling of the CVP problem. We demonstrate the versatility of using the Mellin transform to model the CVP problem, and present a generalization of the CVP model when the contribution margin and sales volume are both defined by continuous random distributions.