Today, technology enables companies to extend their reach in managing the supply chain and operating it in a coordinated fashion from raw materials to end consumers. Order promising and order fulfillment have become key supply chain capabilities which help companies win repeat business by promising orders competitively and reliably. In this dissertation, we study two issues related to moving a company from an Available to Promise (ATP) philosophy to a Profitable to Promise (PTP) philosophy: pseudo order promising and coordinating demand fulfillment with supply.
To address the first issue, a single time period analytical ATP model for n confirmed customer orders and m pseudo orders is presented by considering both material constraints and production capacity constraints. At the outset, some analytical properties of the optimal policies are derived and then a particular customer promising scheme that depends on the ratio between customer service level and profit changes is presented. To tackle the second issue, we create a mathematical programming model and explore two cases: a deterministic demand curve or stochastic demand. A simple, yet generic optimal solution structure is derived and a series of numerical studies and sensitivity analyses are carried out to investigate the impact of different factors on profit and fulfilled demand quantity. Further, the firm's optimal response to a one-time-period discount offered by the supplier of a key component is studied. Unlike most models of this type in the literature, which define variables in terms of single arc flows, we employ path variables to directly identify and manipulate profitable and non-profitable products. Numerical experiments based on Toshiba's global notebook supply chain are conducted. In addition, we present an analytical model to explore balanced supply. Implementation of these policies can reduce response time and improve demand fulfillment; further, the structure of the policies and our related analysis can give managers broad insight into this general decision-making environment
