Integrating operations and marketing decisions using delayed differentiation of products and guaranteed delivery time under stochastic demand

• Price-lead time-inventory trade-off with product variety and uncertain demand.
• The model is a two-stage, stochastic, integer, non-linear programming problem.
• Demand correlation, lead time & price sensitivity of demand, semi-finished goods.
• Solve large problem instances based on variant of Benders’ Decomposition method.
• Improved cost and lower bound over Classic Benders’ and Non-linear B&B methods.

In this research, we integrate the issues related to operations and marketing strategy of firms characterized by large product variety, short lead times, and demand variability in an assemble-to-order environment. The operations decisions are the inventory level of components and semi-finished goods, and configuration of semi-finished goods. The marketing decisions are the products price and a lead time guarantee which is uniform for all products. We develop an integrated mathematical model that captures trade-offs related to inventory of semi-finished goods, inventory of components, outsourcing costs, and customer demand based on guaranteed lead time and price.The mathematical model is a two-stage, stochastic, integer, and non-linear programming problem. In the first stage, prior to demand realization, the operation and marketing decisions are determined. In the second stage, inventory is allocated to meet the demand. The objective is to maximize the expected profit per-unit time. The computational results on the test problems provide managerial insights for firms faced with the conflicting needs of offering: (i) low prices, (ii) guaranteed and short lead time, and (iii) a large product variety by leveraging operations decisions.We also develop a solution procedure to solve large instances of the problem based on an accelerated version of the Generalized Benders’ Decomposition (GBD) method. The accelerating mechanism involves search intensification and diversification around solutions which improve the upper bound. The suggested GBD method gives a better solution and a tighter lower bound in a given time period than the conventional GBD implementation and the non-linear branch-and-bound method.