Profit Maximization Modeling for Supply Chain Planning

Introduction

In a large firm with a presence spanning multiple countries, one often finds an organization consisting of many different manufacturing and distribution units. Developing integrated strategic, tactical and operational manufacturing and distribution plans for the global supply chain of such a firm represents a formidable planning, as well as organizational undertaking. Moreover, to develop and execute plans that are not only integrated, but which maximize profits on a global basis presents a challenge of far greater magnitude. The use of advanced optimization modeling based analytics can generate keen insights and guidance for management decisions regarding sourcing, production, distribution, inventory and demand management on supply chain networks. The use of these techniques can bring clarity to the complex decisions that make integrated manufacturing and distribution planning both difficult and important (Shapiro, 2010).

For purposes of this chapter, we will define “optimization modeling based analytics” as the utilization of mathematical optimization models to provide decision support for supply chain network decisions and management (i.e., models employing linear, mixed integer and nonlinear programming and related heuristic algorithms). Thus, optimization based modeling techniques represent an important component of the overall set of analytic decision support tools that can help facilitate efficient and effective supply chain network planning and management. Examples of other analytic tools and techniques used in supply chain network planning include simulation, data mining, database management, and forecasting to name a few (see Liberatore & Nydick, 2003; Shapiro, 2010, for additional examples and background).

In summary, the objectives of this chapter include the following:

• 1.

  To review the role of optimization modeling based analytics in supporting a firm’s supply chain planning and management activities,

• 2.

  To discuss how mathematical optimization models with profit maximizing objective functions fit into a hierarchical framework for a firm’s supply chain network planning and scheduling processes,

• 3.

  To review why optimization modeling based analytics will continue to play an increasingly important role in supply chain network decision support and management.

Background

Advanced analytics for manufacturing and distribution planning is the application of supply chain management models and descriptive models to data in order to develop and analyze information, and to generate management decision support and recommended solutions to the process of planning, sourcing, making and delivering products and services to end customers and consumers.

Supply chain management mathematical optimization models, which include linear programming, mixed integer programming and nonlinear programming models, are the optimal tools for analyzing complex supply chain management problems (Shapiro, 2010). In this chapter, we will focus on “deterministic” mathematical optimization models where a model solution is driven by an exogenously given (i.e., pre-determined) forecast. We note that in private industry supply chain practice, the vast majority of optimization models employed are deterministic. Practitioners typically address the potential limitations of using a single, fixed forecast by running their optimization planning models under multiple forecast scenarios, where often probabilities are assigned to each scenario. This approach alleviates the potential limitations of developing a planning solution based upon just one, deterministic forecast (Shapiro, 2010). Descriptive models such as forecasting, data mining, management accounting and others, are utilized to process the data acquired from transactional databases such as a firm’s Enterprise Resource Planning (ERP) System and stored in the Supply Chain Management (SCM) database. Examples of potential uses of advanced optimization based analytics are as follows: in planning, the analysis of data to predict market trends of products and production capacity requirements; in sourcing, the use of an agent-based procurement system; and in delivering products, the applications of business analytics in logistics management to bring the products to the markets more efficiently (Trkman et al., 2010).