Supply and Demand Synchronization in Assemble-To-Order Supply Chains

In 2000 AMR Research identified the benefits of the 21st century supply chain and introduced the concept of Demand-Driven Supply Networks (DDSN). A Demand-Driven Supply Network is a system of technologies and business processes that senses and responds to real time demand across a network of customers, suppliers, and employees. The DDSN principles require that companies shift from a traditional push-based supply chain to a pull-based, customer-centric approach. Leading companies that have adopted the DDSN business strategy have become more demand sensing, have more efforts on demand shaping and focus on a profitable demand response (e.g., O’Marah and Souza 2004; Cecere et al. 2005).

Lee (2004) describes how leading companies approach a DDSN strategy to attain sustainable competitive advantage. He observes that topperforming supply chains possess three different qualities: agility (the ability to respond quickly to short-term change in demand and supply and manage external disruptions more effectively); adaptability (ability to adjust the design of the supply chain to meet structural shifts in markets and modify supply network strategies, products, and technologies); and alignment (ability to create shared incentives that align the interests of businesses across the supply chain). Similar principles are exercised in the Sense and Respond Value Net model described in Lin et al. (2002; 2004), an event-driven model with proactive sensing and intelligent responding for collaboratively optimizing the performance of end-to-end value networks. The model combines timely decision support with risk and resource management, extended supply chain optimization, business process automation and partner alliances into an integrated management system. It enables value network partners to adapt to changing business environments and respond quickly with the best available policies for achieving financial or operational business objectives. Although the articles by Lee and Lin et al. do not directly address DDSN, the recommended tasks are directly in line with the process of building DDSN capabilities.

In this chapter we describe an event-driven demand and supply planning process that incorporates the DDSN principles of demand shaping and profitable demand response to drive better operational efficiency. The proposed business process, called availability management process, extends its focus beyond aligning demand, supply and financial metrics and directly applies demand and supply data to better respond to changes in the marketplace. It aims at creating marketable product alternatives to mitigate misalignments of supply and demand and to enable companies to take full advantage of a “sell-what-you-have” strategy. In contrast to traditional enterprise planning applications that generally require weeks to adapt to changes in demand, event-driven availability management can quickly highlight unexpected events such as component shortfalls, excess inventories, delayed shipments, or spikes in customer demand. As a result, production managers detect problems and opportunities earlier and can develop a focused response that avoids the time and expense of completely regenerating a production plan. A well executed availability management process benefits the customers through improved delivery times, and it benefits the enterprise through higher inventory turns, fewer supply overages and shortfalls, and reduced inventory liability exposure.

We further describe an analytical optimization model that supports availability management and determines profitable product offerings that minimize inventory liabilities and lost sales risks over the entire supply chain. The model provides dynamic, real-time sales recommendations based on current availability, price, performance and customer demand information. This enables on demand up-selling, alternative-selling and down-selling to better integrate the supply chain horizontally, connecting the interaction of customers, business partners and sales teams to the procurement and manufacturing capabilities of the company. The optimization is most effective in an assemble-to-order (ATO) environment where end products are configured from pluggable sales building blocks.

The remainder of this chapter is organized as follows. In section 2 we present the underpinning principles of availability management and discuss the advantages and disadvantages of different business processes to implement availability management. In section 3 we survey the relevant literature. In section 4 we formulate the problem of finding marketable product alternatives in a given product portfolio that best utilize the available component supply as an optimization problem and develop an efficient computational procedure for solving the problem. Numerical findings and discussions of results are presented in section 5. This produces several insights into how advanced availability management can help to proactively coordinate supply and sales, and it quantifies several business benefits in the context of assemble-to-order manufacturing. Section 6 concludes this chapter.