Demand-Driven Supply Networks: SCM Done Right

Supply Chain Management’s failure to deliver on promises in many organizations has as much to do with the lack of demand visibility as it does with poorly implemented, expensive projects. A new approach requiring incremental investments in demand data and analytics will turn the situation around.

Supply Chain Management (SCM) has received a bad rap over the past few years, and somewhat deservedly so. The market hype of the late 1990s came crashing down a few years later with the very public failures of large vendors like i2 Technologies, and big customers such as Cisco having to write off more than $2 billioneven though it had been considered best-in-class in supply chain practices. However different, these companies failed in their respective supply chains because they didn’t understand and properly gauge demand. These factors and others set the stage for a fundamental shift in how SCM will be used in the next three to five years, but not, perhaps, how it is bought.

Demand masters and process fragmentation set the stage for DDSN

The success of demand-driven companies such as Dell and Wal-Mart is being considered and emulated by industries across the board, from Banking to Batch Chemical Production. At the same time, supply chains have become substantially more complex as processes are outsourced. As a result, the requirements for SCM are expanding as demand data and processes are being incorporated into a new method of managing supply and demand.

This new method, the Demand-Driven Supply Network (DDSN), integrates demand data and processes across the supply networks of customers, suppliers, and employees to balance revenue against costs.

The shift is occurring for the following reasons:

• SCM is no longer a four-walls activity. SCM must encompass not only managing the planning and execution of daily operations but broaden its reach to its suppliers and customers. Traditional SCM tools with a supply orientation must be supplemented by a demand-oriented set of products. Moreover, companies are now trying to address unified management of their global supply chains.
• Traditional SCM deals poorly with rapid change. Many implementations of SCM work best during the steady-state demand periods of a product lifecycle, but poorly during its ramp up and down. With product lifecycles shortening and configurations/segmentations increasing, traditional methods of SCM fit poorly into dynamic product portfolios. Supply chain variability is one of the chief threats to profit margins, and in some industries this can be as high as 100 percent. A new set of products and integration capabilities is needed to address this issue.
• Traditional SCM doesn’t incorporate essential demand signals. The traditional demand signals of stovepiped forecasts are finally being recognized as too distorted and out of time to represent actual demand. This distortion coupled with unpredictability can cost manufacturers more than 10% of the Cost of Goods Sold (COGS). These views must be supplemented and supplanted by new interpretations, controls, and feedback for demand data.

DDSN manages revenue and decreasing costs

Supply chain systems have primarily dealt with managing the cost and resource utilization of capital equipment and labor. DDSNs will, too, but they will supplement them with a demand focus so that the right goods (rather than the planned ones) are made at the right time. However, for companies to make this type of leap, compensation and incentive plans need to be changed in manufacturing and logistics organizations as well as marketing and sales. Today, fewer than 25 percent of all companies are ready to make such a jump, thus leaving the upside of DDSNs to industry leaders. In fact, AMR Benchmarking research shows that in numerous industries, strong supply chain performers save 7 percent to 8 percent of revenue in total supply chain cost when compared to weaker peers in their industry.

New technology will be incremental and potentially simple

DDSNs are not a set of new products, but rather a new method of using older products that are supplemented with new technologies, data, and analytics:

• New dataSCM systems have used cost- and supply-oriented structured data. The DDSN places the demand management at the core of the value chain optimization problem. DDSNs need to supplement that data with unstructured data that captures more of the pure demand signals coming from customer and supplier sources as varied as the Web, newspapers, and weather reports. A group of technologies can capture, aggregate, and structure this data.
• New technologiesBecause DDSNs will cross different company boundaries, Web-based integration and collaboration technology, including workflow, is needed. To provide demand and supply visibility, a variety of technologies are needed at the personal level (such as instant messaging), as well as at the software level (such as service-based messaging for data/applications).
• New analyticsAs these new data streams are brought together and integrated, companies must analyze, segment, and decide how they want to best run their DDSNs. These systems will permit companies to decide how far and fast to run after revenue opportunities and conserve their resources to manage costs. Such capabilities will come on top of Enterprise Resource Planning (ERP) and SCM products from vendors such as SAP, Oracle, PeopleSoft, i2, and Manugistics. Certain needs like pricing, specialized demand management, and more, will be handled by niche vendors having a narrow vertical industry or functional focus.

Four-stage maturity model helps to plot long-term track

To deploy DDSNs, companies must traverse a four-stage process from simple planning at an individual facility to dynamically balancing an array of supply and demand partners. The four stagesReactive, Coping, Anticipating, and Orchestratingconsider process maturities as well as information maturities. (We will publish more detail on the maturity model in upcoming research.) With the AMR Research DDSN Maturity Model, companies build upon the success and technology base of prior levels, permitting the company to increase the value of earlier investments. Interestingly enough, the investment needed to achieve this may actually be minimal. Most Fortune 1,000-class companies have already purchased much of the needed technologies. Now it will be a matter of global, integrated deployment and developing the process performance insight to move to successively higher levels of achievement.

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