"Demand shaping" is a demand-driven, supply-constraining customer-centric approach to planning and execution. It aligns process with customer demand at strategic and tactical levels and with an organization's capabilities, resulting in improved revenue, profitability and market share. It also helps optimize use of resources, reducing excess inventory and improving inventory turns. At the strategic level, the emphasis is on aligning customers' long-term demand patterns to long-term resource and capacity constraints. At the tactical level, the focus is on understanding demand patterns and then influencing customers' demand toward available supply, using the levers of price, promotion and products/services bundling.

Demand shaping may become an integral part of sales and operations planning. Tactically, it complements the demand and supply planning process by closing the loop through execution. While companies use forecasting to plan for customer demand, they should use demand shaping to reduce the gap between their expectations and ac- tual customer demand.

Our research identified key business issues influencing new investments over the next eight to 12 months in customer order management. More than 50 percent of re- spondents said increasing order volume, as well as new product introductions and a wide product range, were key considerations.

Despite the market imperative and the stark nature of the associated operational implications, many companies have been unable to incorporate demand shaping effectively. There are two main reasons for this.

The first one has to do with immature pro- cesses, especially with respect to de- mand shaping levers, including effective configuration management, pricing and quotation management, and guided selling within overall customer acquisition.

The second is the limited sphere of influence within the industry value chain. With the evolution of the high-tech industry, most companies have found partners to manage noncore operations. That has led to a whole set of intermediaries within the value chain, so that any one company has a very limited sphere of influence.

The value of real-time visibility
Dell Inc. operates closely with customers and suppliers, but the company also has real-time visibility into the supply chain, including inventories of product, assemblies and parts all the way from suppliers' docks to supplier hubs. There is also real-time visibility into interactions among tiers of suppliers through electronic collaboration.

On an hourly basis, Dell's manufacturing supervisors track inventory levels for parts in the supplier hub. When the inventory for a particular part goes below the "support" level, they tell the sales and marketing team whether to promote or demote corresponding end products. The key is to make changes to price, configurations and bundles, and to publish them to customers quickly.

After checking the possibility of replenishing the unavailable component to solve the problem, Dell begins its process of demand shaping in near-real-time. A customer ordering a computer with a 20-Gbyte drive, for ex- ample, is given a delayed ship date, al- lowing supply to catch up. If the customer doesn't accept that, the Web site offers a "deal" on an alternative drive (say, a 40-Gbyte drive)--reducing Dell's margin on that configuration slightly, but still increasing the gross margin from the sale. If that still doesn't work, the 40-Gbyte drive may be offered at no extra charge, reducing margins some more, but securing the sale.

Thus, a demand-supply mismatch seamlessly becomes resolved, maximizing sales and satisfying customers. The customer-direct model (and its Web-based tools) and the intelligent responsiveness Dell employs rapidly recognize a developing supply chain challenge and quickly mitigate any bottom-line impact.

The underlying principles of de- mand shaping are centered on three key processes:

•demand pattern recognition;

•supply supportability analysis;

•optimal demand steering.

None of these processes is groundbreaking. What is typically lacking, however, is optimal interplay among the three processes in an integrated, closed loop.

Demand pattern recognition involves gathering and processing data from every customer touch point to identify the demand pattern, including real-time visibility of demand and end-customer data, along with channel partner data and multidimensional (time, geography, customer group, product type) analysis of both structured and unstructured data for demand pattern identification and predictive analytics.

Supply supportability analysis provides real-time information on available supply to identify the mismatch between demand pattern and available supply. Two sets of information are required: supply and capacity. Companies need to extend their sphere of influence to multiple tiers upstream into the supply chain to achieve these key characteristics of the supply supportability process:

•real-time supply visibility;

•comprehensive information;

•collaboration with suppliers across tiers in short- and long-term contexts.

The third key process is optimal de- mand steering--to quickly steer customer demand to meet expected revenue, eliminate excess inventory and introduce products successfully.

Levers used for steering demand depend on the situation, such as constrained supply (low inventory) for a certain part, leading to promotion of a substitute product, or revenue below target, leading to product mix changes (through bundling).

The opportunity cost of revenue loss from the high-end product should be weighed against the benefit of preventing revenue leakage. When multiple levers can be used to shape demand, the optimal mix of levers and their values should be derived before applying the changes.

Demand shaping framework
The principles and processes outlined here may be brought together in an integrated application framework to provide top- and bottom-line improvements. Key elements of the application framework are:

•inter- and intra-organizational connectivity across tiers;

•the ability to capture, structure and comprehend structured and unstructured data from customers and channels;

•advanced business intelligence and data-mining systems to identify demand patterns across structured and unstructured data;

•an optimization engine to se- lect the optimal set of demand steering levers and their values;

•common processes for executing changes to demand steering levers across channel partners;

•a common data model and semantics for key supply data across channel partners and suppliers;

•common performance metrics across channel partners and suppliers for accountability and sharing of risks and benefits;

•available-to-promise and capable-to-promise capabilities to carry out capability analysis in real-time;

• an event and exception management framework to identify changes in demand and supply;

•electronics negotiation and collaboration with customers and channel partners.

Demand shaping can provide a powerful competitive advantage. Based on AMR Research's benchmark analytics data, companies that employ demand-driven and -shaping processes achieve 17 percent better perfect order performance (service level) with 15 percent less inventory and 35 percent shorter cash-to-cash cycle times. That translates into 10 percent higher revenue, and up to 7 percent better profit margins, compared with "product-push supply chain" competitors.

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