Building a better supply chain

Using the demand-driven supply network to improve efficiency.

by Ronald S. Swift

Change is coming to supply chain management—possibly a bigger change than any in the last 100 years. Business expansion, transportation issues and consumer expectations are combining to create a challenge for industry: how to balance supply and demand.

In the past, supply chains were built to forecast demand and create large stocks of inventory to be sold into the marketplace. These supply chains were driven by productivity-centered factory schedules. Today, companies are finding they must make their supply chains flexible, responsive and synchronized to customer demand, thus reducing inventories and financially risky surpluses that may or may not be purchased or discounted in the marketplace.

The opening of global markets, increased product customization, the proliferation of SKUs and the global rate of new product introductions all drive the need to improve supply chain operations. Businesses also face transportation concerns, such as port and trucking capacity and rising fuel and labor costs. And there are the ever-increasing expectations from consumers that the prices of products and services should continue to go down.

The bottom line is this: In order to reduce inventories while meeting customer expectations of lower costs, companies have to figure out how to make their supply chains more efficient and at the same time responsive to actual customer demand.

The demand-driven supply network
To meet this challenge, businesses must have flexible supply chains and accurate demand signaling. Once this environment is in place, they can redesign their supply chains to fit actual demand—as reported by demand signals—rather than to fit forecasts. These demand driven supply networks (DDSNs) create more flexible, agile networks and improve the entire supply chain process.

A DDSN means building all supply chain processes, infrastructure, and information flows to serve the downstream source of demand, rather than the upstream supply constraints of factories and distribution systems. By letting demand be the driver, companies are finding that they can improve efficiencies against rising costs and maintain customer expectations.

Demand forecasting is particularly important to building a solid DDSN. Better demand forecasting can be achieved by integrating point-of-sale (POS) data, in near real time, into demand tools which will help build better forecasts and account for seasonality, regional variations, pricing, promotions, distribution and manufacturing constraints within the supply chain.

In the DDSN, where demand can be closely monitored through analytics, companies find they are actually able to shape customer demand. A DDSN helps companies manage strategic inventory reserves, and it can provide early signals of demand changes so suppliers have timely notice of when they need to adjust their output.

For example, a major computer maker is now able to run promotions on certain PC products based on what it has to supply to the marketplace. In this way, the company shapes consumer demand for the products it has, and it steers consumers away from items it doesn't have. By creating a demand-driven strategy, the company shapes customer demand in the marketplace and a more streamlined supply chain.

Capturing the demand signal
To increase the efficiency of their supply chains, many consumer packaged goods (CPG) companies today are demanding access to POS data from retailers in order to more accurately predict demand. In the past, these companies were at the mercy of retailers to receive orders and then build to those requests. Today, CPG companies are able to analyze real demand at the point of sale. This level of detailed data access brings near real-time information to manufacturers so they can adjust their supply chains to react to what's really being sold, not to what a retailer orders to stock a distribution center.

This supply chain reaction relies on demand signals that link production with true demand by integrating data from a wide range of demand visibility sources—including POS, channel partners, market research, customer service, marketing and promotional information, and inventory availability. That data is then combined in a centralized database to create a demand signal repository (DSR). This repository allows for the creation of a single, detailed forecast that each division of the company can use to generate comprehensive operating plans.

With a DSR from Teradata, a company will finally be able to answer such detailed questions as:

• How are global sales of the new SKU performing two days after the launch?
• How does my store level demand align with my replenishment orders and forecasts?
• What is the optimal inventory and replenishment policy by SKU and store?
• How profitable are each of my SKUs—by store?
• What are the most profitable, yet sustainable, price points per product by location?
• What is the probability that demand will exceed the forecast by more than 15%?

Developing a DDSN model
To achieve a world-class DDSN, a company must make a model of its supply chain process to understand how it works. Modeling means describing the process and the corresponding flow of information, the decision-making process and the rules for how it operates. This starts with a company's knowledge workers.

Companies often engage consultants and integrators, such as Accenture, BearingPoint, Capgemini or Deloitte, to assist in the modeling process. From that modeling process, companies can then derive opportunities to become more demand-driven and pursue opportunities for improvement. For example, one company might move manufacturing closer to a client to take advantage of faster shipping time and lower transportation costs.

Another company might install a postponement operation—holding the actual assembly of a product short of completion and then waiting for customer demand before final configuration. For example, a shock absorber manufacturer might hold its shock absorbers at a point where they haven't been painted. When the company gets an order for silver shock absorbers, it completes the painting process and delivers the product.

Using analytics to drive supply chain efficiencies
Efficiently managing complex supply chains takes a substantial amount of analytical power to do the required level of control and analysis—the kind that lets a company see the entire supply chain from end to end, across operations, databases and organizations.

For instance, the computer company uses analytics to accurately predict PC sales worldwide. After acquiring data on what worldwide consumption of PCs is expected to be, the company calculates its expected market share against overall global demand. Then analytics are used to go to a deeper level of forecasting, where demand is broken down into a list of specific products: desktops vs. laptops, high-speed desktops vs. low-speed desktops, and so on.

The DDSN process then unites management, the corporate workforce and production supervisors, who decide how much product they need to build, guided by operations supervisors, who know current capacity. This combines demand-driven forecasting with analytics to accurately predict consumer demand.

Demand-driven functionality actually examines all of a company's products by inventory level, movement and profit contribution. Then it aligns the products in an A/B/C/D/E/F category to determine which products are contributing the most profit and are the highest-volume movers. The algorithms in the system allow a company to get extremely detailed forecasts to maximize inventory all the way to the store SKU level.

Business intelligence tools can help companies examine their operations. By using analysis, companies can compare processes, such as how long it takes to put away product in one warehouse versus another. By measuring processes from an event management perspective, businesses can determine whether a process was executed at the proper time.

Using analytics to measure the balance between supply and demand can identify differences. If the supply and demand ratio gets out of sync, automatic processes inform a company of a potential excess or shortage of certain products. With a DDSN, the system does this calculation at the SKU and the location level.

A DDSN management system also performs a level of prediction, which takes into consideration a change in supply or demand at any point in the process and predicts the net effect at the end of the supply chain. If customer demand goes up and there are insufficient materials to meet the demand, the system calculates the shortage. If customer demand goes down, the system calculates how much inventory is in the supply chain and the amount of excess. It's a powerful way of maintaining control, visibility and continuous improvement, because businesses have the detail to identify which area of the supply chain needs the most attention.

Meeting business objectives through DDSN support
Historically, supply chain management was focused on operational efficiencies rather than analytical support of the network performance, with little or no visibility into essential business processes and supply chain interactions. For leading global organizations, this is no longer acceptable.

The overarching theme around supply chain management is the need for flexibility and adaptability. To achieve this level of adaptability, companies must have real-time, detailed information about their products and the performance of their supply chain, as well as powerful analytics that help them make important decisions.

Today's rapidly growing, ever-changing global markets demand that companies have the information to turn real-time problems into fixes and real-time opportunities into revenues. With proper analytics, companies can use their DDSNs to focus on the categories that contribute most to business objectives while also increasing efficiency and consumer loyalty. That's the type of supply chain network that world-class companies are looking for. T

Teradata Supply Chain Solutions

WHETHER YOU'RE LOOKING to optimize your inventories or achieve a complete collaboration vision, Teradata Supply Chain Solutions can remove obstacles and lay the course for instant, reality-based decision making that brings your production and customer demand into perfect alignment, propelling you to true business intelligence. Teradata uses two solutions—Teradata Supply Chain Intelligence and Teradata Demand Chain Management—to create an interlocking system that supports a company's demand-driven supply network (DDSN). Managing the supply chain analytically Teradata Supply Chain Intelligence (Teradata SCI) addresses supply chain management issues by synthesizing data from across an enterprise, transforming it into the customized, accurate reports that help companies solve difficult business problems based on real-time data. Teradata has the logistics experience and provides a strong analytics proposition for even the most complex supply chain. Teradata SCI can move your organization to a more analytical, proactive environment with solutions that provide a broad view of the supply chain. This view reveals the entire cycle of plan, source, make and deliver. Forecasting demand more accurately Teradata Demand Chain Management (Teradata DCM) solutions drive significant return on investment through improved customer service levels and reductions in unproductive inventory. It also eliminates the inferior forecasting science and "gut feelings" that create dangerous stock-out conditions. Teradata's highly accurate consumer demand forecasting eliminates the guesswork, while delivering increased sales, improved inventory turns and real ROI. The strength of Teradata DCM is its ability to forecast, using detailed data at the lowest level about products and their locations. Teradata DCM can improve customer service levels, reduce inventory and drive competitive advantage by providing the most accurate store-SKU and promotional forecasts. That translates into significant business benefits: increased sales and turns, reduced shrinkage and markdowns, optimized inventory and a fast return on investment. Teradata enables the global DDSN Teradata's experience and knowledge can power a company's growth and profitability, giving businesses the competitive advantage they need with customers, suppliers and trading partners. Teradata takes today's global businesses to supply-chain dominance. Some of the biggest names in the industry grow their businesses with Teradata Warehouse solutions.

Ronald S. Swift is vice president of cross-industry marketing solutions for Teradata. He is an internationally known consultant, author, developer and strategist who assists hundreds of clients on five continents in achieving their business goals.

For more information on the demand-driven supply network, read the white paper "Redefining Demand Visibility" by supply network strategist Jared Schrieber.