Build Supplier Metrics, Build Better Product
How can short-range forecasts be developed to respond more quickly to market changes? How can the performance of a company?s top five suppliers be monitored? What will it cost to shift to a new supplier in a particular product line? To get at this information, managers and analysts often have to spend days, or even weeks, cobbling together aging or obsolete information on spreadsheets. Measuring forecast accuracy and supplier performance is still a hit-or-miss proposition. Supply chains are becoming increasingly complex, growing from linear arrangements to synchronized, multi-echelon, outward-facing networks of distributed servers.
Most companies lack the tools that can quickly sift through and present data coming in from supply chain partners and systems. Gartner Group (Stamford, CT), a leading IT consultancy, estimates that less than 1% of companies today are capable of monitoring and measuring online supplier performance. A study conducted by the University of Texas for Dell Computer Corp. found that only 11% of the 1,000 companies studied have some form of online transactional and information-sharing capabilities with their suppliers.
Building an information sharing and analysis capability will be critical to sustaining competitive advantage during the next few years. "The movement of supply chains in the 1990s was powered by optimization, but in the 2000s, the trend is toward workflow and analytics," says Lora Cecere, analyst with Gartner.
Achieving visibility across a supply chain means not only being able to track the performance of a supplier, but also that of a supplier?s suppliers. Such visibility enables a more agile level of demand planning, in which production and quality issues can be addressed within days, or even hours.
Supply chain analytics, which is the process of extracting and presenting supply chain information to provide measurement, monitoring, forecasting and management of the chain, forms the foundation of such an effort.
Consider the role of the network administrator, who needs to monitor, in real time, a company?s servers, networks and interfaces. The administrator needs to be alerted when bottlenecks appear or if a system is underutilized or stressed. He also needs to be able to run and review reports to spot developing trends in system use, and be able to plan for acquisitions or upgrades of future system resources. If a network administrator is kept in the dark about the performance of a corporate computer system, the system would be in sad shape. Likewise, a business manager trying to optimize a large supply chain network needs to be able to monitor the system on an end-to-end basis.
A supply chain analytics system will enable a company?s analysts and executives to view the performance of their supply chain over a secure extranet and alert them to problems. Predefined thresholds, fed into the system by users, trigger these alerts. A new generation of monitoring tools not only provides monitoring and alert capabilities, but also enables end-users to drill-down, visualize and analyze trends in real time.
Processes that can be tracked by a supply chain analytics system include production, materials management, procurement, manufacturing, warehousing, transportation, inventory, supplier management, fulfillment, customer relationship management, demand management, order fulfillment, product development and returns management.
Once implemented, supply chain analytics can help companies achieve more surplus?or economic profit?from their supply chains. Analytics also can help drive down costs, increase productivity and increase market opportunities across a supply chain.
The formal return on investment for a supply chain analytics effort is significant. Gartner Group calculates the potential return on investment on supply chain analytics at about 40% after five years. However, supply chain analytics can deliver value in a more profound and immediate way?by actually increasing cash flow. That?s because these analytics can help companies reduce their inventory levels, which means more cash on hand, rather than money tied up in inventories. Typically, manufacturers are forced to overproduce inventories to meet unpredictable demand, only to be followed by forced markdowns of excess items.
The most likely end-users of analytics tools are those involved in key processes in the supply chain, such as transportation, procurement, or manufacturing managers and analysts. Ideally, a group made up of representatives from each affected process should be enlisted to determine what metrics make sense in this new supply chain and be able to look at continuous improvements against their goals.
As analytics capabilities mature, the end-user base will grow to include managers from supplier and trading partner firms. This information sharing is where the value of an analytical infrastructure begins to be seen. Because the analytics involve monitoring and measuring data from suppliers, manufacturers, planners, sales and marketing, logistics and customers, this information needs to be shared with these partners. Historically, businesses have been reluctant to share such information. However, there is a growing recognition that by sharing analysis of business process data across the supply chain, the overall profit "pie" will grow, as partners become more responsive to the market. A number of companies in vertical industries are already collaborating online with such initiatives as vendor-managed inventory and collaborative planning, forecasting and replenishment (CPFR).
Technology: tying it all together
Information from supply chain management processes must be collected, measured, analyzed and continuously monitored. This requires integration of data coming out of enterprise resource planning (ERP), customer relationship management (CRM) and all other systems supporting these business processes. Increasingly, applications supporting these processes also are supporting common standards such a XML, UDDI and WDSL, so new releases are moving closer to the interoperability required to support collaboration between partners.
Each process has its own set of metrics that can be analyzed. Information streaming in from various points on the supply chain?from back-end mainframes to Web servers?is consolidated in a centralized database, data warehouse or data mart-type of environment. Data from transactional systems is summarized in an analytical database, which should be able to scale to large sizes, and can be continually updated. Because most organizations have a wide array of packaged or custom-built applications, a data warehouse approach, which can store information from these various data silos in one place, is often the best choice. "Enabling seamless navigation of data requires a common, unified data model that ensures a ?single version of the truth? about the business," according to Henry Morris, analyst with International Data Corp. (Framingham, MA).
A key component of an analytical database is a rules engine that links to agents running within key applications. The rules engine alerts managers of problems based on predefined tolerance parameters. The information contained in this database must also be easily accessible to the partners in the network in a cost efficient manner?any time, any place and via any device.
The front end of an analytics system needs to be speedy, accessible and user friendly. While supply chain analytic systems may employ sophisticated tools running against data stored on high-end systems, it is important that the end results are user friendly and accessible either through PCs or browsers.
Alerts, for example, should be presented in a familiar format, such as a grid or checklist. Historical data should be rendered in a graphical format, such as a bar or line chart. If end-users have difficulty using a system, or cannot pull up the data they need within a few seconds and navigate down through the information toward a solution, they will abandon the application and its benefits will not be realized. Final presentations to end-users need to be in the form of a graphic presentation that can quickly illustrate the business implications of the trend being measured.
Whether the results are made available on a thin-client browser or as a Windows PC application depends on the way the data will be used. For some companies, it pays to support "power users," typically analysts, who can access and conduct custom analysis from their workstations. For users from the operational side of the business, a browser that accesses predefined reports is sufficient.
Accelerating data delivery
To pull supply chain logistics reports for customers, end users at a major transportation and logistics company had to pull down numerous reports from back-end mainframes and load them into spreadsheets. Often, it took up to four days to get an answer to a customer?s question. As the company sought to grow its supply chain management business, it knew it had to speed up this process. The company?s business goal was to support, on an outsourced basis, customers? supply chains, including product movement between manufacturers, distributors and retailers.
The solution was a supply chain analytics system that ran against an operational data warehouse that mirrors its mainframe database. The company opened up its online systems to customers over an extranet, including analytical tools to help them track shipping and logistics transactions, and, as a result, the three days of lag time was reduced to almost instantaneous feedback to inquiries. Information is sent between the company?s data center and its vehicles by satellite, providing faster response times for customers. Shipping bottlenecks or other issues can be fixed quickly. Customers also are provided an interface to view historical data.
Supply chains are rapidly evolving from linear arrangements to real-time, customer-facing networks. With analytics, companies that share information about their supply-chain management processes throughout the supply network will be able to capture more surplus than companies that do not. "The world of supply chains is probably changing the most around latency, time to market and agility," says Cecere. "Being able to carve out the key metrics in these areas is essential."
In the years to come, analytical capabilities will increasingly be facilitated by continuing improvements in application and user interface design, particularly as enterprise information portals become the leading gateways to relevant applications and content.
Real-time monitoring capabilities will play a greater role in analytics. These capabilities are already necessary for internal operations, such as manufacturing. An analytical tool that sits on top of a manufacturing execution or ERP system can monitor real-time events as they happen. For example, a manager in the consumer products industry can monitor daily data to see which products were sold, and replenish these products the following day. An e-commerce firm can monitor sales from hour to hour to gage spikes in demand.
These are still the early days of supply chain analytics, and most companies have only begun to start leveraging information from inside their company, let alone outside the firewall. The challenge is identifying key processes for measurement among top suppliers or trading partners. Over the next few years, companies will be developing these capabilities and bringing out this visibility to trading partners.
• Supply chain analytics is the process of extracting and presenting supply chain information to provide measurement, monitoring, forecasting and management of the chain.
• Information sharing and analysis capabilities are critical to sustaining a competitive advantage.
• Achieving visibility across a supply chain means not only being able to track the performance of a supplier, but also that of a supplier’s supplier.
• A supply chain analytics system enables a company’s analysts and executives to view the performance of their supply chain via a secure extranet. Benefits of Supply Chain Analytics
• Reduce costs. Supply chain analytics help centralize production and purchasing operations by identifying processes that can be consolidated.
• Increase working capital. Supply chain analytics help companies manage and anticipate spikes in demand. By keeping inventory levels low, companies free up cash that would otherwise be tied up in inventory.
• Improve business partners’ decision-making. Trading partners or suppliers have access to new information, to enable them to more quickly address cost overruns, distribution bottlenecks and customer complaints. All supply chain members will also have access to the same customer questions or complaints.
• Open new markets. Supply chain analytics provide visibility to the final customer and help companies track purchasing patterns by profitable customer segments. For example, a manufacturer that only sells through distributors and retailers and has no direct contact with end-users will be able to analyze how its products are being used. To take the concept one step further, companies with analytic warehouses and tools could turn these areas into profit centers by offering these tools on a fee basis to suppliers and customers.
• Address channel profitability. Supply chain analytics can help sales and distributor networks better target customer segments. For example, a boat manufacturer can provide its dealers with information about sales, inventory and deliveries. Dealers can measure quarterly performance against those of other dealers, which ultimately helps the manufacturer sell more boats and accessories.
• Address quality issues. Supply chain analytics can help track product quality issues to the original source. In addition, production problems can be identified as soon as they crop up. The back-end costs of customer returns can be avoided.
• Retain customers. Sharing supply chain analytical data with channel partners and customers helps build a relationship with them and gives you a long-term advantage over competitors that do not provide this capability.