Many companies in a wide range of industries have realized major cost savings by outsourcing and offshoring back-room functions, call centers, IT and business processes. As the expertise of outsourcing providers has improved—and confidence in their expertise has grown—companies have begun to outsource increasingly sophisticated functions, such as specialty design and manufacturing.
Now, a handful of forward-looking companies are off-shoring the analysis of supply chain data to help make better operating decisions. By tapping into the analytical skills of lower-cost countries with a well-educated workforce—such as India—companies are getting valuable insights from data that had once been too voluminous or too complex to deal with quickly and in a cost-effective manner in-house.
Unlike traditional outsourcing and offshoring, which always carry a degree of operational and financial risk—and in some cases, disappointing results—outsourcing supply chain analytics is virtually risk-free. It's relatively easy to implement, requiring no exchange of people, processes or systems. It's also flexible. Instead of being locked into multi-year contracts, most companies simply subscribe to a data-analytics service on an as-needed basis. Outsourcing providers in India are able to spread one highly skilled, low-cost expert across three or four accounts, for far greater leverage than an individual company could achieve on its own.
Better demand forecasting and inventory management
Managed supply chain services can rapidly improve business results, especially in the closely aligned areas of demand management and inventory optimization. By collecting and analyzing point-of-sale data, along with data on inventory levels throughout the supply chain, product seasonality, the effect of promotions in different regions and competitive data, companies can manage demand far more effectively by quickly reacting to market changes and competitive actions to increase revenue. The insights gained can help companies target promotional spending to "shape" demand and make better decisions about when and how to replenish inventory.
A flexible demand forecasting model that regularly tunes itself to changing market conditions gives companies better guidance on how much inventory to keep and which channels and regions to distribute it to, based on buying patterns in the marketplace. These insights, in turn, help companies maintain the right—that is, minimum—level of inventory throughout the supply chain, for major cost savings. Also, focusing on the right inventory strategy can have a huge impact on service levels or availability, affecting revenue and market share.
These insights can be especially valuable for companies with a large number of SKUs. For instance, one large manufacturer in the high-tech industry offered 10,000 different types of components. Keeping the right number available to meet changing customer needs was an ongoing nightmare. But by outsourcing analysis of its sales data, the company discovered that 10 percent of its products—dubbed the A products—resulted in 90 percent of its shipments. Another group of products—C products—was rarely ordered at all. Also, the ordering patterns varied widely by customers. Certain customers gave four to six weeks of lead time for delivery, while others wanted shipment within three days of placing an order. Based on these insights, the company decided to focus on keeping the high-demand components in stock at all times.
The manufacturer also decided to follow an inventory deployment strategy that aligned with customer order lead times—for shorter lead times, following a make-to-stock model, and for longer lead-time demand, adopting a build-to-order model with periodic inventory target recommendations. Customer-service levels on these high-demand products soared. By contrast, C components were only finished and shipped when a specific customer order came in. This segmentation strategy led to enormous cost savings and greatly simplified inventory management—benefits that the company wouldn't have realized without outsourcing.
Better data analysis can also pinpoint problems that might have gone unresolved before. One consumer electronics company had just started selling a new TV model in a discount store chain. The company shipped five weeks of inventory to the store's distribution network, then sat back and waited for the revenue to roll in. But sales were far lower than expected. Working with data on sales per store, per region, the outsourcer discovered that in the two regions that usually had the highest sales, the TVs had only reached the discount store's distribution centers, not the stores themselves. In fact, the retailer had an undiscovered execution issue. When the service provider showed the retailer the analytics that revealed the log jam, the store quickly corrected the problem. Sales doubled in those regions over the next three weeks. This level of analysis—and the speed with which it was done—would have been impossible with the company's existing staff and expertise. Managed supply chain services can rapidly improve business results, especially in demand management and inventory optimization.
Getting started
With shrinking product life cycles, ongoing cost pressures and growing variability in customer demand, the supply chain must be more agile and effective than ever before. Yet many companies have been disappointed in their supply chain software, often after making major investments in time and money. Add to this the fact that the granularity and frequency of information are increasing from such sources as POS data capture, market research, the Internet and RFID. Subscription outsourcing is a way to improve supply chain planning and effectiveness quickly, at a reasonable cost. Getting started can take as little as two months, so time-to-results is far faster and less costly than implementing yet another software program or developing the needed skills in-house, which takes even longer because of the steep learning curve involved.
Outsourcing supply chain analytics usually starts with a needs-analysis stage, where the service provider seeks to understand the company, its supply chain and its data. Based on its findings, the service provider creates a customized data-analysis program designed to meet the company's needs. Typically, the program focuses on a specific problem or goal, such as preventing lost sales, increasing inventory turns or improving promotional effectiveness. These goals are then linked to improvement in a specific supply chain competency, such as more accurate forecasting or better inventory management. Then, the provider integrates with the customer's data sources and determines how often to receive data, in what format and so forth. This process takes about two months. Then, the data-analysis service is up and running, and new insights begin to accrue.
Although starting up a data-analysis program is relatively straightforward, a company may find that getting its employees to trust and use the findings will be a challenge. Changing the way people work is always hard. Strong executive sponsorship is usually needed to ensure that the new insights are acted on. Another hurdle for some companies is becoming comfortable giving out proprietary data. However, most service providers have strong security measures in place and will sign non-disclosure agreements upfront.
At some point, many companies elect to bring the data-analysis service in-house in a "co-sourcing" arrangement, where the outsourcing team integrates with the client team and teaches them the needed data-analysis skills.
Outsourcing data analysis holds great promise for companies that want to reach the next level of supply chain effectiveness quickly, at a relatively low cost. By supporting better planning, demand forecasting and inventory management, this new category of out-sourcing is delivering major returns to the companies that have tried it.
— by Amarnath Thombre, Madhu Rajendran, and Lee Wilwerding
Request more information at: supply_chain_leader@i2.com.
