Kimberly-Clark connects its supply chain to the store shelf
By James A. Cooke | From the Quarter 1 2013 issue
In its quest to achieve a demand-driven supply chain, Kimberly-Clark turned to software that generates shipment forecasts based on point-of-sale data. Now the consumer products giant can better serve some of its customers with a lot less inventory.
For the past six years, Kimberly-Clark Corp. has been on a mission to connect its supply chain to the store shelf. The manufacturer of personal-care products wanted to create a demand-driven supply chain that would make and warehouse only the precise amount of inventory needed to replace what consumers actually purchased.
The company had good reason to make this one of its top priorities. "If we align our activities to what's happening on the shelf, we can take a lot of cost, waste, and inventory out of the system," explains Rick Sather, Kimberly-Clark's vice president of customer supply chain for North America consumer products.
That's easier said than done, of course. The roadblock for Kimberly-Clark was that its store shipments were based on historical sales forecasts, which were not very accurate predictors of future sales. To match shipments with actual demand, the company would need to use point-of-sale (POS) data from consumer purchases as the basis for replenishments to grocers and retailers.
Toward that end, the manufacturer began using software that utilizes sales data to generate forecasts that trigger shipments to stores. To date, only three of Kimberly-Clark's largest customers are participating in the program, but the results have been notable. These demand-driven forecasts, which are more accurate than the historical sales forecasts, let the manufacturer better serve those customers but with much less inventory.
Based in Irving, Texas, a suburb of Dallas, Kimberly-Clark makes such well-known personal-care products as Kleenex facial tissues, Huggies diapers, and Scott's paper towels. Its worldwide sales exceeded $20 billion in 2011.
Back in 2006, company executives decided to refocus Kimberly-Clark's supply chain strategy from supporting manufacturing to serving the specific needs of its retail and grocery customers. As a first step, the company reconfigured its North American distribution network to place its warehouses closer to those customers. Before the reconfiguration, Kimberly-Clark used 120 facilities of various types, and it shipped from 60 to 70 locations to satisfy all customer orders. The shipping location depended on the product mix of the order. As a result, different product mixes resulted in different shipping locations for the same customer, and forecasting and maintaining the proper mix of products at any given DC was difficult.
By 2008, Kimberly-Clark had reduced the number of warehouses it used to 30 multiproduct facilities strategically located near its customers. The reconfiguration involved a combination of opening new, larger facilities—some of which handle Kimberly-Clark's full product line—and repurposing some existing sites. For example, a few of the distribution centers began supporting a smaller group of customers, or they switched to shipping only promotional items. Today, 20 of the 30 warehouses and distribution centers now ship directly to customers.
Because the reconfiguration placed more warehouses and DCs closer to Kimberly-Clark's customers, the company was able to increase order frequency and reduce transit times for many of them. That paid off not just for the customers but for the manufacturer, too. "We realigned our DC network and streamlined it to bring inventory and costs out of the system and make ourselves more responsive to customer needs," says Manager of Supply Chain Analysis Michael Kalinowski. "We used to view our supply chain as ending once we delivered to the customer's door, but now we've extended that to the customer's retail location, and in some cases, right to the shelf."
Becoming one with demand
The ultimate objective of any change in supply chain strategy is to increase company profits. Kimberly-Clark viewed a demand-driven supply chain as being critical to achieving that objective. The Great Recession of 2008-2009 brought additional "energy" to that focus as Kimberly-Clark sought to reduce its inventory holdings to free up working capital, says Director of Supply Chain Strategy Scott DeGroot.
To become a truly demand-driven supply chain, managers knew, Kimberly-Clark would have to incorporate demand-signal data—information about actual consumer purchases—into its plans for resupplying retailers with products. In 2009, the company made some limited use of downstream retail data in its demand-planning software, but it continued to rely for the most part on historical shipment data as the basis for its replenishment forecasts. But forecasts based on historical sales are prone to errors, because they cannot predict spikes in consumer demand. Such errors left Kimberly-Clark with excess safety stock and unsold inventory.
To address that problem and improve forecasting, Kimberly-Clark conducted a pilot program with the software vendor Terra Technology that aimed to incorporate demand signals into its North American operation. The pilot proved successful, and in 2010 the consumer products giant purchased and implemented Terra Technology's multienterprise demand-sensing solution. Initially, Kimberly-Clark only ran the software's forecast engine, using its own internal data. Since 2011, however, it has been using actual retail-sales data to drive both replenishment and manufacturing.
Three retailers, which account for one-third of Kimberly-Clark's consumer products business in North America, currently provide point-of-sale data. That information is fed daily into the solution's engine, which then recalibrates the shipment forecast for each of those retailers. Each day, the software evaluates any new data inputs from the retailers along with open orders and the legacy demand-planning forecast to generate a new shipment forecast. That forecast, in daily buckets, covers the current week plus the next four weeks. Kimberly-Clark then uses that forecast to guide internal deployment decisions and tactical planning.
The software contains algorithms that process data provided by the retailers, such as point-of-sale information, inventory in the distribution channel, shipments from warehouses, and the retailer's own forecast. It reconciles all of that data to create a daily operational forecast. The software also identifies patterns in the historical data to determine which inputs are the most predictive in forecasting shipments from Kimberly-Clark's facilities. The inputs are re-evaluated weekly, and how much influence each input has on the forecast can change. For example, POS might be the best predictor of a shipment forecast on a three-week horizon, but actual orders could be the best predictor for the current week.
By using actual demand—that is, the point-of-sale data—to recalculate its operational forecasts, Kimberly-Clark can better ensure that it has the products consumers want to buy in stores at the right time. Although only three companies at the moment are providing POS data, Kimberly-Clark is also using the Terra solution to create forecasts for its other retail customers. For that customer group, the manufacturer relies on historical shipment data to develop its forecast.
Lower forecast error rates
The incorporation of demand signals into Kimberly-Clark's shipment forecasting has resulted in substantial improvements in several respects. For one thing, the company has been able to develop a more granular metric for forecast errors. In the past, it measured forecasts by product categories; the metric it uses now tracks stock-keeping units (SKUs) and stocking locations. This metric is defined as the absolute difference between shipments and forecast, and it's reported as a percentage of shipments.
Using that particular metric to evaluate its daily forecast, Kimberly-Clark has seen a reduction in forecast errors of as much as 35 percent for a one-week planning horizon and 20 percent for a two-week horizon. "What we've noticed is that as you go farther out in the [planning] horizon, the inputs are less predictive and the amount of forecast-error reduction tends to erode," says Jared Hanson, a demand planning specialist.
Thanks to that reduction in forecast errors, there is less need for safety stock. In fact, Hanson says, more accurate forecasts have allowed Kimberly-Clark to take out one to three days of safety stock, depending on the SKU. "From a dollars or return on investment perspective, that's the most tangible benefit," he says.
More accurate forecasts and the commensurate reductions in safety stock have helped Kimberly-Clark reduce its overall inventory. The company says it has cut finished-goods inventory by 19 percent in the last 18 months.
Equally important, say Kimberly-Clark's supply chain executives, is that this stellar inventory performance has not compromised the quality of service it provides to its customers. "As we sit today," says Rick Sather, "our ability to serve customers with this level of inventory is the best it's been in many years."
James A. Cooke is a supply chain software analyst. He was previously the editor of CSCMP's Supply Chain Quarterly and a staff writer for DC Velocity.
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