By Andrew K. Reese
Procter & Gamble has made supply chain management a core competency, earning the company a top three spot in AMR Research's "Top 25 Supply Chains" rankings for the past two years. But even while it wins industry accolades, P&G has continued to invest in improving its supply chain performance. Recently the company undertook an initiative to boost its short-term forecasting accuracy as a way of lowering safety stocks and improving its chances of winning at what P&G calls the "first moment of truth" — when the consumer looks for a product on the store shelf.
Mark Kremblewski, a Toronto-based global business expert working on demand planning at Procter & Gamble, says that the company's decision to tackle the short-term forecast challenge stemmed, in part, from a corporate-level realization a couple years ago that P&G needed to do additional work to reduce its inventory levels. The project also offered a quick-hit opportunity to produce a measurable, verifiable return on investment. "It made sense to focus on safety stock reduction because there is a theoretical direct correlation between accuracy benefit and safety targets," Kremblewski says. "There are all sorts of models showing that if you improve forecast accuracy by X amount, your safety stock goes down by Y amount — it's very calculable."
At Procter & Gamble, the company's demand planners focus on short-term horizons (one to six weeks) as well as medium- to long-term horizons (seven weeks to two years) in their forecasts, using the demand planning capabilities within P&G's SAP enterprise resource planning (ERP) solution. P&G also has an analytics group that develops safety stock models. The group's analysts use Excel spreadsheets and internally developed applications to work with data pulled from shipping and billing systems on a daily or weekly basis, adding in forecast accuracy data and then running the numbers through the models to produce target safety stock levels — a tedious, time- and resource-consuming process.
What P&G's demand planners lacked was an off-the-shelf software tool for short-term forecasting. In fact, until just a few years ago, such tools did not really exist. But Kremblewski's boss, Dick Clark, associate director for demand planning at Procter & Gamble, was aware of a company called Terra Technology that provides demand sensing and inventory optimization solutions targeted at "real-time forecasting" (thus the solution's moniker, Real–Time Forecasting, or RTF). Based in Norwalk, Conn., Terra Technology was founded in 2001, and it has built a client base that includes some of the largest consumer products companies in the world, such as Campbell Soup, by offering something of a unique solution to the short-term forecasting challenge. "To our knowledge Terra were the only people that were playing in this very short-term improvement area," Kremblewski says.
Piloting the Black Box
Before fully engaging with Terra Technology, Procter & Gamble ran through an initial test exercise with the solution provider. P&G provided shipment and historical forecast data from several different businesses within the company. Terra ran the data through its solution to produce benchmark models showing the potential for double-digit improvements in short-term forecast accuracy using the Terra tool. These results were convincing in themselves, but Terra Technology's solution also was attractive because it could run as a "black box," according to Kremblewski. In other words, once P&G connected the Real-Time Forecasting solution to the necessary data feeds from the appropriate enterprise systems, it would run on its own, without Procter & Gamble's demand planners having to tweak the system or do maintenance on it.