Kimberly-Clark Makes Sense of Demand

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Kimberly-Clark Makes Sense of Demand

By Heather Clancy - 10/11/2012
There are certain products that consumers ask for by name, and Kimberly-Clark happens to make at least three of them: Kleenex tissues, Huggies diapers and Kotex feminine napkins.

So when store supplies for the company’s retail partners are out of synch with production forecasts it can have a very real impact on the $20.8 billion personal care products giant’s sales. Empty shelves send impatient buyers to competitors’ products, while too much inventory can result in unwanted carrying costs.

For years, Kimberly-Clark relied on historical data to guide forecasts, but that changed in 2007, when the company began a complete end-to-end overhaul of its supply chain and the company invested in data analytics software that provides far more visibility into real-time demand trends.

Although that transformation is ongoing, the company has already made remarkable progress. It has reduced the number of customer-facing shipping locations to approximately 20 from 80 – all while growing annual sales an average of 5 percent during that timeframe. Kimberly-Clark also has decreased forecast errors by anywhere from 15 percent to 35 percent, depending on the weekly horizon window.

“The supply chain transformation started by moving away from functional silos to an end-to-end value chain,” says Rick Sather, vice president Customer Supply Chain, North America, Kimberly-Clark. The focus was on information flow to link the demand creation capability with supply capability while removing complexity and redundancies.”

The Cost of Inaccuracy

Why is forecast accuracy such a big deal for Kimberly-Clark? Consider the example of its $1 billion facial tissues business. During the high-volume flu season, a one-day reduction in safety stock for that business unit can equate to $10 million in savings across the supply chain network.

But Kimberly-Clark’s previous manual methods of tracking demand made it tough to manage safety stock very closely. So it used the supply chain overhaul to rethink this process.

“Particularly as it relates to the short-term demand forecast and cash conversion, it was determined that technology could provide capability that current processes and resources could not effectively manage,” says Scott DeGroot, director Customer Supply Chain Strategy, Kimberly-Clark. “Short-term demand management and reconciliation of the forecast to open orders was a time-consuming process that yielded only questionable benefit. These adjustments also required the analytical time of analysts that could be better spent improving the assumptions in the longer-range forecast.”  

But the Kimberly-Clark supply chain team knew throwing software at an inefficient business process wasn’t a recipe for success. So it set clear business objectives based on its existing pain points. Among the goals that it set out to achieve:
  • Reducing the time spent by analysts on micro-management of open orders so they could focus on more strategic projects
  • Standardizing the forecast approach that could be used with top retail customers
  • Ensuring the proper geographic distribution of products
  • Communicating trade promotions in a more timely manner
  • Building adequate inventory levels for constrained products

The business drivers guided the selection of Terra Technology’s Multi-Enterprise Demand Sensing (MDS) system, a forecasting analysis tool that has been instrumental in helping Kimberly-Clark marry information from monthly shipment forecasts with daily orders and retailer data. The ultimate mission of this software: to manage tactical forecasting adjustments while reducing forecast error.

Close Collaboration

While the project plan for the Terra MDS rollout was designed by the business side, Kimberly-Clark relied on the company’s IT team to facilitate the master plan and to ensure that the proper resources were dedicated to meet milestones.

Representatives from the two departments met on a weekly basis to review the project and to identify and discuss deviations that posed risks to the schedule or to key business objectives. A steering committee, including members of the functional supply chain leadership team, was appointed to oversee the process, meeting on a monthly basis, and any major changes required that group’s approval.

The Kimberly-Clark project team focused first on using Terra MDS to transform and modify two specific business functions: the short-term demand forecasts process and the process of assigning safety stock. Before the software rollout even began, the company began limiting the sorts of adjustments that could be made to short-term forecasts.

“The installation further defined what forecast adjustments to the short-term horizon added value,” says Jared Hanson, demand senior specialist for Kimberly-Clark’s North America Supply Chain Center of Excellence, and a key member of the implementation team.

Once the forecast process was in place, the team was able to review and adjust its overall policies for calculating safety stock, based on the new Terra MDS-generated forecasts.

“Tangent processes, such as the incremental event planning process, were also modified to leverage the short-term forecast adjustments from Terra MDS,” says Hanson. “The actual release of forecast data to the supply chain was changed from a weekly process to a daily process in the short-term horizon. This change was completed to take advantage of daily optimization in the Terra-generated forecast.”

Improved Strategic Response

What can a supply chain planning team do with this sort of data? For one thing, the improved forecast visibility has enabled Kimberly-Clark’s supply chain team to introduce weekly strategic demand meetings where deviations can be discussed and specific responses can be planned with far more information than in the past.

Aside from internal data collected across Kimberly-Clark’s own supply chain, the Terra MDS software also gives the company deeper access to data streams from three key retailers that were included in the initial implementation, helping Kimberly-Clark move toward standardizing the way open orders are considered in its forecasts.

“Data streams in use by at least one of the retailers include point-of-sale (POS) data, store level inventory, customer warehouse inventory, customer warehouse withdrawals and POS forecast. Most of the data updates are daily, but there are several customer streams updated on a weekly frequency,” says DeGroot.

Adds Sather: “Retailers are noticing these improvements, and it is starting to improve our sales and operations planning decision-making in a tangible way. We only wish we could roll out these kinds of improvements across the enterprise more quickly.”

As for its original objective to reduce forecast errors?

The reengineering effort and the technology behind it has enabled Kimberly-Clark to create a new metric for tracking forecast error (defined as the absolute difference between shipments and forecast, reported as a percentage of shipments). This metric, which reaches down to the SKU and shipping location level, can be tracked on weekly aggregate horizons from one to four weeks out. When evaluating the daily forecast (daily granularity), Kimberly-Clark has observed forecast error reductions as high as 35 percent in the first week of the horizon, and 20 percent on a two week horizon. The return on investment comes from a safety stock reduction built on lower forecast error. The forecast error reductions can translate into one to three days reduction in safety stock, says Hanson.

Looking Into the Future

And that’s just the first phase of its transformation. Based on its initial success, the Kimberly-Clark supply chain team is evaluating whether to funnel more retailer information through Terra MDS, further expanding its visibility into customer demand at the POS. It is also looking for other ways that it can optimize other inventory processes, which would further minimize its overall safety stock exposure.

The team is even using data drawn from the Terra MDS software to benchmark with peers on complexity in the forecast and supply chain. For example, the team has started to correlate how the number of different versions of a base SKU, or the number of possible shipping locations, contribute to forecast error. Using these insights may help identify opportunities to reduce complexity, and ultimately, forecast error.

“The journey isn’t over! Augmenting our short-term forecast with technology was one part of the journey. Initiatives are still underway to further improve our forecasting and supply chain capability. This year we are rolling out a new trade promotion management solution. And we are advancing our lean journey to better define inventory purpose and the impact of demand variability through each supply chain,” concludes Sather.  

Company at a Glance

Kimberly-Clark was the first to create facial tissue, has 57,000 employees working in 36 countries, has leading brands sold in more than 175 countries and has been in the business for 140 years. Nearly one-quarter of the world’s population purchases its products every day, and with brands like Kleenex, Scott, Huggies, Pull-Ups, Kotex, Poise and Depend, Kimberly-Clark holds the No. 1 or No. 2 brand share in more than 80 countries. The company was previously ranked first in the personal care category in Dow Jones’ Sustainability World Index five years in a row and has contributed $32.1 million in cash and products to charitable causes in 2011.

5 Best Practices for Better Forecasting

The most effective technology transformations are guided by clear business objectives. Demand Senior Specialist Jared Hanson recommends taking these steps to improve the odds that a supply chain evolution project will be successful.

1 Before evaluating any particular solution or technology to improve supply chain or forecasting capabilities, closely measure existing processes and understand the gaps. Get specific.

2 Assess what is really necessary to close or minimize those gaps. Should the business process be changed? Can technology fix the problem? Or, do you need a combination of both?

3 Use pilot opportunities to test conclusions. Make sure that test looks at multiple scenarios that your business encounters, especially if you have very different inventory and supply chain needs for each of your products.

4 Get the right people involved, particularly on the IT side, for collecting and cleansing data. Dedicated IT support is critical for technology-based transformation solutions.  

5 Benchmark, and then work on improving those benchmarks by learning from the forecasting and supply chain experiences of other companies. Use existing contacts or join forums to learn how others have approached similar opportunities in their supply chains.