Some years ago I took up a new role as Director of Demand Planning at a global sporting goods company. I was charged with overhauling its planning function. This is challenging at the best of times, but this was complicated by the company’s unique growth-by-acquisition strategy. The following is a case study of the transformation project I led, covering the problems I inherited, the step-by-step improvements I implemented, and how it was designed to facilitate decisions that directly supported organizational priorities.
This Indiana-based company imports and distributes multiple widely-recognized sporting goods and athletics brands globally. They do this through major retailers, specialty dealers, key online retailers, traditional department stores and eCommerce. The company operates primarily in North America with over fifty corporate accounts that includes companies like Walmart, and others. They also sell directly to Amazon and on Amazon marketplace. They launched their own internal website and fulfilment for direct-to-consumer sales last year and it already accounts for over 10% of their business.
Their business model was simple: grow through acquisition. During my time there, they owned forty-seven brands. While there was moderate organic growth within some of their brands, they relied on consolidation of the market to increase market share and top line growth. To do this, cash was King and the availability of capital was a key priority for the organization.
Their Existing Planning Process
Being their focus was on adding to their portfolio of brands, they had inherited a mishmash of various ERP systems and planning processes. For the most part their forecasting process was still somewhat manual, using traditional time-series methods like moving averages and seasonal random walk. They got some inputs from sales reps but their input was typically either about products that customers want in the current month or products that they thought were needed in inventory.
There were often competing objectives across inventory, purchasing, logistics, and manufacturing with attempts to get products at the lowest cost while constant pressures to reduce inventories. These problems were compounded with adding new brands and product mangers attempting to provide value to corporate accounts with unique offerings which added cost and caused SKU proliferation.
The Challenge: A Changing Marketplace
Over the past few years, they have seen a changing landscape in the way consumers are making purchases. This impacts how they needed to go to market. Direct-to-consumer was less than 10% just a few years ago. It now accounts for over 25% of their business. This includes all eCommerce business including Amazon, other retail websites, and the company’s own direct selling. It is estimated to grow by double digits over the next few years.
A major challenge they were facing is that their supply chain was designed around what retail stores were purchasing, i.e. the were planning for bulk orders with 2 week lead times. That is fine for retails order, but not for the increasing amount of direct-to-consumer orders that required single items to be delivered in 48 hours. This necessitated having inventory on hand instead of making to order, which required high quality forecasts.
To add to this challenge there is the issue of retail stores making up less of their total sales volume because now they are increasingly dealing directly with consumers. Given these shifts, their forecasts had gotten worse, as evidenced by a higher error percentage over the past couple of years.
Some challenges we faced were:
- Direct to-consumers expect a 36-hour delivery window. Prior retail customers traditionally allowed up to 2 weeks or more.
- Average lead-times to produce or source items has grown from 46 days on average to over 118 days as more products are now coming from China.
- Forecast accuracy at a weighted mean absolute percentage error (lag 1 WMAPE) with has gone from 68% to 85% due to SKU proliferation and complexity of new channels.
- Previous On Time and in Full (OTIF) was at 89%. It is now 77% due to the added volume of direct-to-consumers.
- Inventory turns have decreased from 4.2 to 3.7 as inventory rises due to SKU proliferation, longer lead-times, and poorer forecast quality.
The Solution: Integrated Planning
The company kicked-off a comprehensive digital transformation project whose goal was to standardize different planning processes to create competitive advantages, while Improving Total Cost and Enabling Inventory Optimization by integrating strategy, forecasts, planning, and perpetual inventory. Over an 18-month time horizon we would totally revamp the planning process, implement new platforms and technology across the entire organization, and introduce SKU rationalization, segmentation, and add predictive analytics—all of which was aligned to the organization’s growth-by-acquisition strategy.
Our initial focus was data and within first few months we went live with a new data warehouse and central data storage repository (DSR), and new business intelligence software (BI). These critical first steps helped the company find hidden issues in their data structure and in the information that was being used to make decisions. It provided visibility into data and was important for insuring they had the right data for planning and to create insights. It also allowed us to look at new attributes using web crawlers that extracted consumer information and other information about the new eCommerce channel that could be used in modeling.
Part of this new visibility included the development of new balanced scorecards and performance metrics to understand the trade-offs of decisions and how they impacted strategy. We made the KPIs more relevant to what the business wanted to achieve: number of active items, minimum order quantities, and gross margin as return on investment (GMROI). Understanding more of the drivers and being able to see the interdependence of metrics, we could now decide at what cost we were willing to service our customers or not.
We knew the importance of cash to the business model and that the availability of capital was a key objective of the organization. To this point, we determined that it made strategic sense to not aim for higher levels of service at the cost of higher inventories or additional, specialized SKU’s. Further consideration was given regarding the tradeoff inherent with larger orders that have longer lead times, i.e. they save upfront costs but risk tying up cash in inventory if it is not sold quickly.
After the initial focus on data, visibility, and decision making, attention was given to people, process, and technology. By the end of the first full year of the project, we defined and created specialized roles and hired new planners and a data architect to augment the current team, and went live with an advanced planning system (APS). We used clustering methods to help segregate items and customers which allowed us not only to focus planning resources on the most important items, but to do SKU rationalization to eliminate poor performing items. We now had a planner focused on eCommerce and began forecasting weekly using a combination of traditional methods and new models such as decision trees using external data. One example of external data is social media comments about new products which we used to predict through sell through post-launch.
The results came with much coordination, collaboration, challenges, and success. Due to these efforts, this company by the end of the second year saw a 10% improvement in fill rates, a 26% improvement in forecast accuracy, a 19% reduction in some supply chain costs, and an 11% reduction in excess inventory. Add to this real time visibility into data and new insights, they had a much better way to manage their business. Significantly, we saw a return on investment of the entire transformation project in less than 14 months. The company continues to be a leader in their industry and is taking full advantage of the changing consumer landscape.