A Supply Chain Planning Case Study

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  • Corporate growth brought diversified product lines, expanded geographic reach and increased complexity
  • Implemented demand planning, inventory optimization and pre-build capacity planning
  • Improved performance across multiple business metrics

Project and Objectives

Since listed as a public company, Thule had been growing both organically and through acquisitions. As the business diversified into new product lines and expanded its geographic reach, Thule was faced with increasingly complex planning and forecasting processes. After being based first on Excel, and then later decentralized, the demand planning process was then centralized to obtain better forecasts. However, centralization brought too much manual work and a poor connection between customer service and inventory.

The issue was most acute in Outdoor, Thule’s largest product segment. The Outdoor segment had a global customer base primarily of car and sports distributors, with a significant portion of sales from OES (Original Equipment Suppliers) who provide parts to the automotive industry. Their supply chain consisted of six factories in Europe, one central warehouse and five regional distribution centers with more than 4000 finished goods SKUs.

Outdoor also faced distinct seasonal demand spikes for their summer and winter product lines. Peak season demand and capacity constraints required pre-season production, but bulky products also limited storage possibilities.

They also had no optimally defined safety stocks for slow moving items and intermittent demand products. Another challenge was “kits” (often with shorter life cycles) that created higher demand variation.


Thule’s Outdoor segment called upon trusted supply chain partner Optilon to implement an automated forecasting system that delivered a more efficient and accurate demand planning process.

Thule Demand Planner, Michael Wolfsteiner, explained the uniqueness of the solution, based on ToolsGroup’s SO99+ software, “Traditional demand planning considers demand history in terms of quantity only, ignoring customer order lines. ToolsGroup’s SO99+ on the other hand analyzes the demand history both in terms of quantity and customer order lines, in order to better model the shape of the demand. The close connection between desired service level and inventory level is very positive.

The solution included inventory optimization. Thule‘s old safety stock routine was based on a seasonally dependent coverage rate (e.g., number of days’ average demand). The new optimization instead starts from an aggregated service level target and defines service levels for each SKU, taking advantage of a wide variety of variables, such as demand variation, average demand, order frequency, costs and lead time. It modifies service and coverage to maximize the overall use of the capital within a pre-defined “service group”. Minimum service levels are set for business critical items.

The system also included rough cut planning to identify requirements to pre-build stock due to capacity limitations. The actual replenishment is still done in the ERP system.

Results & Benefits

Upgrading and automating their front-end forecasting to back-end production planning improved Thule’s performance across a range of business metrics.

  • Service level increased by more than 20 points, from 75% to 95+%
  • Outdoor segment average inventory was reduced by more than $2 million
  • Workload reduced by 75%

Thule offers a wide assortment of products with a focus on how to bring equipment with you when using a car (roof racks, bike and water sport carriers, roof boxes).Today, Thule is divided into four product segments; Outdoor, Towing, Bags and New Ventures.The company operates in 17 countries, has annual revenues of more than $800 million, and 3100 employees at 50 production facilities and sales offices worldwide.

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