The company operates 3 production sites, employing approximately 1800 people.
Our client believed that levels of raw materials and finished goods were less than optimal and the opportunity to reduce working capital was significant. The cash released was needed to fund an ambitious growth initiative.

• Define optimal stock- levels for raw-materials, work in process and finished goods
• Optimise the procurement portfolio through improved understanding and management of a volatile demand pattern
• Develop and implement control measures to ensure optimal stock levels are adhered to
• Identify and implement tools to digitalise / automate the supply-chain process and free up resources
• Reduce working capital between 8-10%

Our approach followed a two-stage process.

Stage 1: Assessment

• The assessment included a detailed review of the overall supply-chain along with an analysis of the three main inventory categories (raw material, WIP, finished goods).
  Volatility in the demand pattern was identified as one of the main factors responsible for the level of working capital.
• In addition, added complexity due to SKU proliferation was also a factor.

Stage 2: Recommendation & Implementation

• Based on the analysis findings, the following measures were introduced.
• The demand pattern was analysed statistically and broken down into components of more predictable demand and volatile demand. A model was created and a tool developed to more accurately predict finished goods requirements as well as procurement BOM based on historical yields and forecast demand.
• A ‘postponement strategy’ was introduced to increase WIP as opposed to finished SKUs to react more flexibly to fluctuations in demand at SKU level
  SKUs were both reduced and standardised to reduce the number of different raw-materials required
• A controlling feature was introduced to the ERP system to adjust stock using a combination of actual weekly demand and probability. This gives visibility of how well planners are managing stock.

• Inventory Management
• Statistical planning rules
• Process digitization
• Process optimization