Industries: Supply chain planning for the consumer goods industry
Our team has developed a deep understanding of the challenges of running high volume, fast moving supply chains. We understand that driving consistent ways of working across the planning processes is critical. The more you can standardise the processes, the more you can automate your supply chain planning. This frees up valuable time to plan strategically and means you can provide reliable accurate numbers to feed into your monthly planning cycles.
The challenges
FMCG. It’s a fast-paced market with pressure from competitors fighting for shelf space, with extra cost and complexity coming through increased customisation and personalisation. It’s vital to develop a supply chain strategy which lets you react with agility whilst containing your cost footprint.
Key challenges
- • Staying ahead of your competition will require effective management of the SKU portfolio. To avoid obsolescence and maintain on-shelf availability, it’s critical to be able to plan with shelf life and understand how you can phase in and phase out.
- • Converting long term forecasts into short term operational demand plans can be challenging, when you have to deal with the impact of one off events and promotional impacts across the market place.
- • You may also face many different supply challenges, including complex change over strategies, group planning, balancing multiple sources of supply or planning with intermediate constraints.
How we can help
We are experts in designing and implementing advanced planning solutions that run some of the world’s largest FMCG supply chains, including Diageo, Unilever, McCormick, Kerry’s, Kellogg’s, Carlsberg and BAT . Examples of solutions we have developed for the consumer product supply chains include:
Olivehorse solutions
- • Supply network optimisation models to help balance manufacturing capacity with inventory across hundreds of locations with many thousands of SKU codes
- • Planning critical bottlenecks at finished good and intermediate levels to produce a constrained production plan
- • Models for complex network relationships between pack lines and intermediate mixing resources, including tank capacity constraints
- • Models for complex sequencing problems to help reduce changeover losses
- • Planning solutions using Product Interchangeability function in SNP to help model discontinuation
- • Detailed statistical forecast assessment, analysis and roadmap to ensure optimal level of statistical forecast generation
A few of our clients
