Strategic Supply Chain Optimization through End-to-End Cost Modelling

In today’s highly competitive retail environment, optimizing supply chain operations is not just a tactical move – it’s a key strategic lever for improving margins, ensuring product availability, and supporting sustainability goals. However, when imagining the country’s largest food and FMCG retailer, operating over 300 stores of varying formats (hypermarkets, supermarkets, convenience stores) and a fast-growing e-commerce channel, the complexity grows exponentially.

Each product, store, and process step adds layers to a network where trade-offs between cost efficiency and service level are constant. Understanding this ecosystem holistically – and identifying where to intervene – requires more than just data: it demands a rigorous, detailed quantification of the end-to-end supply chain.

We partnered with the retailer to unravel this complexity and create actionable insights. The goal was to minimize the total supply chain cost while considering operational requirements. This meant going beyond traditional siloed analysis to systematically assess and optimize key parameters – such as the order picking policy (the warehouse logistical flow, e.g., switching from a stock policy to a flow-through with preparation) and the size of pack delivered to stores (e.g., carton size) – for each SKU.

The challenge lay in developing a methodology capable of weighing the intricate cost implications both upstream (e.g.,warehouse handling, transport) and downstream (e.g., store replenishment, shelf stocking), ensuring decisions enhanced overall efficiency without simply shifting costs from one part of the chain to another.

The aforementioned digital twin model was developed using advanced analytica0l methodologies that enabled the creation of distinct what-if scenarios to be used by multiple stakeholders across the organization’s structure – the opportunities and value to be captured were quickly evident, as different approaches could easily be simulated and quantified.

One key feature of the methodology was the ability to embody different characteristics regarding specific product subsets (e.g. products from a supplier which only works with one logistical flow, implying that changes should be simulated to all of its products) and store characteristics (e.g. different formats, such as proximity stores, may not be able to handle bigger boxes, so pack increments must take capacity into account).

Stakeholder adoption was key for the success ofthe project, and the rollout across the company was scheduled according to deployment sprints, ensuring smooth implementation based on a logic of “value-first” – the first business units to access the tool were the ones were the value to be captured was most evident and clear.

A global analysis of logistical flow changes across main FMCG categories (foods, beverages, pet food, etc.) showcased significant savings when switching ~20% of products from a stock policy to a flow through with preparation one, as warehouse space and operations savings were more than enough to cover the increase in work load and store space, amounting to over 8% in direct total savings across the supply chain.

Fresh produce and frozen goods were the key focus on the first iteration of pack sizing adjustments, with promising results which suggested mixed adjustments in increase/decrease of pack and box size –the key takeaway was a reduction of approximately 9% in total supply chain costs, highlighting the significant savings potential to be captured in optimal pack sizing.

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