Chapter 5 — full chapter
5.2 Supply Chain Optimization
A forecast is a number; a supply chain is a sequence of commitments made under it. How much to order and from where, how much safety stock to carry, which units go to which store or fulfillment center, when to replenish and when to stop: each decision is taken with incomplete information and, traditionally, defended with buffers. Those buffers are the waste. Long lead times are absorbed by ordering early and in bulk, uncertainty is absorbed by holding excess stock, and the residue is cleared through markdowns and, until the EU banned the practice, destruction.
This section treats those decisions as a continuous optimization problem, combining machine learning forecasts with operations research and network simulation. The environmental argument is that tighter matching removes the need for the buffers rather than disposing of their consequences, and that the same precision is what makes the small-batch and on-demand models of Chapter 4 economically viable.
5.2.1 From Forecast to Flow: Optimizing Sourcing, Allocation and Replenishment
Where forecasting produces a number, optimization turns that number into decisions: how much to order and when, how much safety stock to hold, how to allocate limited units across stores and channels, and how to route and replenish them. Data-driven supply chains treat this as a continuous optimization problem, combining machine-learning forecasts with operations-research methods and, increasingly, digital twins that simulate the whole network before committing real inventory. The goal is to match supply to demand tightly enough that the classic defenses of the fashion supply chain, long lead times, high safety stock and end-of-season markdowns, with destruction of unsold goods as the final valve, now banned in the EU under Regulation (EU) 2024/1781 (ESPR), are no longer needed. The sustainability payoff is direct: less overproduction, fewer returns and shorter lead times, which in turn make the small-batch, on-demand models of Chapter 4 economically viable.
5.2.2 Case example: Zalando, machine-learning allocation and returns optimization
Zalando[28], Europe's largest fashion platform, shows supply-chain optimization on the demand-facing side of the network. Machine-learning models allocate inventory across its fulfillment centers and pre-position stock close to where demand is expected, cutting delivery time and last-mile cost; unusually, they also fold returns directly into the optimization, accounting for items already in processing, returns in transit, and products statistically likely to be sent back, so each warehouse is stocked against its real available inventory rather than its nominal one. Because returns are the defining waste of online fashion, every rejected parcel means duplicate shipping, handling and often markdown or disposal, Zalando attacks the problem upstream too, with AI size-and-fit tools and a 3D virtual fitting room: it reports that in 2025 these prevented roughly 8% of size-related returns overall, with the fitting room cutting returns by up to 40% where used. The case makes the sustainability logic of optimization explicit: matching supply to demand tightly and preventing returns before they happen remove emissions and waste that no downstream recycling can recover (Zalando, 2025).
Figure 5.2, Zalando's AI size-and-fit tool: phone-based body measurements drive size recommendations that cut returns. (Source: zalando.com)