# Optimising for uncertainty: turning supply chains from pain points to value creators

> White paper · 8 pages · 2026-08-04

A QuantSpark case study on turning supply chains from a pain point into a value creator. During global disruption we rebuilt a premium fashion retailer's stock planning at SKU level, delivering a $3.8m net gain, over $12m in working capital and 30% more factory capacity.

- **$3.8m** Net gain over six months, after air-freight costs (QuantSpark client engagement)
- **$12m+** Working capital saved by removing excess stock
- **30%** Additional factory capacity created for future seasons

## Why it matters

- **Traditional MRP breaks under real disruption** Off-the-shelf material requirements planning systems cannot dynamically adjust when constraints hit at once: factory power cuts, freight shortages and customs delays. When they cannot reprioritise, late stock lands out of season and margin leaks quietly away.
- **The value hides in SKU-level visibility** A monthly, aggregated view of stock masks the real picture. The decisions that protect margin, which lines to air-freight, which orders to cut, live at the level of individual colours, sizes and styles. Daily SKU granularity is where the money is.
- **Disruption is recurring, not a one-off** Global supply-chain shocks keep coming. Every FMCG, retail and manufacturing business is exposed, so resilience has to be designed into the planning model rather than bolted on after the next crisis.
- **Your own data is the asset to build on** Most businesses already hold the data they need; what is missing is a model that turns it into daily, actionable decisions. A bespoke, adjustable model on your existing data is what turns a supply chain from a cost centre into a value creator. Talk to us about doing the same.

## Introduction

**Predicting and managing supply during unpredictable global conditions is hard, and getting it wrong is costly.** Consumer retail, FMCG and manufacturing all depend on a visible, reliable flow of products and parts to hold healthy stock levels. When global supply chains seize up, traditional stock planning and forecasting systems cannot adapt to logistical limits. The knock-on effects erode margin and, ultimately, lose revenue.

This paper sets out the opposite case. QuantSpark's work with a premium retail brand shows that, with dynamic, data-driven planning systems and models in place, a business can not only avoid losses but create significant real-terms value.

![Headline impact of QuantSpark's predictive modelling on the client's supply chain.](https://baytqnxeuvjwpvnwqpvj.supabase.co/storage/v1/object/public/website-assets/white-papers/optimising-for-uncertainty/page-3-impact.png)

## Our client's problem

**Global disruption exposed the limits of traditional planning.** Our client, a private-equity-backed British premium fashion brand with annual revenues of over £100m, faced shortages driven by a wide array of external factors: heavy manufacturing delays and hard limits on Chinese industrial capacity; sea-freight delays and price hikes; UK and European customs delays; and transport staff shortages. As a premium brand, late stock risked pushing deliveries out of season and rapidly cutting both the likelihood of purchase and retail value.

The client faced three key stock-arrival issues:

- **Increased demand.** Having outsold projections for several years, required unit volumes were uncharacteristically high.
- **Decreased factory capacity.** Chinese manufacturers had been hit by government-mandated power cuts, reducing capacity on most production lines by close to 50%.
- **Global shortage of ocean freight.** Following the effects of the Covid-19 pandemic, both costs and lead times had risen sharply, by an average of four weeks.

The client's existing MRP system could not dynamically adjust to these constraints or prioritise seasonal key products for delivery. QuantSpark's task was to take purchase-order data from the current material requirements planning system, enrich it with data from across the business, weigh the constraints and priorities, and produce an optimised purchase-order solution that maximised availability while minimising cost.

## The analytics case: data rich, insight poor

**A lack of data visibility compounded the MRP limitations.** Mirroring a common trend in modern business, the client held an extensive collection of historic sales and purchase orders alongside usage prediction. What was missing was SKU-level granularity, the resolution needed to navigate the data and produce daily, actionable insight.

## QuantSpark's solution: the approach

The client's particular mix of factory conditions and freight logistics called for a custom model, built in three moves.

**Build absolute clarity on stock positions.** We constructed a sawtooth graph from purchase and sales information. The client had previously viewed stock at a monthly level, aggregated roughly, with limited visibility of individual colours, sizes and styles. A custom sawtooth function lifted this to the daily SKU granularity the business needed.

**Develop a strategy for stock-shortfall minimisation.** Although many SKUs ended in shortfall, the business held an overall positive stock position thanks to excess stock in other lines. Much of that excess was already warehoused, and a further proportion was set to arrive in future purchase orders.

**Redirect capacity from excess to need.** Reducing future orders of SKUs already in excess freed factory capacity that could be transferred to key seasonal products and products in shortfall. Combined with price-constrained optimisation of air-freight volumes, the model delivered drastic stock-position improvements while minimising freight costs.

## The model: four custom modules

**Straightforward in concept, complex in execution.** Our bespoke model used existing purchase-order data, supplemented it with additional sources to increase resolution, and implemented four custom modules to uplift stock shortfalls. It ran several thousand times to factor in the various situational constraints, driven by computational methods and measured by a proprietary KPI that assessed unacceptably late fulfilments.

**1. Increase on-time, in-full orders.** The model found SKUs spending time in shortfall and reassigned them from sea to air freight so they arrived in time to lift on-time, in-full (OTIF) orders. Limits on air-freight volumes could be set, and SKUs were prioritised by lowest cost to air, or highest remaining margin after air costs.

**2. Remove excess stock.** The model identified SKUs ending the period in excess and reduced prior purchase orders to remove it, careful not to drop a product into shortfall. A safety net of stock could be specified to prevent stockouts. This trimming increased capacity on both factory lines and freight.

**3. Optimise factory capacity around shipping dates.** Switching low-priority, air-freight-viable SKUs from sea to air freed factory capacity early in the season. Shorter lead times let the factory manufacture later in the period, so higher-priority SKUs could be produced.

**4. Redeploy manufacturing capacity to SKUs in danger of shortfall.** Finally, the model took all newly created factory and freight capacity and allocated it to the highest-priority SKUs with the greatest shortfalls.

> The solution was straightforward in concept but complex to implement: the model ran several thousand times to factor in the various situational constraints.

![The four optimisation modules, shown on an illustrative example SKU: on-time in-full uplift, excess-stock removal, factory-capacity optimisation, and redeployment to shortfall. Charts are illustrative single-SKU examples.](https://baytqnxeuvjwpvnwqpvj.supabase.co/storage/v1/object/public/website-assets/white-papers/optimising-for-uncertainty/page-6-modules.png)

## Conclusion: a clear return on investment

The model's effectiveness at optimising factory and freight capacity generated a clear return for the client.

**Revenue recovered.** By capturing demand that would otherwise have been missed as SKUs went into shortfall, the model contributed to a $6m gross revenue uplift over six months. After the tactical use of air freight to maximise OTIF deliveries, the client's net gain was still in excess of $3.8m.

**Working capital released.** The removal of over 420k units of excess stock, and all the associated manufacture, transport and warehousing costs, saved over $12m in working capital that could be reinvested in the business.

**Resilience built in.** The model's dynamic redeployment of manufacturing baked in resilience against future shocks by increasing factory capacity by 30%. That capacity could be used to make next season's products ahead of time, which in turn meant products could ship by cheaper ocean freight, saving money and returning value to the client once again.

- **$6m** Gross revenue uplift over six months

- **420k+** Units of excess stock removed

![Aggregate results across the engagement: initial versus final excess-stock balance, and factory capacity gained month on month.](https://baytqnxeuvjwpvnwqpvj.supabase.co/storage/v1/object/public/website-assets/white-papers/optimising-for-uncertainty/page-7-results.png)

## Key takeaway: use your own data to optimise stock and planning

**Disruption is not a one-off.** Supply-chain and stock-management disruptions are global, and every FMCG, retail and manufacturing business is at risk.

Broad, durable data models support the widest range of insight and reporting, with the least maintenance and rework. They are what turns your data, an intangible asset, into liquidity.

Many off-the-shelf MRP solutions lack the functionality to respond to unforeseen limits in supply-chain links, and almost all lack the ability to prioritise certain products during such events. None provide bespoke, adjustable parameterisation to your specific scenario. With QuantSpark's supply chain knowledge and analytical expertise, you can increase your business's robustness in the face of disruption, safeguarding your investment and ultimately improving your revenues.

> Supply chain disruptions and stock management disruptions are not one-off events.

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