-
As brands scale across stores, channels, and seasonal launches, inventory problems rarely begin in the warehouse. They usually begin in planning. That is the lesson from Lush’s recent North America planning transformation. The beauty retailer now operates 850+ shops globally across 50 countries, including 250+ stores in North America, alongside 38 websites and a broad digital footprint. In 2024 alone, it sold more than 21 million bath bombs, on top of a larger portfolio spanning lotions, hair care, makeup, and other beauty products.
That scale created a specific planning challenge. Lush’s North America team had to support 250 stores, a digital fulfillment business, and two manufacturing sites in Vancouver and Toronto. At the same time, store managers retained substantial autonomy over ordering, rather than operating under a centralized push model. That flexibility helped preserve local ownership, but it also made demand and inventory planning harder, especially when layered on top of a portfolio that can reach about 1,000 SKUs at a time, including roughly 600 core products plus large seasonal and limited-edition launches such as a 150-SKU Christmas range.
The complexity was amplified by network design. Lush North America distributes all products from two distribution centers in Canada, while serving stores that can be thousands of miles away in the southern United States, increasing lead-time pressure and raising the cost of planning errors. In that context, spreadsheets stopped being a workable operating system. Lush’s demand planning manager, James Gregory, said directly that “spreadsheets were no longer a viable solution” for planning and forecasting as the business grew.
The real issue was not visibility. It was planning coordination.
What makes this case useful is that it highlights a broader supply-chain truth: when store autonomy, seasonal assortment complexity, and long replenishment lead times collide, spreadsheet-based planning tends to break first. Lush needed to preserve local inventory control while still ensuring that products were in the right place at the right time. According to the case study, the company’s manual processes had become cumbersome and inefficient, making it difficult to maintain data integrity and generate reliable forecasts.
That matters because forecasting errors create two kinds of cost at once. One is lost sales, when stores do not have the products customers want. The other is preventable operating cost, when a business has to expedite shipments or carry misallocated inventory to compensate. Inbound Logistics notes that better forecasting can reduce both effects by improving inventory placement and lowering the need for costly exception handling.
The intervention: replace spreadsheet planning with a collaborative planning system
Lush implemented several planning solutions from Arkieva, a supply-chain planning software provider focused on demand, inventory, and supply planning. The result, according to the case study, was a more streamlined forecasting process, better inventory visibility, and stronger demand-planning insight for materials planning. Lush also gained the ability to assess how demand changes would affect revenue more quickly.
The most important outcome was not generic “digitization.” It was better control at the SKU and store level. Gregory said the team is now better able to direct where inventory needs to be, including more precisely accounting for how seasonal products affect year-round sales. That is the real operating win here: not more dashboards, but more accurate allocation decisions inside a high-variability retail model.
Why this case matters beyond beauty retail
The Lush example is relevant well beyond cosmetics. It reflects a planning problem many multi-store brands face once they scale: local flexibility starts to collide with central planning discipline. The more channels, launches, and SKUs a network carries, the harder it becomes to rely on manual files without creating forecast drift, inventory imbalance, and slower decisions. This is especially true when replenishment lead times are long and seasonal spikes materially reshape demand patterns. The case study supports this interpretation through Lush’s store autonomy model, seasonal assortment breadth, and long-distance distribution structure.
What stands out strategically is that Lush did not appear to solve the problem by eliminating store autonomy. Instead, it strengthened the planning layer underneath it. That is an important distinction. In modern retail supply chains, the answer is often not to centralize every decision, but to create a planning system strong enough to support decentralized execution without losing control. This is an inference based on the case-study details about store-manager autonomy and the reported forecasting improvements.
The executive takeaway
Lush’s case points to a broader shift in retail supply-chain strategy: growth does not break operations first, unmanaged planning complexity does. Once assortment breadth, store autonomy, seasonality, and long lead times reach a certain scale, spreadsheet planning stops being a low-cost workaround and starts becoming a service-risk multiplier.
The companies that adapt fastest are likely to be the ones that build collaborative planning capabilities before those cracks widen. Not because planning software is inherently strategic, but because inventory precision becomes strategic when every stockout, markdown, and expedite decision compounds across hundreds of stores. That final point is an inference from the Lush case and the operational outcomes described.
Why it matters:
For multi-store consumer brands, the next margin and service gains may come less from pushing more product through the network and more from improving the quality of the forecast that decides where that product should go in the first place.
