Netherlands-based small enterprise offers a cloud platform for demand forecasting, inventory optimisation and supply chain planning; goes live in weeks not months.
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Term of Validity: 9 September 2026 - 9 September 2027
Summary - Profile Type - Technology offer - POD Reference - TONL20260909027 - Term of Validity - 9 September 2026 - 9 September 2027 - Company’s Country - Netherlands - Type of partnership - Commercial agreement with technical assistance - Targeted Countries - All countries General information - Short Summary - A Dutch small enterprise offers a cloud platform for demand forecasting, inventory optimisation and supply chain planning for mid-sized manufacturers, wholesalers and retailers. It combines machine learning and statistical forecasting with cost-optimal stock allocation, works alongside the existing enterprise resource planning system, and goes live in weeks not months. The company seeks commercial agreements with technical assistance and supplier agreements with European resellers. - Full Description - A Netherlands-based small enterprise, founded by a supply chain architect with over fifteen years of experience implementing planning systems for large international manufacturers, retailers and consumer goods companies, offers a cloud platform for demand forecasting, inventory optimisation and supply chain planning. The offer addresses a clear market gap. Companies turning over between ten million and one billion euro have supply chains that have outgrown spreadsheets, yet are poorly served by the established planning vendors, whose systems need twelve to eighteen months, an internal project team, external consultants, and licence fees only very large organisations can absorb. These companies therefore keep planning in spreadsheets, with familiar consequences: working capital tied up in slow-moving stock, simultaneous shortages of fast-moving items, forecasts based on judgement rather than evidence, and planning knowledge that leaves when an experienced employee retires. The state of the art falls into two groups. At the upper end sit large integrated planning suites, capable but expensive, slow to deploy and consultant-dependent. At the lower end sit lightweight ordering tools, often bundled with enterprise resource planning software, which deploy quickly but rely on simple reorder-point rules, with no probabilistic forecasting and no cost-service optimisation. Little exists between the two. The platform is built for that middle ground: a multi-tenant cloud service that sits alongside the customer’s enterprise resource planning system rather than replacing it. Data is exchanged by secure file transfer, cloud object storage or a programming interface, keeping integration effort low for companies with small information technology teams. Onboarding is a configuration exercise rather than a development project, which makes a live deployment within thirty to ninety days realistic. The platform runs ten forecasting models per stock item, seven deep learning architectures and three statistical methods, and automatically selects the best performer for each item on back-tested accuracy. Pre-trained foundation models give usable forecasts even where sales history is short. Stock items are classified by value, variability and demand pattern, and safety stock is allocated by marginal cost analysis, placing each additional unit where it cuts total cost most rather than applying one blanket service level per category. An optional reinforcement learning agent refines replenishment proposals on top of the classical policy, always retained as a fallback. Sales and operations planning scenarios are balanced against budget and capacity constraints, and results are delivered into standard business intelligence tools. A bidirectional integration with a widely used enterprise planning suite lets customers keep their planning interface while replacing the forecasting engine underneath. A knowledge management module preserving institutional planning expertise is in development. Application fields include discrete and process manufacturing, wholesale and technical distribution, fast-moving consumer goods, retail, and pharmaceutical distribution. A commercial agreement with technical assistance was selected because the technology requires joint integration work with each customer’s data environment, delivered by the company directly. A supplier agreement is offered in parallel because reaching customers in other European markets efficiently requires resellers and consultancies that already hold trusted relationships there. The desired outcome is a small number of long-term partners in Germany, Austria, Switzerland, France, the Nordic countries and Iberia who resell or embed the platform, deliver first-line implementation locally, and feed market requirements into the roadmap. Cooperation would begin with a joint pilot at a partner customer, followed by a reseller or referral agreement, consultant enablement, and an agreed commercial model. - Advantages and Innovations - • Positioned deliberately between enterprise planning suites and basic reorder-point tools, a segment that is largely unserved for mid-sized companies • Configuration-based onboarding rather than a development project, targeting a live deployment in thirty to ninety days against the twelve to eighteen months typical of enterprise suites • Ten forecasting models per stock item, seven deep learning and three statistical, with automatic per-item selection on back-tested accuracy rather than one model applied across the whole assortment • Pre-trained time series foundation models give usable forecasts for customers with short or incomplete sales history, where conventional statistical methods fail • Safety stock allocated by marginal cost analysis, placing each incremental unit where it reduces total cost most, instead of the flat class-level service levels used by most competing tools • Cost curves derived from the customer’s own holding, ordering and shortage costs rather than vendor defaults • Variability classification computed from forecast error rather than raw historical demand variance, giving a materially more accurate picture of which items are genuinely unpredictable • Reinforcement learning applied as a correction on top of a classical replenishment policy, never as a replacement, so proposals cannot fall below the conventional baseline • Works alongside the existing enterprise resource planning system, so the customer replaces no core system and retains their planners in control of every decision • Consumption-based cloud pricing removes the upfront licence and infrastructure investment that normally blocks this segment - Stage of Development - Already on the market - Sustainable Development Goals - Goal 9: Industry, Innovation and Infrastructure - IPR status - Secret know-how Partner Sought - Expected Role of a Partner - The company is looking for two complementary partner types across Europe, with priority on Germany, Austria, Switzerland, France, the Nordic countries, Belgium and Iberia. First, resellers, value-added distributors, systems integrators and supply chain consultancies. These partners should already sell software, consulting or implementation services to mid-sized manufacturers, wholesalers, technical distributors, consumer goods companies, retailers or pharmaceutical distributors in their home market, and should hold established relationships with operations, supply chain and finance decision makers in those companies. Enterprise resource planning implementation partners and business intelligence consultancies are a particularly good fit, because the platform complements rather than competes with what they already deliver. Expected tasks for this partner type are: identifying and qualifying suitable customers in their market; positioning and selling the platform, either as a resale or on a referral basis; running a first joint pilot with one of their existing customers; delivering first-line implementation and support locally, including data mapping from the customer’s enterprise resource planning system; and providing local language support where the market requires it. The company will train and certify the partner’s consultants, provide second-line technical support, handle all platform engineering and hosting, and support the first joint opportunities directly alongside the partner. Second, end users willing to act as early adopters under a commercial agreement with technical assistance. These are mid-sized manufacturers, wholesalers, distributors or retailers with at least two years of transactional sales history, an operational enterprise resource planning system, and a recognised problem with either forecast accuracy, working capital tied up in stock, or service levels. They are expected to provide access to historical sales, stock and item master data, nominate an internal planning owner, participate in a structured onboarding, and give structured feedback on forecast quality and stock recommendations. In exchange they receive direct access to the development team, influence over the product roadmap, and early-adopter commercial terms. For both partner types the company is interested in long-term relationships rather than single transactions, and expects to agree measurable success criteria at the outset, such as forecast accuracy improvement, stock reduction at constant service level, and reduction in manual planning effort. Partners should be comfortable with a cloud-delivered subscription model and with a solution that integrates with, rather than replaces, the customer’s existing systems. Experience with demand planning, inventory management or sales and operations planning is valuable but not a precondition, as full enablement is provided - Type and Size of Partner - SME 11-49 - SME 50 - 249 - SME <=10 - Big company - Type of partnership - Commercial agreement with technical assistance Dissemination - Technology keywords - 01003003 - Artificial Intelligence (AI) - 01003010 - Databases, Database Management, Data Mining - 01003008 - Data Processing
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