demand forecasting
Forecast each store and SKU, and hold less cash in stock
- Pilot
- 10–16 weeks
- Starts with
- A diagnostic, typically 1–3 weeks
- Measured against
- Your own agreed baseline
The problem
Does this sound familiar?
Who it is for
Grocery and fashion retailers, FMCG companies and pharmaceutical distributors. The system does the arithmetic and shows its reasons; planners make the call.
A typical situation
We run out of fast sellers while the warehouse is full of slow ones. Replenishment depends on a few experienced planners and their spreadsheets. In fashion we send the wrong sizes to the wrong stores and mark down too late. In FMCG and pharma we write off expired stock and handle returns we could have avoided.
Outcomes
What Demand Forecasting & Inventory Optimisation can improve
Indicative ranges, validated in your pilot. These figures come from comparable industry programmes, not from TashIT projects. Your pilot sets a baseline with you and measures the actual change.
- Indicative
Inventory and working capital
Typically 10-15% lower inventory at the same or better availability
Basis: Store-SKU forecasting and replenishment programmes in grocery, FMCG and fashion. Depends on current forecasting practice, supplier lead times and master-data quality.
- Indicative
Stock-outs on core lines
Typically 20-40% fewer
Basis: Better forecasts and replenishment parameters for high-velocity SKUs. Depends on supplier reliability and order frequency.
- Indicative
Markdown and margin
Typically 1-3 percentage points of gross margin gained in fashion categories
Basis: Size-curve allocation and earlier, data-led markdown decisions. Depends on range depth, season length and pricing policy.
- Indicative
Expiry and returns
Typically 15-30% lower write-offs
Basis: Shelf-life-aware replenishment and early transfer of near-expiry stock in grocery, FMCG and pharmaceutical distribution. Depends on shelf life and returns terms.
From data to decision
How demand forecasting works
Step 1: Clean the history
Sales, stock, prices and promotions are brought together. Stock-out periods are identified so lost sales are not mistaken for low demand, and new products are mapped to similar existing ones.
Step 2: Forecast demand by store and SKU
Machine learning models forecast by store, SKU and day or week, accounting for seasonality, promotions, price changes, holidays that move each year such as Ramadan and Eid, and weather where it matters. Accuracy is reported by category against your current method.
Step 3: Set replenishment and allocation
Order quantities and safety stock are calculated per SKU and location from forecasts, lead times, pack sizes and service targets. Fashion uses size-curve allocation by store; perishables and pharma use first-expiry, first-out rules.
Step 4: Guide markdowns, transfers and returns
Sell-through tracking flags slow lines early and recommends store-to-store transfers, markdown timing and depth, and returns to suppliers before stock expires.
Step 5: Review with planners
Recommendations appear in a planner workbench or as suggested orders in your ERP. Planners approve or override them and record the reason, and forecast accuracy and service levels are reviewed weekly.
Data and integration
What we need, and what it connects to
Data we need
- 2-3 years of sales by store, SKU and day or week
- Stock on hand, receipts, transfers and stock-out history
- Product master: category, size, colour, pack size, shelf life and supplier
- Promotion, price and markdown history
- Supplier lead times, minimum order quantities and delivery schedules
- Store attributes: size, format, location and opening dates
Integrates with
- Point-of-sale (POS) systems
- ERP and merchandising systems (SAP, Oracle, Microsoft Dynamics, Odoo, LS Retail and local systems)
- Warehouse management systems
- Distributor management and sales-force automation systems
- E-commerce platforms and marketplaces
- BI tools such as Power BI
How the pilot works
Piloting demand forecasting
Duration
- 10–16 weeks
Scope
- One category or a cluster of 3-20 stores. Forecasts and replenishment recommendations first run alongside current planning, then are used live for part of the pilot against comparable control stores.
Success measures
Agreed in writing before work starts, with targets set against your baseline.
- Forecast accuracy and bias against your current method
- Availability on core lines in pilot stores versus control stores
- Inventory days of cover in pilot scope
- Markdown, expiry or return value in pilot categories
The pilot follows a short diagnostic and ends with a written results review. Scaling up is a separate decision, made on the measured results. How our engagements work
- See the solution blueprint
Solution blueprint
Store-level demand forecasting and auto-replenishment
SKU-by-store forecasts that become suggested orders and transfers every day, so fast movers stay on the shelf while slow and short-dated stock stops building up.
Status: Reference architecture and pilot plan, available for a paid proof of concept. Reference material, not a client project.
Where it applies
Industries and capabilities
Industries where it applies
- Retail & DistributionGrocery, fashion and national distributors: forecasting, allocation, shrink and route cost.
- Food & AgribusinessSugar, dairy, food processing and farming: procurement integrity, yield and cold chain.
- Logistics & FuelFuel retail, ports, fleets and couriers: stock integrity, planning and turnaround time.
- HealthcareHospitals, laboratories and insurers: claims, receivables, patient flow and integration.
Capabilities behind this solution
- AI & Machine LearningCustom models, AI agents, computer vision, predictive analytics and private AI deployments, integrated with the systems you already run.
- Software EngineeringEnterprise applications, SaaS platforms, ERP and CRM extensions, APIs and cloud systems engineered for long-term operation.
- Business Process AutomationWorkflow automation, finance and inventory integrations, automated reporting and document processing that remove manual work.
- Web & Mobile DevelopmentFast, accessible websites, web applications, customer portals and mobile apps for growing businesses and enterprises.
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FAQs
Questions about demand forecasting
Something else to ask?
Email your question with a little about your site and systems, and we will reply in plain language.
info@tashit.comOur data is messy. Can we still start?
Usually, yes. Most retailers and distributors have usable sales history even when master data needs work. The diagnostic measures data quality first and tells you what must be fixed before forecasts can be trusted.
Will the system place orders automatically?
Only if you want it to. We recommend starting with suggestions that planners approve. Automatic ordering can be switched on for stable lines once accuracy has been demonstrated over several order cycles.
How do you handle new products and seasonal events?
New products borrow demand patterns from similar items, adjusted as real sales arrive. Events that move each year, such as Ramadan, Eid and school terms, are modelled explicitly rather than treated as noise.
Ready to scope a pilot?
Tell us about your site or process and the data you already have. We will reply with a diagnostic plan, the measures we would propose for a pilot and an honest view of feasibility.