Nov 15, 2025·8 min
Create a Web App for Inventory Forecasting & Demand Planning
Plan and build a web app for inventory forecasting and demand planning: data setup, forecasting methods, UX, integrations, testing, and deployment.
Plan and build a web app for inventory forecasting and demand planning: data setup, forecasting methods, UX, integrations, testing, and deployment.
An inventory forecasting and demand planning web app helps a business decide what to buy, when to buy it, and how much to buy—based on expected future demand and current inventory position.
Inventory forecasting predicts sales or consumption for each SKU over time. Demand planning turns those predictions into decisions: reorder points, order quantities, and timing that align with service goals and cash constraints.
Without a reliable system, teams often rely on spreadsheets and gut feel. That typically leads to two costly outcomes:
A well-designed inventory forecasting web app creates a shared source of truth for demand expectations and recommended actions—so decisions stay consistent across locations, channels, and teams.
Accuracy and trust are built over time. Your MVP demand planning app can start with:
Once users adopt the workflow, you can steadily improve accuracy with better data, segmentation, promotions handling, and smarter models. The goal isn’t a “perfect” forecast—it’s a repeatable decision process that gets better every cycle.
Typical users include:
Judge the app by business results: fewer stockouts, lower excess inventory, and clearer purchasing decisions—all visible in an inventory planning dashboard that makes the next action obvious.
An inventory forecasting web app succeeds or fails on clarity: what decisions will it support, for whom, and at what level of detail? Before models and charts, define the smallest set of decisions your MVP must improve.
Write them as actions, not features:
If you can’t tie a screen to one of these questions, it likely belongs in a later phase.
Pick a horizon that matches lead times and buying rhythm:
Then choose the cadence for updates: daily if sales change quickly, weekly if purchasing happens on set cycles. Your cadence also determines how often the app runs jobs and refreshes recommendations.
The “right” level is the level people can actually buy and move inventory:
Make success measurable: service level / stockout rate, inventory turns, and forecast error (e.g., MAPE or WAPE). Tie metrics to business outcomes like stockout prevention and reduced overstock.
MVP: one forecast per SKU(-location), one reorder point calculation, a simple approve/export workflow.
Later: multi-echelon inventory optimization, supplier constraints, promotions, and scenario planning.
Forecasts are only as useful as the inputs behind them. Before choosing models or building screens, get clear on what data you have, where it lives, and what “good enough” quality means for an MVP.
At minimum, inventory forecasting needs a consistent view of:
Most teams pull from a mix of systems:
Decide how often the app refreshes (hourly, daily) and what happens when data arrives late or is edited. A practical pattern is to keep immutable transaction history and apply adjustment records rather than overwriting yesterday’s numbers.
Assign an owner for each dataset (e.g., inventory: warehouse ops; lead times: procurement). Maintain a short data dictionary: field meaning, units, timezone, and allowed values.
Expect issues like missing lead times, unit conversions (each vs. case), returns and cancellations, duplicate SKUs, and inconsistent location codes. Flag them early so your MVP can either fix, default, or exclude them—explicitly and visibly.
A forecasting app succeeds or fails on whether everyone trusts the numbers. That trust starts with a data model that makes “what happened” (sales, receipts, transfers) unambiguous, and makes “what is true right now” (on-hand, on-order) consistent.
Define a small set of entities and stick to them across the whole app:
Choose daily or weekly as your canonical time grain. Then force every input to match it: orders might be timestamped, inventory counts might be end-of-day, and invoices might post later. Make your alignment rule explicit (e.g., “sales belong to the ship date, bucketed to day”).
If you sell in each/case/kg, store both the original unit and a normalized unit for forecasting (e.g., “each”). If you forecast revenue, keep original currency plus a normalized reporting currency with an exchange-rate reference.
Track inventory as a sequence of events per SKU-location-time: on-hand snapshots, on-order, receipts, transfers, and adjustments. This makes stockout explanations and audit trails far easier.
For every key metric (unit sales, on-hand, lead time), decide one authoritative source and document it in the schema. When two systems disagree, your model should show which one wins—and why.
A forecasting UI is only as good as the data feeding it. If numbers change without explanation, users stop trusting the inventory planning dashboard—even when the model is fine. Your ETL should make data predictable, debuggable, and traceable.
Start by writing down the “source of truth” for each field (orders, shipments, on-hand, lead times). Then implement a repeatable flow:
Keep two layers:
When a planner asks, “Why did last week’s demand change?”, you should be able to point to the raw record and the transform that touched it.
At minimum, validate:
Fail the run (or quarantine the affected partition) rather than silently publishing bad data.
If purchasing runs weekly, a daily batch is usually enough. Use near-real-time only when operational decisions depend on it (same-day replenishment, rapid e-commerce swings), because it increases complexity and alert noise.
Document what happens on failure: which steps retry automatically, how many times, and who gets notified. Send alerts when extracts break, row counts drop sharply, or validations fail—and keep a run log so you can audit every forecast input.
Forecasting methods aren’t “better” in the abstract—they’re better for your data, SKUs, and planning rhythm. A great web app makes it easy to start simple, measure results, then graduate to advanced models where they actually pay off.
Baselines are fast, explainable, and excellent sanity checks. Include at least:
Always report forecast accuracy versus these baselines—if a complex model can’t beat them, it shouldn’t be in production.
Once the MVP is stable, add a few “step-up” models:
You can ship faster with one default model and a small set of parameters. But you’ll often get better results with per-SKU model selection (choose the best model family based on backtests), especially when your catalog mixes steady sellers, seasonal items, and long-tail products.
If many SKUs have lots of zeros, treat that as a first-class case. Add methods suited for intermittent demand (e.g., Croston-style approaches) and evaluate with metrics that don’t punish zeros unfairly.
Planners will need overrides for launches, promotions, and known disruptions. Build an override workflow with reasons, expiry dates, and an audit trail, so manual edits improve decisions without hiding what happened.
Forecasting accuracy often rises or falls on features: the extra context you provide beyond “sales last week.” The goal isn’t to add hundreds of signals—it’s to add a small set that reflects how your business behaves and that planners can understand.
Demand usually has a rhythm. Add a few calendar features that capture that rhythm without overfitting:
If promotions are messy, start with a simple “on promo” flag and refine later.
Inventory forecasting is not just demand—it’s also availability. Useful, explainable signals include price changes, lead time updates, and whether a supplier is constrained. Consider adding:
A stockout day with zero sales does not mean zero demand. If you feed those zeros directly, the model learns that demand vanished.
Common approaches:
New items won’t have history. Define clear rules:
Keep the feature set small and name features in business terms inside the app (e.g., “Holiday week” not “x_reg_17”) so planners can trust—and challenge—what the model is doing.
A forecast is only useful when it tells someone what to do next. Your web app should convert predicted demand into specific, reviewable purchasing actions: when to reorder, how much to buy, and how much buffer to carry.
Start with three outputs per SKU (or SKU-location):
A practical structure is:
If you can measure it, include lead time variability (not just average lead time). Even a simple standard deviation per supplier can noticeably reduce stockouts.
Not every item deserves the same protection. Let users choose service level targets by ABC class, margin, or criticality:
Recommendations must be feasible. Add constraint handling for:
Every suggested purchase should include a short explanation: forecasted demand over lead time, current inventory position, chosen service level, and the constraint adjustments applied. This builds trust and makes exceptions easy to approve.
A forecasting app is easiest to maintain when you treat it as two products: a web experience for people, and a forecasting engine that runs in the background. That separation keeps the UI fast, prevents timeouts, and makes results reproducible.
Start with four building blocks:
The key decision: forecasting runs should never execute inside a UI request. Put them on a queue (or scheduled jobs), return a run ID, and stream progress in the UI.
If you want to accelerate the MVP build, a vibe-coding platform like Koder.ai can be a practical fit for this architecture: you can prototype a React-based UI, a Go API with PostgreSQL, and background-job workflows from a single chat-driven build loop—then export the source code when you’re ready to harden or self-host.
Keep “system of record” tables (tenants, SKUs, locations, run configs, run status, approvals) in your primary database. Store bulk outputs—per-day forecasts, diagnostics, and exports—in tables optimized for analytics or in object storage, then reference them by run ID.
If you serve multiple business units or clients, enforce tenant boundaries in the API layer and database schema. A simple approach is tenant_id on every table, plus role-based access in the UI. Even a single-tenant MVP benefits from this because it prevents accidental data mixing later.
Aim for a small, clear surface area:
POST /data/upload (or connectors), GET /data/validationPOST /forecast-runs (start), GET /forecast-runs/:id (status)GET /forecasts?run_id=... and GET /recommendations?run_id=...POST /approvals (accept/override), GET /audit-logsForecasting can get expensive. Limit heavy retrains by caching features, reusing models when configs don’t change, and scheduling full retrains (e.g., weekly) while running lightweight daily updates. This keeps the UI responsive and your budget stable.
A forecasting model is only valuable if planners can act on it quickly and confidently. Good UX turns “numbers in a table” into clear decisions: what to buy, when to buy it, and what needs attention right now.
Start with a small set of screens that map to daily planning tasks:
Keep navigation consistent so users can jump from an exception to the SKU detail and back without losing context.
Planners slice data constantly. Make filtering instant and predictable for date range, location, supplier, and category. Use sensible defaults (e.g., last 13 weeks, primary warehouse) and remember the user’s last selections.
Build trust by showing why a forecast changed:
Avoid heavy math in the UI; focus on plain-language cues and tooltips.
Add lightweight collaboration: inline notes, an approval step for high-impact orders, and change history (who changed the forecast override, when, and why). This supports auditability without slowing down routine decisions.
Even modern teams still share files. Provide clean CSV exports and a print-friendly order summary (items, quantities, supplier, totals, requested delivery date) so purchasing can execute without reformatting.
Forecasts are only as useful as the systems they can update—and the people who can trust them. Plan integrations, access control, and an audit trail early so your app can move from “interesting” to “operational.”
Start with the core objects that drive inventory decisions:
Be explicit about which system is the source of truth for each field. For example, SKU status and UOM from ERP, but forecast overrides from your app.
Most teams need a path that works now and a path that scales later:
Whichever route you choose, store import logs (row counts, errors, timestamps) so users can diagnose missing data without engineering help.
Define permissions around how your business operates—typically by location and/or department. Common roles include Viewer, Planner, Approver, and Admin. Make sure sensitive actions (editing parameters, approving POs) require the right role.
Record who changed what, when, and why: forecast overrides, reorder point edits, lead time adjustments, and approval decisions. Keep diffs, comments, and links to affected recommendations.
If you publish forecast KPIs, link definitions in-app (or reference /blog/forecast-accuracy-metrics). For rollout planning, a simple tiered access model can align with /pricing.
A forecasting app is only useful if you can prove it performs well—and if you can spot when it stops performing well. Testing here isn’t just “does the code run,” but “do the forecasts and recommendations improve outcomes?”
Start with a small set of metrics that everyone can understand:
Report these by SKU, category, location, and forecast horizon (next week vs. next month can behave very differently).
Backtesting should mirror how the app will run in production:
Add alerts when accuracy suddenly drops, or when inputs look wrong (missing sales, duplicated orders, unusual spikes). A small monitoring panel in your /admin area can prevent weeks of bad purchasing decisions.
Before full rollout, run a pilot with a small group of planners/buyers. Track whether recommendations were accepted or rejected, plus the reason. That feedback becomes training data for rule tweaks, exceptions, and better defaults.
Forecasting apps often touch the most sensitive parts of a business: sales history, supplier pricing, inventory positions, and upcoming purchase plans. Treat security and operations as product features—because one leaked export or a broken nightly job can undo months of trust.
Protect sensitive business data with least-privilege access. Start with roles like Viewer, Planner, Approver, and Admin, then gate actions (not just pages): viewing costs, editing parameters, approving purchase recommendations, and exporting data.
If you integrate with an identity provider (SSO), map groups to roles so offboarding is automatic.
Encrypt data in transit and at rest where possible. Use HTTPS everywhere, rotate API keys, and store secrets in a managed vault rather than environment files on servers. For databases, enable at-rest encryption and restrict network access to only your app and job runners.
Log access and critical actions (exports, edits, approvals). Keep structured logs for:
This isn’t bureaucracy—it’s how you debug surprises in an inventory planning dashboard.
Define retention rules for uploads and historical runs. Many teams keep raw uploads briefly (e.g., 30–90 days) and keep aggregated results longer for trend analysis.
Prepare an incident response and backup plan: who is on call, how to revoke access, and how to restore the database. Test restores on a schedule, and document recovery time objectives for the API, jobs, and storage so your demand planning software remains dependable under stress.
Start by defining the decisions it must improve: how much to order, when to order, and where to order for (SKU, location, channel). Then choose a practical planning horizon (e.g., 4–12 weeks) and a single time grain (daily or weekly) that matches how the business buys and replenishes.
A solid MVP usually includes:
Keep everything else (promotions, scenario planning, multi-echelon optimization) for later phases.
At minimum, you need:
Create a data dictionary and enforce consistency in:
In the pipeline, add automated checks for missing keys, negative stock, duplicates, and outliers—and quarantine bad partitions instead of publishing them.
Treat inventory as a set of events and snapshots:
This makes “what happened” auditable and keeps “what is true now” consistent. It also makes it easier to explain stockouts and reconcile disagreements between ERP, WMS, and POS/eCommerce sources.
Start with simple, explainable baselines and keep them forever:
Use backtests to prove any advanced model beats those baselines. Add more complex methods only when you can measure improvement (and when you have enough clean history and drivers).
Don’t feed stockout zeros directly into the training target. Common approaches:
The key is to avoid teaching the model that demand disappeared when the real issue was availability.
Use explicit cold-start rules, such as:
Make these rules visible in the UI so planners know when a forecast is proxy-based vs data-driven.
Convert forecasts into three actionable outputs:
Then apply real-world constraints like MOQ and case packs (rounding), budget caps (prioritization), and capacity limits (space/pallets). Always show the “why” behind each recommendation.
Separate the UI from the forecasting engine:
Never run a forecast inside a UI request—use a queue or scheduler, return a run ID, and show progress/status in the app. Store bulk outputs (forecasts, diagnostics) in analytics-friendly storage referenced by run ID.
If any of these are unreliable, make the gap visible (defaults, flags, exclusions) rather than silently guessing.