Supply Chain Intelligence

Predictive Procurement & Raw-Material Planning

Supply-chain decision support combining predictive demand, MRP/BOM requirements, inventory constraints, vendor intelligence and logistics economics to recommend purchasing actions.

Case study
  • Microsoft
  • SQL
  • Python

The business challenge

Raw-material purchasing had to account for much more than current inventory. Production requirements, sales forecasts, BOM/MRP demand, supplier performance, transportation, pricing, international sourcing variables and warehouse constraints all affected whether material would actually be available when production needed it.

The solution

Designed a predictive procurement tool in a Microsoft, SQL and Python environment. The solution evaluated future material requirements and sourcing alternatives, then produced purchase recommendations and advance alerts so procurement and operations could act before a projected shortage affected production.

Implementation

How the system was built, step by step.

1

Demand forecast

Forecast future production and material demand from sales forecasts, sales accuracy and the production plan with its capacity limits.

2

BOM/MRP translation

Translated production requirements into raw-material needs: required quantities and timing.

3

Inventory constraints

Compared projected needs against inventory, minimum/maximum levels, storage limits and expected scrap.

4

Vendor & sourcing intelligence

Evaluated available vendors and international sourcing options using SLA, capacity, location, pricing, tariffs, transportation distance and currency conversion.

5

Predictive engine

Identified when projected supply would no longer cover production requirements and recommended the required purchasing action, triggering alerts up to six months in advance.

Business results

What changed for the business once the system was live.

Planning visibility

Projected material shortages could be identified up to six months ahead.

Procurement productivity

Reduced manual analysis by consolidating multiple operational and sourcing variables into one decision process.

Production continuity

Purchasing recommendations considered production requirements, inventory coverage, scrap and warehouse constraints.

Smarter sourcing

Purchasing alternatives could be evaluated across vendors and locations using cost, logistics, SLA and availability factors.

Actionable alerts

The system shifted procurement from reacting to shortages toward planning before the risk materialized.

Case positioning

Not simply a reporting solution: it connected operational data, predictive planning, supply-chain rules and procurement economics to support a business decision: what material should be purchased, from which available option, in what quantity, and early enough to protect production.