Technology for uninterrupted supply

Background

A mid-sized manufacturer of industrial pumps (“Firm B”) operated three plants producing 240+ SKUs across metals, castings and fabricated components. The business struggled with unpredictable material availability, despite stable demand.

The operations head blamed procurement. Procurement blamed suppliers. Suppliers blamed “urgent, last-minute orders.”

The real problem was forecasting — based on averages, not reality.

The Problem

Material shortages led to:

14 production interruptions in a year
Chronic WIP buildup due to missing components
Excess inventory for low-velocity parts
High cash locked in raw material buffers

Cycle time for some SKUs was 68% longer than planned — not because capacity was inadequate, but because production was continuously waiting on inputs.

Root-Cause Diagnosis

A 45-day diagnostic revealed three failure loops:

Failure Point	Issue
Demand Forecast	Built on monthly averages; no seasonality or order pattern modeling
Material Planning	MRP triggered after shortages rather than ahead of demand
Production Scheduling	No correlation between sales probability & material readiness

Shortages were not random — they were predictable, but the planning system wasn’t looking.

Strategic Intervention — Predictive Analytics for Material Readiness

Firm B implemented predictive modeling directly into MRP & production planning.

The new system blended:

Sales probability curves (by SKU and customer segment)
Seasonality + project-cycle patterns
Historical urgency rate by client
Supplier lead-time variability data
Consumption rate by machine cell

Instead of ordering when stock dropped low, the system predicted material exhaustion before it happened.

Execution — 4-Step Rollout

Data cleanup: 9 years of sales, 4 years of consumption, 18 months of lead-time variability normalized
Model build: SKU-level demand probability + risk-weighted reorder points
System integration: MRP + supplier portal + production scheduler sync
Performance governance: Daily readiness dashboard + weekly predictive review

Procurement and planning finally spoke the same language: probability and timing.

Results (10 Months After Deployment)

KPI	Before	After	Change
Production interruptions	14 / year	2 / year	–86%
WIP (₹)	₹51 crore	₹32 crore	–37%
Raw-material buffer	78 days	41 days	–48%
Order-to-delivery time	42 days	26 days	–38%
Forecast accuracy	63%	92%	+29pp
Supplier friction	High	Low	Clear scheduling 4 weeks ahead

The company didn’t increase capacity. It increased predictability.

What Changed Inside the Business Culture

Production stopped “waiting” — it scheduled with confidence
Procurement stopped firefighting — it negotiated proactively
Finance stopped complaining about inventory — it freed working capital
Suppliers stopped blaming planning — they received visibility

Reliability produced organizational calm — and commercial advantage.

Lessons & Takeaways

Forecasting accuracy is not about demand certainty — it is about pattern recognition
Predictive analytics is not a dashboard — it is a planning philosophy
Shortages are rarely surprises — they are blind spots
Predictability reduces both interruption cost and working-capital burden

Predictive planning didn’t replace people. It empowered them to make decisions before problems happen.

*We take our clients' confidentiality seriously. While we 've changed their names, the results are real.