Havish M Consulting
Customer Case Study · Procurement
Wires and Cables × Havish M Consulting

Engineered BOMs for Wires & Cables

A calibrated BOM engine cut new-SKU BOM time from 1.5 days to 30 minutes and took the guesswork out of copper and compound buys.

ClientWires and Cables FunctionProcurement ThemeBOM Creation
BOM turnaround, new SKU
0min
▼ 97% vs 1.5 days
BOM accuracy vs actual consumption
0%
▲ +5.5 pts vs 93.1%
Conductor buffer built into BOM
0%
▼ from 3.5%
Material buy avoided
0%
▲ vs manual-BOM baseline

Figures indicative; validated metrics available on request.

The Challenge

Buying metal and compound off spreadsheet BOMs

With 33,000+ SKUs across house wires, power, control and specialty cables, the BOM was the procurement plan. It was built by hand, one SKU at a time.

A spreadsheet per SKU

Engineers recalculated kg/km by hand for every new SKU or tender. A single BOM took one to two days.

Buffers on buffers

Upper-tolerance thickness plus a flat safety margin at each step. Copper and PVC were over-bought and sat as inventory.

One cable, many BOMs

The same cable code carried different BOMs across plants and files. No single version matched the approved construction.

MRP on stale numbers

Material plans used BOMs that missed spec changes and real scrap rates. The result was compound shortages and spot buys.

HMC Approach

From hand calculation to a governed BOM engine

Engineering owns the physics. Procurement owns the buffers. The engine makes both visible.

Select a step to see what happened.

Discover · Compare the BOM to the shop floor

We sampled 200 high-volume SKUs and checked each BOM against actual consumption.

  • Reconciled BOM kg/km with production issue slips and drum weights.
  • Traced where the margin came from: upper-tolerance thickness, rounded diameters, flat buffers.
  • Mapped the BOM journey from engineering to the plant and on to MRP and the purchase order.

Deliverable: Variance baseline by product family and material.

Design · One calculation logic, one parameter master

Standard construction logic for each family, aligned to the relevant IS specifications.

  • Parameter master: material densities, lay-up factors, nominal thicknesses and armour coverage.
  • Separate policy table for process allowances by plant and material, owned by procurement.
  • BOM versioning rules and an engineering–procurement sign-off workflow.

Deliverable: Calculation rulebook and parameter master.

Build · Engine plus familiar front-end

Python calculates the BOM. Engineers keep working in Excel.

  • Python BOM engine computing kg/km and ₹/km for every layer of the construction.
  • Excel/VBA front-end: pick the construction, get the BOM and an ERP-ready upload sheet.
  • Claude extracts construction parameters from customer tender specs and datasheets into the template.
  • Power Query pulls consumption data; a Power BI dashboard tracks BOM vs actual by SKU.

Deliverable: BOM engine, Excel front-end, variance dashboard.

Adopt · Pilot on LV power cables

The highest-value family went first, with engineering and procurement reviewing together.

  • Parallel run: manual and engineered BOMs side by side for six weeks.
  • A weekly variance review closed gaps in the parameter master.
  • MRP switched to engineered BOMs once variance held below 1.5%.

Deliverable: Pilot sign-off and variance thresholds.

Scale · Every family, every plant

Rolled out family by family, with calibration built into the monthly cycle.

  • House wires, control, flexible, instrumentation and solar cables onboarded.
  • Monthly recalibration of process allowances from actual consumption.
  • A named owner for the parameter master; changes are logged and approved.

Deliverable: Runbook, trained owners, governance cadence.

The Solution

Every layer of the cable, priced per kilometre

Pick a construction, then toggle between the manual and engineered BOM. Hover a layer or bar to see how it is calculated.

Cross-section (not to scale between constructions)
Material per km
–
Material cost per km
–
Conductor buffer
–
How the engineered BOM works
  1. Layer geometry from nominal thickness and lay-up factors.
  2. Mass from volume × density, layer by layer.
  3. Process allowance from actual scrap, not a flat margin.

Material prices and allowances indicative. Construction parameters simplified for illustration.

Impact Dashboard

Leaner buys, faster BOMs

Most of the saving came from removing stacked margins. Turnaround dropped from days to minutes in every family.

Where the material saving came from

Per ₹100 of material bought on manual BOMs · axis starts at 90

TotalReductionIncrease

BOM turnaround by product family

Hours per new SKU BOM, before → after (log scale)

ManualEngineered

Figures indicative; validated metrics available on request.

Before vs After

What changed for procurement

DimensionBeforeAfter
BOM creationHand-built in Excel per SKU, 1–2 daysGenerated from construction in ~30 minutes
Calculation basisUpper-tolerance thickness, rounded diametersNominal geometry from a governed parameter master
BuffersFlat margins stacked at each stepProcess allowance by plant and material, from actual scrap
Version controlMultiple files per cable codeOne versioned BOM with engineering–procurement sign-off
Tender specsRead and re-keyed manuallyClaude extracts parameters into the template for review
Link to MRPStale BOMs drove plans and spot buysERP-ready upload; monthly recalibration against consumption
Tools & Tech Used

Engineering logic, everyday tools

Claude tender spec extraction Python BOM engine Excel / VBA engineer front-end Power Query spec & consumption data Power BI BOM vs actual dashboard ERP BOM upload template
Key Takeaways

What transfers to any engineered-product buyer

01

Separate physics from policy

Geometry and density are fixed. Buffers are a business decision. Keep them in separate tables so each can be challenged.

02

Calibrate against consumption

Use actual issue data to set allowances. Opinions about scrap are usually higher than scrap.

03

Give the master data an owner

One accountable owner for the parameter master kept 33,000 SKUs consistent long after go-live.