Alcatel-Lucent priced repair contracts with stochastic spare-parts math

Alcatel-Lucent's post-sales repair and exchange services business needed to quote and evaluate customer support contracts. Those contracts depended heavily on how many spare parts the company would need to hold across a two-echelon service network.

The old spreadsheet approach could not handle the interaction between multiple items, multiple locations, stochastic demand and service constraints. That made tendering slow and made inventory cost too easy to underestimate.

Researchers built a tactical inventory planning tool using stochastic modeling and optimization algorithms. Its first job was practical: help the repair and exchange services group size spare-parts investment fast enough to support contract offers.

The nice move was treating bid preparation as an operations-research problem. The tool did not merely optimize an existing warehouse; it changed what the company knew before it agreed to a service promise.

Quote service contracts from inventory risk, not averages

The model surfaced inventory cost while a contract was still negotiable.

A generic tool let the repair group reuse the same logic across many tenders and analyses.

Stochastic modeling fit low-volume spare-parts uncertainty better than average-demand spreadsheets.

When a support contract hides inventory risk, build the risk into the quote. A fast generic model can prevent sales teams from promising service levels that operations cannot fund.

The published case reports that the tool supported Alcatel-Lucent's RES business tendering process and became useful for other business analyses because of its generic design. The work appeared in Interfaces in 2015.