Digital Costing – Mechanical and electronical equipment

Description

Context

  • Equipment costing relies on supplier’s raw data which are difficult to gather and extract industrially

– 20 large suppliers ; 30 major systems (flight controls, hydraulic power, fuel, engines control..)

– About 100 mechanical and electronical equipment in “build to spec” mode

– Hundreds of electronic cards references and thousands of components (diodes, capacitors …)

  • Business in constant evolution with product innovation (electrification, automation of crew operations, …), new data-based services and key actors’ strategy change (Make or Buy, acquisitions …)
  • Need to spend less time on data extraction and more time to bring added value in price negotiation

– Key points of the approach

  • Data structuring: a stand-alone suite was build and delivered to the customer with a user-friendly interface allowing assisted data exploration (automated recognition of data structures, invites for user’s action/input when necessary, learning/capitalization of new recognized formats)
  • Technical cost drivers extraction: a digital solution was developed in python to feed cost models

– Automated generation of Bill of Materials and cost drivers based on

  • electronic board pictures recognition (deep learning classifier, regex algorithm ..)
  • semi-automated extraction from engineering pdf documents (vision algorithms)

– Web Scrapping solution to collect prices of standard electronic components from internet

Cost model digitalization: ~40 cost models for majority of AC equipment

– Functions (I/O, CPU, Power …) based on electronic components (diodes, capacitors …)

– Printed Circuit Boards, Electronic boards assembly and tests, Computers assembly and tests

– Mechanical organs (actuators, valves,…) ; energy converters (motors, pumps..) ; displays, sensors

Results

– From days with thousands of tasks, to a few hours costing

  • Eg1 : Costing of a full Landing gear with analysis of scenarios (footprint, production rates), done in 6 weeks instead of 6 month
  • Eg2 : Costing of 4 MCU computers based on functional inputs (performance, …) allowing to challenge make or buy policy

Description

Context

  • Equipment costing relies on supplier’s raw data which are difficult to gather and extract industrially

– 20 large suppliers ; 30 major systems (flight controls, hydraulic power, fuel, engines control..)

– About 100 mechanical and electronical equipment in “build to spec” mode

– Hundreds of electronic cards references and thousands of components (diodes, capacitors …)

  • Business in constant evolution with product innovation (electrification, automation of crew operations, …), new data-based services and key actors’ strategy change (Make or Buy, acquisitions …)
  • Need to spend less time on data extraction and more time to bring added value in price negotiation

– Key points of the approach

  • Data structuring: a stand-alone suite was build and delivered to the customer with a user-friendly interface allowing assisted data exploration (automated recognition of data structures, invites for user’s action/input when necessary, learning/capitalization of new recognized formats)
  • Technical cost drivers extraction: a digital solution was developed in python to feed cost models

– Automated generation of Bill of Materials and cost drivers based on

  • electronic board pictures recognition (deep learning classifier, regex algorithm ..)
  • semi-automated extraction from engineering pdf documents (vision algorithms)

– Web Scrapping solution to collect prices of standard electronic components from internet

Cost model digitalization: ~40 cost models for majority of AC equipment

– Functions (I/O, CPU, Power …) based on electronic components (diodes, capacitors …)

– Printed Circuit Boards, Electronic boards assembly and tests, Computers assembly and tests

– Mechanical organs (actuators, valves,…) ; energy converters (motors, pumps..) ; displays, sensors

Results

– From days with thousands of tasks, to a few hours costing

  • Eg1 : Costing of a full Landing gear with analysis of scenarios (footprint, production rates), done in 6 weeks instead of 6 month
  • Eg2 : Costing of 4 MCU computers based on functional inputs (performance, …) allowing to challenge make or buy policy