Medical Equipment Supply Chain Planning

Medical equipment is built in low volumes against lumpy capital orders, from long-lead parts, and supported for years after the sale. DemandLabs plans each build against component lead times and sets service parts against their own service targets.

What Shapes Medical Equipment Planning

Medical equipment is sold as capital, built to order and kept running for years in the hospital.

Capital orders

Orders Follow Hospital Budgets

Systems are bought from capital budgets and tenders that close on the hospital's calendar, so orders arrive in lumps and a delayed approval moves the shipment plan.

Options

Many Options on Each System

A system ships with a chosen set of options and accessories, so a forecast at system level has to become a plan for every module and long-lead part.

Installed base

Service Parts Outlive the Model

Hospitals keep equipment in use for years after a model leaves the price list, and a system waiting on a part is out of service.

Solutions for Medical Equipment

The DemandLabs applications that fit medical equipment, and how each one handles the planning.

Assembly capacity, crews and the lead times of long-lead parts solve together, and a late system order is traced to the machine, part or crew holding it back.

Explore supply planning

Service parts are their own segment with their own service targets, and DemandLabs shows the stock each target needs across depots before you commit.

Explore inventory optimization

Expected tender and budget-cycle orders are entered in one consensus forecast that sales and supply teams both edit, and forecast value add shows whether each seller's changes beat the model.

Explore demand planning

Unit plans for each system tie out to the finance plan period by period, and scenario rooms compare deal timing and build plans side by side on P&L, cash and service.

Explore IBP

How Medical Equipment Planning Connects

Each system order links to the long-lead parts it needs and to the installed base it will join.

Demand

System Forecast

  • Capital budgets
  • Tenders
  • Large deals
Supply

Assembly Plan

  • Assembly capacity
  • Test capacity
  • Crews
Materials

Long-Lead Parts

  • Electronics
  • Options and accessories
  • Supplier lead times
Service

Service Parts

  • Installed base
  • Depot stock
  • Service targets
Finance

Order Margin

  • Margin by system
  • Expedite cost
  • Parts inventory value

Built for Medical Equipment

Equipment planning follows each system from the order to its years of service in the field.

Late Systems Traced to a Part

Each late system shows the part, machine or crew behind the delay, with the expedite options priced against that order.

Service Parts on Their Own Targets

Parts for the installed base carry service targets apart from production stock, including parts for models no longer built.

Intermittent Demand for Service Parts

Service parts with sparse, lumpy demand are forecast with Croston, SBA or TSB, the methods built for parts that sell rarely.

Single Sources Behind Each System

The sub-tier sources behind your board and display suppliers are mapped from your BOM and supplier records, and single sources are flagged against the systems they hold up.

Medical Equipment Planning FAQs

Can DemandLabs plan service parts for models no longer sold?

Service parts are a segment of their own with their own service targets, and slow movers use intermittent-demand methods, so older models keep their parts planned.

How do large deals enter the forecast?

Sales teams edit the consensus forecast directly, every edit is stamped with its owner, and forecast value add shows over time whether those edits beat the model.

Do approved plans go back to our ERP?

Approved build and purchase plans write back to your ERP through standard connectors for SAP, Oracle EBS and NetSuite.

How long does implementation take?

Eight weeks to the first live cycle for one planning area, with a value review with your CFO in week 10.

See Your Installed Base and Build Plan Together

A 45-minute walkthrough on your own data.