Demand and product mix
Start from the customer: which products, in what volumes, and how the mix changes. The line is designed for the mix, not for one product.
The method
What is Mixed Model Manufacturing?
Mixed Model Manufacturing builds many different products on one line, in a level sequence, at the pace of customer demand. Instead of dedicated lines and big batches, the line is designed from the start for the mix, so variety stops being the enemy of flow.
Why it matters
Customers want variety. Batches make it expensive.
Most plants make many products. Building them in large batches means long lead times, piles of inventory, and a schedule that is out of date the day it is printed. Adding a dedicated line for every product family costs floor space, capital and people.
Design first
Design flow from the start. Don't kaizen it later.
Most lean programs remove waste after it has already been built into a line. Mixed Model Manufacturing designs the flow up front: the pace, the balance, the buffers and the material system are calculated before the line is built.
At a glance
Traditional batch production vs. Mixed Model
| Traditional batch production | Mixed Model Manufacturing | |
|---|---|---|
| Product variety | Separate lines or long runs for each product | Many models built on one line, every day |
| Scheduling | Large batches pushed to a forecast | A level, repeating sequence at the pace of demand |
| Pace | Each machine runs as fast as it can | Every station paced to takt time |
| Imbalance between models | Absorbed by overtime, expediting and piles of WIP | Designed in: balanced work plus small, sized in-process kanbans |
| Material | Big stockrooms, forklifts, parts searched for | A plan for every part, delivered to the point of use on a route |
| Improvement | Kaizen removes waste after it is built in | Flow designed from the start, then improved |
Core ideas
The building blocks of a mixed-model line
Each idea is simple on its own. The power is in designing them together. Where we have a free demonstrator, you can try it in your browser.
Takt time
The pace of customer demand: available work time divided by the units required. If a shift has 450 minutes and the customer needs 90 units, takt is 5 minutes. Every station is designed to that beat.
Try Time Plus Inventory ↗Work content and standard work
Each model's process is broken into work elements and timed. Different models carry different work content, and that difference is what the line design has to manage.
Line balancing
Work is spread across stations so each one fits takt across the whole mix, not just for one model. Where a model runs longer at a station, the design accounts for it rather than hoping it averages out.
Try the Sequencing & IPK Lab ↗In-process kanban (IPK)
Small, deliberately sized buffers between stations absorb the variation between models, so one long cycle does not block or starve the whole line.
Try the IPK Flow demonstrator ↗Leveled sequencing
Instead of all of one model, then all of the next, models are mixed in a repeating sequence. Workload and material demand stay smooth, and every model is available every day.
Material flow and PFEP
A plan for every part: how it is packaged, where it is stored, how it is replenished by kanban, and how it reaches the point of use on a regular delivery route.
Try the Containerization Tool ↗Pull, not push
The line builds what the customer has taken, and material is replaced as it is used. Inventory stops being a guess and becomes a designed quantity.
Try Push · Pull · Flow ↗How it is done
From assessment to a sustained line
- Step 01AssessUnderstand demand, mix, current flow and where the waste is.
- Step 02DesignCalculate takt, balance the work, size the IPKs, design the layout and the material flow.
- Step 03ImplementBuild the line, train the people, and launch with standard work.
- Step 04SustainManage to the design with visual controls and problem solving, and keep improving.
Where Ezbelta fits
Software built on the method
Ezbelta tools encode Mixed Model Manufacturing as Leonardo Group Americas teaches and implements it. AI does the preparation; your team makes the decisions.
Go deeper