Resource Centers

Data Drives Lean

Data is needed to focus improvement efforts.

Objective data is needed to make sound judgments.
Data from time studies, equipment loading data, takt times, staffing requirements, process yields, and lot and kanban sizing information will lead to workflow configurations and process layouts that help optimize value streams.

Almost everyone thinks they know how much time it takes to perform routine process tasks but time studies usually prove that the assumptions are wrong.
There is no substitute for real time studies, a staple of the “old-time” industrial engineer, compete with a stopwatch and clipboard.

Equipment loading rates are a function of throughput rates, bottlenecks, and takt time.
The nameplate capacity, full loading potential, and capacity utilization rates together create the basis of reality checks on throughput rates.

The takt time is established by the customer’s average buying rate.
If the takt time is 10 minutes, that means that over the course of a day, week or month, customers are buying competed products at a rate of one every 10 minutes.

If any process equipment in the workflow fails to function when it is scheduled to do so, the entire workflow comes to a halt and customer demand will go unmet.
Data on reliability of the equipment can be used to adjust full loading potential and throughput rates until TPM efforts can improve the reliability.

Historically, scheduling practices have favored long runs and large lot sizes to reduce the impact of the time and cost of tedious process set-ups and long product change-overs.
If set-ups and change-overs are significantly reduced, the benefits of converting to small lot sizes are enormous.
Data on current lot sizes and the rationale used to set current lot sizes is helpful as a conversion to lean and pull manufacturing practices is planned.

Data on actual process yield rates is a necessary planning input for a lean conversion.
The reasons for yield losses is important information. Sources of yield losses are usually targets for improvement efforts although they may influence lean plans as well.
For example, if a process has a fixed quantity of inherent start-up waste that cannot be eliminated or reduced with current technology, the impact of small lot sizes on yield may overshadow the benefits of small lot sizes.

A conversion to lean can have a major impact on staffing methods as well as staffing levels. Staffing levels are commonly reported to be at least 20%.
Crew staffing instead of worker staffing is often more effective in lean operations.
Current staffing data provides both a baseline and reality check for confirming planned lean staffing requirements.

Good data collection forms organize information into useful formats, serve as communication tools, and help trigger solid improvement efforts.