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  • Michael Asbury

Bottom Line Impact with Rapid Improvements

Achieve rapid system level improvement with Constraint Utilization & Applied TLS (Combined Lean Six Sigma & Theory of Constraints)

Rapid System Level Improvement

To make a rapid impact on a system, its important to understand that the performance of any system is limited by its constraints (Theory of Constraints). At any given time, one constraint is dominant over the others. This is the best place to remove waste & make value flow (Lean) and apply a structured problem-solving approach such as DMAIC (Define-Measure-Analyze-Improve-Control from Six Sigma).

Measuring Constraint Utilization

On my first TLS project (Combined Lean Six Sigma & Theory of Constraints), we found that the heat treatment (HT) process was our constraint. To help prioritize what to improve, I adapted the OEE metric (Overall Equipment Effectiveness -- credited to Seiichi Nakajima in the 1960's) from TPM (Total Productive Maintenance) within the Lean toolbox to create Constraint Utilization (UT). Constraint UT is the amount of scheduled time the constraint is effectively adding value. The difference between UT and OEE is that performance is split into load performance and time performance, since it compounds, and has different root causes & solutions. We wanted this level of detail because we wanted HT running as much as possible, with as many cycles as possible, with the most tons per cycle possible, with the most value-added tons as possible. 100% capacity utilization would result in UT = 1.00.

UT = U x T x L x Y (Constraint Utilization)

  • U = Up-time -- Time equipment is running within specifications relative to time scheduled to run

  • T = Time Performance -- Time used running loads relative to Up-time

  • L = Load Performance -- Load relative to load capacity

  • Y = Yield -- Amount of Value Adding Units Processed relative to Load Performance

UT should only be used at a constraint as over activation of a non-constraint leads to overproduction and all other forms of waste. OFE (Operational Functional Effectiveness) would be more appropriate, which I will define in another post.

We calculated the baseline UT and found how much capacity we had relative to how much we were utilizing.

  • U = 0.85 Up-time

  • T = 0.85 Time Performance

  • L = 0.59 Load Performance

  • Y = 0.54 Yield

Baseline UT = U x T x L x Y = 0.85 x 0.85 x 0.59 x 0.54 = 0.23

From the baseline UT, we realized that we could rapidly ramp up HT by getting creative (versus spending capital). With this added insight we could easily calculate the impact of the wastes and solutions. Improvements at a constraint will flow through to the bottom line until the constraint is broken. This helped with prioritizing solutions based on estimated impact, ROI, and payback time. Fortunately, many improvements were free and inexpensive.


We created a project team and quickly analyzed the process interdependencies & sequence of dependent events as well as the wastes for each of the UT factors. Next, we held a kaizen event (rapid improvement project) and used PDCA (Plan-Do-Check-Act) and an Action Item log for some quick hits with each of the solutions in a test phase followed by full implementation to rapidly improve HT.

  1. HT Data Log -- More tons per load, less time between cycles, fewer cycles running too long, less rework

  2. Downtime Log -- Less downtime & quicker maintenance response time

  3. Operator Training (HT Compatible Alloys & Covering Breaks) à More value-add tons per load & cycles per day

  4. 6S Staging Area & Process Changes -- Less time between loads and more value-add tons per load

  5. Quick Changeovers & Mistake Proofing -- Less time between loads for more cycles per day

  6. Mix Policy Change (Limited Number of High Hour Heats Melted per Day & Week) -- More Cycles per Day

  7. HT Spec Changes -- Some Cycles Spec Too Long; More Cycles per Day

  8. TPM (Increased Quench Tank Agitation) -- Less Time between Cycles; More Cycles per Day

  9. Design for Manufacturability / Process Re-engineering (Redesigned Oven Racks & Pedestals) -- Less Downtime, More Value-Add Tons per Load, Less Material Usage for making them in-house. Used DMADOV: Define-Measure-Analyze-Design-Optimize-Validate (Six Sigma)

These improvements compounded to result in a 120% increase from 36.4 to 80.3 average value-add tons/day. We knew we would be breaking the constraint by this point, but we went ahead and calculated the impacts and sequence that we could continue to improve HT as we applied TLS to other constraints to control whether we want HT to become a constraint again or keep it improving it slightly ahead of our preferred choice of constraint (which was melting).


This allowed us to gain 20% more throughput (BIG $$$) through the plant, stop running HT on the weekends, reduce labor by 37% (even while giving the employees a $3/hour raise), and save 25% on HT utilities (~$50K/month). Our lead times also came down rapidly and the on-time delivery rose from 34% to 78% and the mountain of WIP waiting to go through HT vanished. The payback time for the $23K of capital invested in improvements was 1 day.

TLS is a very powerful methodology to make substantial gains in performance and results.

For More Info Contact:

O 888.489.5121

F      704.479.7206



North Carolina, United States

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