TOC five focusing steps

TOC five focusing steps

Define the system goal and identify what currently limits it. First examine how to use the existing constraint effectively, then align other work to support that decision. Add capacity or make a larger change when the evidence shows it is needed. After improvement, look again: the limiting factor may move, and old rules can become the next obstacle. The loop is about system performance, not making every resource equally busy. Exploiting a constraint means protecting useful capability, not overburdening people or bypassing maintenance and quality. Verify the result through the defined system outcome and repeat the learning cycle.

Follow the method

1. Identify
2. Exploit
3. Subordinate
4. Elevate
5. Repeat

Return to the current constraint after improvement

Fictional pump route: customer demand is 90 accepted units per shift. Comparable effective capacities are cutting 110, finishing 70 and final test 85. Manager Priya initially identifies finishing as the limiting stage. A controlled improvement later demonstrates finishing at 95, while final test remains 85.

Follow the method

1. Identify
2. Exploit
3. Subordinate
4. Re-identify after trial
5. Further finishing investment

Improving the old constraint can become inertia

Apply the five focusing steps to a stated system outcome, distinguish using existing capacity from adding capacity, and return to identification when the limiting condition moves.

Fictional pump route: customer demand is 90 accepted units per shift. Comparable effective capacities are cutting 110, finishing 70 and final test 85. Manager Priya initially identifies finishing as the limiting stage. A controlled improvement later demonstrates finishing at 95, while final test remains 85.

Role: System owner coordinating the affected process leaders.

Normal condition

Improvement increases reliable system delivery while maintaining quality and other necessary conditions.

The gap

The team plans another finishing investment even after final test has become the lower-capacity stage.

• Capacities are supplied fictional effective rates for the same mix and period.
• A capacity comparison is a working hypothesis; observed starvation, blocking, quality and policy effects still need review.

1. Identify the present limiting condition

   Priya defines the delivery boundary and compares 110, 70 and 85 with demand 90. She treats finishing at 70 as the initial constraint hypothesis and checks actual operating evidence.

   Why: The biggest department or queue is not automatically the system constraint. The comparison must use like-for-like effective capacity and real demand.

   Evidence: Initial model limit: finishing 70 accepted units per shift.

2. Decide how to exploit existing capacity

   The finishing team investigates lost time from missing kits and avoidable rework, and protects the approved work sequence and quality readiness using current resources.

   Why: Exploitation seeks better use of the existing limiting resource. It is not a demand to overburden people or skip controls.

   Evidence: Existing-resource action plan names loss evidence, owner and expected system effect.

3. Subordinate supporting decisions

   Upstream release, material readiness and downstream response are aligned to the finishing plan. Local output targets are reviewed so cutting does not flood the route with unwanted WIP.

   Why: Supporting stages must help the system’s limiting decision rather than maximize their own activity independently.

   Evidence: Release/support rules are explicit; idle nonconstraint capacity is not automatically a failure.

4. Elevate only if still justified

   If the verified limitation remains after appropriate exploitation and subordination, evaluate additional capability or capacity with cost, quality and demand evidence. Recheck the constraint after any improvement, including earlier steps.

   Why: Elevation is not mandatory if the limiting condition has already moved. The five steps are not a purchasing checklist.

   Evidence: Any additional-capacity proposal has a remaining system gap and evidence-based justification.

5. Repeat when the limiting condition changes

   After finishing demonstrates 95, compare again: cutting 110, finishing 95, test 85, demand 90. Final test is now the first internal constraint hypothesis; revise support rules and investigate its losses.

   Why: Continuing to optimize finishing through habit would not remove the current modeled 85-unit delivery limit.

   Evidence: Return to identification; modeled system gap is now 5 units against demand, not the old 20.

Demand drops below all capacities

Confirmed demand falls to 60 while all stages retain the original 110, 70 and 85 capacities.

Reconsider the system boundary and market/demand limitation rather than declaring finishing a binding internal constraint against this demand.

The slowest resource can have spare capacity relative to current demand.

Demand basis and constraint hypothesis are revised together.

Run a practice session

Materials

• Stage-capacity cards and a customer-demand card
• Before/after change card and policy log

1. Define the goal · 4 minutes

   What counts as accepted system output?

2. Choose the first actions · 8 minutes

   Which actions use existing capacity and which add it?

3. Reveal the improved stage · 8 minutes

   Why return to step one before buying more finishing capacity?

4. Apply the service variant · 5 minutes

   Which local rule could undermine the new focus?

Debrief

• Challenge a mandatory march through elevation.
• Ask whether demand or an external policy changes the boundary of the hypothesis.

Write the limiting comparison before and after, then connect each supporting rule to the current result.

Transfer into the work

Owner: System owner with current constraint and supporting-process leaders

Record: Constraint hypothesis, focusing decisions and system-output observations

Review: After each material improvement and at the ongoing system review

Evidence: Reliable accepted delivery, current constraint evidence and aligned support decisions

Reopen the boundary or hypothesis; do not preserve obsolete rules because the previous project was successful.