Tag: process-optimization

In complex operations, the instinct to “work faster” often makes performance worse. Teams optimize local activity—more tasks, more movement, more overtime—while overall throughput stays flat. The reason is simple: throughput is governed by constraints.

If you improve everything except the constraint, the system doesn’t improve.

Local efficiency is not the same as flow

A department can be very busy and still not improve throughput. High utilization can actually increase queue time and delay. Flow improves when work moves smoothly through the constraint with minimal waiting, rework, and variation.

Find the constraint (not the loudest problem)

Constraints show up as:

• Persistent queues upstream
• Downstream starvation (waiting for input)
• Higher lead time and variability around one step
• Frequent expedites around the same area

But constraints can be hidden by firefighting. Use a simple approach:

1. Trace a unit of work through the process (or a job through maintenance)
2. Record waiting points and reasons
3. Identify the step that consistently limits completion

The constraint is where work becomes “stuck,” not where people complain the most.

Measure what matters: queue time and variability

Most “bottleneck” discussions focus on cycle time. In reality, queue time dominates. Two levers matter:

• WIP (work-in-progress): too much work released creates congestion
• Variability: unstable inputs and frequent changes disrupt flow

Even small variability at the constraint can ripple through the system.

Five practical levers to improve constraint performance

You don’t need a full transformation. Start with these levers:

1) Protect the constraint
Ensure the constraint is not interrupted by avoidable issues: missing materials, unclear priorities, unplanned meetings, or low-value tasks. Protecting time is often the fastest win.

2) Subordinate upstream to the constraint
Stop releasing more work than the constraint can handle. This feels counterintuitive, but it reduces congestion and improves lead time.

3) Simplify changeovers and handoffs
If the constraint suffers frequent changeovers, clarify sequencing rules, reduce unnecessary switches, and standardize preparation.

4) Stabilize inputs
The constraint cannot perform with unstable inputs. Improve readiness checks upstream so the constraint receives “ready” work, not partial work.

5) Elevate only when necessary
Before adding people or equipment, remove waste and stabilize. Elevation is costly; control and simplification often deliver more.

Sustain improvements with a simple control routine

Constraint improvements will decay unless you manage them. Add:

• A daily constraint review (what blocked it yesterday?)
• A trigger list (top recurring blockers)
• Action tracking with owners

Where INJARO helps

INJARO helps teams diagnose constraints, redesign planning and release rules, and define control routines. We make the process automation-ready by defining priority logic and information requirements clearly—so digital workflow tools can be implemented later by internal IT or an implementation partner.

Improving flow is not about pushing harder everywhere. It’s about controlling the constraint and reducing the friction that keeps work from moving.

Process mapping is often used as a workshop exercise: gather people, draw boxes, produce a diagram, and declare progress. The diagram looks professional—but operational performance doesn’t change.

A useful process map does not exist to document. It exists to find loss and redesign control.

Why most maps fail

Common failure patterns:

• The scope is too broad (“end-to-end”) and becomes abstract
• Steps are described at the wrong level (either too high or too detailed)
• No one owns the handoffs
• The map is disconnected from actual performance data
• The map doesn’t lead to changes in routines, controls, or standards

If a map doesn’t change decisions or execution, it becomes wallpaper.

Map with a purpose and a loss hypothesis

Before you map anything, define:

• Boundary: where the process starts and ends (be strict)
• Purpose: what outcome the process must deliver (quality, time, cost, safety)
• Loss hypothesis: where you believe loss occurs (delay, rework, waiting, variation)

Example: “Shipment release process from final inspection to dispatch. Hypothesis: delays and rework occur at document checks and permit handoffs.”

This keeps mapping focused and actionable.

Add friction markers, not just boxes

A map should make friction visible. Add markers for:

• Queue points: where work waits for capacity or approval
• Rework loops: where outputs are rejected and sent back
• Decision gates: where criteria are unclear or subjective
• Information gaps: where teams create “shadow tracking”
• Handoffs: where ownership changes (risk of misalignment)

These are the places where time and quality are usually lost.

Validate with data (lightweight is fine)

You don’t need perfect data to start, but you need some evidence. Ask:

• Typical and worst-case lead time?
• Where does work wait the longest?
• Most common reasons for rework?
• Frequency of exceptions?

Use simple sampling if needed: 10 cases over 2 weeks can reveal patterns.

Convert findings into control

Optimization is not just “remove steps.” Often the biggest wins come from improving control:

• Define entry criteria for each stage (what “ready” means)
• Clarify decision rules (what qualifies/doesn’t qualify)
• Reduce approvals by aligning risk levels to approval levels
• Standardize handover information (minimum required fields)
• Install triggers (when lead time exceeds threshold, escalate)

Turn the map into an operating mechanism

A process map becomes useful when it is tied to:

• A standard work definition (who does what, when)
• A KPI or lead time measure with triggers
• A routine where performance is reviewed and actions are taken
• Clear ownership of handoffs

That’s how mapping becomes operational improvement—not documentation.

Where INJARO helps

INJARO designs process optimization efforts that connect mapping to governance, decision logic, and performance control. We produce automation-ready process definitions—clear enough to support later system implementation by internal IT or a partner—but focused first on making the process run better today.

A good map is not a picture. It’s a tool to find loss, redesign control, and make execution more reliable.