Manufacturing Psychology Pitfalls

1. Goal Confusion: Forgetting That Profit Is The Goal

The Pitfall: Organisations optimise intermediate metrics (efficiency, utilisation, cost reduction) whilst losing sight that the singular business goal is profit maximisation through throughput increase, expense reduction, and inventory minimisation — in that priority order.

Consequence: Optimising intermediate metrics drives counterproductive behaviour—building unsaleable inventory to "keep busy", reducing labour that starves constraints (cutting 15% labour whilst reducing 25% throughput increases unit cost), maximising non-constraint utilisation that creates WIP accumulation. Organisations achieve their stated metric targets whilst destroying profit.

2. Constraint Misidentification: Speed vs Throughput

The Pitfall: Conflating two distinct constraint types leads to investment misallocation and zero-ROI capital projects:

• Speed Bottleneck: Process step with lowest theoretical maximum speed (defines OEE denominator)
• Throughput Constraint: Process step with lowest actual throughput (speed × availability) — where improvements actually increase system output

Worked Example: Flour mill believed capacity-constrained at 280 tonnes/week (theoretical: 504 tonnes/week, 55% OEE). Management planned £1M+ investment in additional milling equipment (the speed bottleneck). Correct constraint identification revealed conditioning silos upstream created the throughput constraint through 8-hour cycle times. Operational sequencing optimization increased throughput to 430 tonnes/week (+54%) with zero capital — representing £2M+ annual profit impact.

3. Problem Visibility: The Rumsfeld Matrix

The Pitfall: Human attention is optimised for salience (intensity × novelty), not cumulative statistical significance. This creates systematic blindness to high-frequency, short-duration problems that often represent 30-40% of total losses.

Speed Loss Example: Production line experiences 50 micro-stops/hour × 3 seconds/stop = 167 hours/year lost. At 100 units/min × £2 margin = £2M annual value loss. Yet this remains completely invisible because individual 3-second events fall below perception threshold for "problem" — no alarm, no log entry, operators habituate to pattern.

Solution: Systematic measurement (continuous speed monitoring, frequency-duration matrices) converts unknown unknowns to known unknowns, enabling prioritisation by financial impact rather than subjective salience.

4. The Semi-Marginal Cost Fallacy

The Pitfall: "Operating faster increases maintenance/energy/consumables costs, therefore we shouldn't increase speed." This confuses cost per unit time with cost per unit output.

Mathematical Reality: Even if maintenance costs increase 25% per hour, unit costs decrease because fixed costs (labour, depreciation, overheads) are amortised over more units:

• Current: 80 units/hr, £50/hr maintenance, £200/hr fixed → £3.13/unit
• Maximum: 100 units/hr, £62.50/hr maintenance (+25%), £200/hr fixed → £2.63/unit
• Result: +25% hourly maintenance cost yields −16% unit cost

Systemic Consequence: Organisations forgo £1.6M/year throughput gains (20 units/hr × 8,000 hrs × £10 margin) to avoid £100k/year maintenance cost increase — a 16:1 value destruction ratio.

Origin: Maintenance managers measured on £/month departmental budgets (not £/unit), creating siloed decision-making without visibility into total cost structure or margin contribution.

5. Loss Aversion & Ego Defence

The Pitfall: Prospect theory demonstrates humans experience losses ~2× more intensely than equivalent gains. In manufacturing, quantifying large improvement opportunities (e.g., "25% labour productivity gain available") is interpreted as implicit criticism ("we've been performing 25% below potential"), triggering defensive reactions rather than collaborative problem-solving.

Manifestations:

• Historical justification: "We've run it this way for 10 years" becomes evidence of correctness
• Sunk cost attachment: £500k automation delivering zero throughput improvement (targeted non-constraint) is defended rather than acknowledged
• Metric softening: When measurements reveal underperformance, adjust measurement methodology rather than address root causes

Solution: Frame opportunities as external ("market conditions changed, previous approaches were optimal then") to depersonalise and create safe space. Benchmark against potential not history: "Process capable of X, currently achieving Y" focuses on system not people.

6. Confirmation Bias: "We Already Know The Problem"

The Pitfall: Teams become attached to familiar root causes (e.g., "it's the bearings", "it's material quality") and selectively attend to confirming evidence whilst discounting contradictory data. Simple root causes (incorrect speed settings, misaligned sensors) are dismissed in favour of complex explanations that justify specialist expertise.

Solution: Implement Split Solve with explicit bias prevention — exhaustive MECE splitting before investigation, measurement-driven classification (verified vs failed vs unknown), hypothesis testing discipline with stated falsification criteria.

7. Misaligned Metrics: Local vs Global Optimisation

The Pitfall: Measuring individual work centre utilisation creates local optimisation that harms global throughput. Maximising non-constraint equipment utilisation creates overproduction accumulating as WIP, constraint starvation when upstream optimises for batch efficiency, and artificial constraints when downstream cannot process volume.

Lean Orthodoxy Danger: Dogmatic WIP elimination removes strategic buffers that decouple process steps and prevent constraint starvation. Eliminating buffers in the name of "waste reduction" decreases constraint availability, directly reducing system throughput. Cost-benefit ratios of 10:1 to 50:1 favouring buffers are common, yet Lean implementations routinely eliminate them.

Solution: Constraint-based metrics — throughput at constraint (not average), constraint availability (not average utilisation), WIP at constraint (not total WIP). Implement asymmetric buffer management: maintain upstream buffers full (protect constraint from starvation), downstream buffers empty (prevent constraint blockage).

Implementation Principles