Opportunities only become options when they fall within your control. – The Mind of a Fox

Introduction to monitoring metrics

Are you monitoring a process using metrics? But cannot think of the metrics that are important to your business to report on? Can’t find the right charts to display or develop actionable tasks?

Below I list the measures that Michel Greif outlines in his book ‘The Visual Factory’, which have helped me to develop several spreadsheets that managers use in my company to report back and drive change with. So for your benefit I list them here, but for more information then search for the book on Google.

Relevant and distant measures

Measures should be time appropriate

• Category 1 – measures can be influenced in the short term and a short-term expectation is appropriate.
  • Examples are scrap, flow distance, WIP turns, changeover time, unsafe acts, capability index, skills mastered.
• Category 2 – take longer to respond.
  • Examples are throughput, raw materials, and finished goods turns, delivery performance, labour productivity, OEE and employee satisfactory.
• Category 3 – measures require longer-term interpretation, not short term rewards or punishment.
  • Examples are market share, customer retention, share price, new products launched.

These are the responsibilities of senior management.

Below are the suggested metrics to measure.

• Flow Metrics
  • Avg production lead time and variance
  • Productivity
  • Fulfilment of commitments (deadlines, quantities)
  • Volume of semi – finished items
  • Flow profile: continuity, regularity, throughput time

Example:

Think of your production line like a river. Average production lead time tells you how long it takes a single order to flow from start to finish. A furniture manufacturer might discover their lead time is 12 days with a variance of ±4 days—that inconsistency signals bottlenecks worth investigating. Productivity measures output against input. If your team assembles 200 widgets per shift using 10 workers, that’s 20 widgets per person—but the real insight comes from tracking this over time. Semi-finished items are like cars stuck in traffic; too many indicate congestion in your process.

• Materials and Inventories metrics
  • Monitoring of unavailable items in warehouses (materials or finished products)
  • Quantity of material needed to build one unit of good product
  • Inventory volume and turnover
  • Warehouse management performance (response time, accuracy of inventories)

Example:

A bakery tracking unavailable items might notice flour stockouts occur every third week, revealing an ordering pattern problem. Material efficiency ratio shows waste—if you need 1.15 meters of fabric to produce a 1-meter product, that 15% excess represents pure loss.

Inventory turnover reveals cash tied up in storage. A tech retailer turning inventory 8 times yearly moves product every 45 days, while a competitor at 4 turns waits 90 days—that’s double the cash locked up gathering dust.

• Technical resources metrics
  • Availability of machinery
  • Yield level (output quantity divided by input quantity)
  • Breakdown rates, or production time without problems
  • Time needed for changing production runs
  • Maintenance costs in relation to production units
  • Percentages of preventive/ remedial maintenance
  • Number and duration of technical assistance calls
  • Avg length of repair periods

Example:

Machinery availability matters because downtime is expensive. A packaging line available 85% of the time loses 1.2 hours per 8-hour shift—multiply that by your hourly production value to see the real cost.

Yield level exposes hidden waste. A chip manufacturer with 92% yield scraps 8 of every 100 chips produced, potentially millions in losses. Changeover time between production runs can make or break profitability—Toyota famously reduced die changeover from hours to minutes, transforming their competitive position.

• Quality metrics
  • Percentages of unacceptable items
  • Rejection and retouching rates
  • Results of quality audits
  • Total cost of not meeting quality standards
  • Period of operation without major problems

Example:

Rejection rates tell a story. If 3% of your production gets rejected, and you produce 10,000 units monthly, that’s 300 units of wasted labor, materials, and energy. One automotive supplier calculated their total cost of poor quality at 15% of revenue—fixing that became their top priority.

Days without major defects (often tracked on factory floor displays) creates positive momentum. “47 days problem-free” gives teams something to protect and beat.

• Clients and Suppliers metrics
  • Sales volume
  • Delivery time
  • Customer satisfaction indicators: quality, service, number of problems

Example:

Delivery time isn’t just logistics—it’s competitive advantage. If your lead time is 6 weeks while competitors offer 4, you’re losing deals. Customer satisfaction indicators need specificity: instead of generic “satisfaction scores,” track concrete problems like “returns per 1,000 units” or “on-time delivery percentage.”

• Employees metrics
  • Labour supply
  • Number of suggestions (proposed, implemented)
  • Hours of training
  • Level of skill diversity within the teams
  • Absenteeism

Example:

Number of implemented suggestions measures engagement. A factory implementing 40 employee ideas yearly (out of 120 proposed) has active problem-solvers on the floor. Training hours quantify development—are you investing 20 hours annually per person or 2?

Skill diversity within teams builds resilience. If only one person can operate your CNC machine, you have a single point of failure. Absenteeism above 5-7% often signals morale problems worth investigating.

• Work environment metrics
  • Housekeeping indicator
  • Safety audits
  • Works accidents

Example:

Housekeeping indicators (often 5S audits scored 0-100) directly correlate with quality and safety. A score below 70 typically predicts higher accident rates. Work accidents should trend toward zero—track both frequency and severity to identify root causes.

• Overhead metrics
  • Monitoring of team’s costs
  • Power, oil, small tools etc..

Example:

Team cost monitoring reveals efficiency. If your support staff costs 25% of direct labor but industry standard is 15%, you’ve found improvement opportunity. Tracking consumables (power, oil, tools) by production unit exposes waste—a 15% spike in energy per unit signals equipment degradation or process drift.

• Misc metrics
  • Number of products covered by quality assurance
  • Number of automatic devices installed on equipment
  • Number of quality circles
  • Number of machines monitored with SPC
  • Distribution of occupied space
  • Level of standardisation of components

Example:

Products covered by quality assurance, automated devices installed, and SPC-monitored machines all measure your operational sophistication. A plant moving from 30% to 70% SPC coverage has fundamentally changed how they manage variation.

Component standardization reduces complexity. Using 500 unique parts versus 300 impacts everything from inventory to training to supplier relationships.

The key isn’t tracking everything—it’s tracking what matters for your specific improvement goals, then acting on what the numbers reveal.

Resources - Internal Engineering Projects

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What are your thoughts? Have I covered everything or is there more you know and would like to share?

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