As a shareholder CEO of a medium sized mechanical engineering company you know the problem: Your production department reports an OEE of 68%, controlling shows an EBIT margin of 12%, and yet you feel you are losing track of the actual levers of your business success.
The challenge does not lie in single KPIs, but in their strategic linkage. Modern mechanical engineering companies with 100-500 employees face the task of linking operational metrics such as Overall Equipment Effectiveness (OEE), throughput times and quality metrics with strategic goals such as market share, EBIT development and customer loyalty.
Concrete challenges of German SMEs in mechanical engineering:
• Data silos between the production floor and management – machine data remains in the MES, financial metrics in the ERP, strategic plans in Excel spreadsheets without systematic linkage.
• Delayed decision making due to manual reporting – by the time Excel reports are created, validated and presented, the underlying production conditions have already changed.
• Lack of transparency about causeeffect chains – why a 5% OEE improvement in the hall does not automatically translate into correspondingly better financial results.
• Reactive rather than proactive management – problems are only recognized when they already have financial impact, instead of being signalled early by operational indicators.
Why “Excel controlling” slows down strategic decisions:
Traditional Excel based reporting systems reach their limits when integrating different data sources. While a single OEE dashboard is feasible in Excel, linking it with ERP data, quality management systems and strategic planning tools quickly becomes confusing and errorprone.
The solution lies in a systematic three-level model that links machine data and operational metrics to the company’s strategy.
Operational KPIs focus on timely process optimization and problem detection. These metrics update at short intervals and aim to support daily production control. Typical operational KPIs are machine availability, current throughput times or scrap rates of the running shift.
Strategic KPIs, on the other hand, measure progress toward longterm corporate goals. They are usually evaluated weekly or monthly and serve as a basis for strategic decisions. Examples are market share development, customer satisfaction indices or return on invested capital (ROIC).
The decisive success factor lies in the systematic linkage of both levels. For KPIs it is critical that strategic and operational metrics are so aligned that everyone in the company recognises the connection between their daily activities and the overall success of the company.
Level 1: Machine data (Operational Dashboards)
• Realtime OEE values of individual plants with automatic traffic light logic.
• Current fault reports with classification by causes.
• Quality metrics per lot with direct linkage to machine parameters.
• Energy consumption per production unit for immediate efficiency assessment.
Level 2: Departmental metrics (Tactical Dashboards)
• Weekly OEE trends with comparison to previous periods and target values.
• Productivity metrics by product groups and cost centres.
• Throughput time development with bottleneck analysis across the process chain.
• Quality costs in relation to production value.
Level 3: Corporate strategy (Strategic Dashboards)
• EBIT development with breakdown by contributions from production efficiency.
• Capacity utilisation versus market demand for strategic investment decisions.
• Customer loyalty indices based on delivery reliability and quality metrics.
• Cash flow development considering working capital optimisation through shorter throughput times.
A mechanical engineering company with €10 million annual revenue improves its average OEE from 65% to 78%. This 13 percentage point improvement corresponds to a capacity release of about 20% which can be used for additional production or cost reduction without further investment.
Operational effects:
• Reduced unplanned downtime through predictive maintenance.
• Optimised setup times through systematic SMED application.
• Improved first pass yield through statistical process control.
• Lower energy consumption per production unit.
Strategic effects:
Based on industry studies, each OEE percentage point in a company with €10 million turnover can generate an annual profit contribution of about €100,000-140,000. The 13 percentage point improvement could thus enable an additional EBIT contribution of up to €1.3-1.8 million.
Purpose: Immediate reaction to production events and continuous process optimisation.
Typical widgets for production management:
• Plant status matrix: colour coded overview of all machines with current availability, performance and quality status in realtime update.
• Fault management centre: automatic alarm forwarding with categorisation by urgency and suggested solutions based on historical data.
• Quality alerts: immediate notification on deviations from target values with direct link to corrective measures.
• Shift performance live: current production volumes versus target values with projected day output.
Technical requirements: These dashboards need direct connection to MES systems and machine controls with data update in seconds to minutes intervals.
Purpose: Trend detection and medium term optimisation measures for area and production managers.
Focus metrics:
• OEE trend analysis: weekly development per plant with benchmark comparisons and systematic improvement potential identification.
• Productivity heat map: visualisation of efficiency evolution by product groups, shifts and teams to identify best practices.
• Cost centre comparison: production cost per unit over time with breakdown by material, energy and labour costs.
• Capacity planning: utilisation rate versus order forecast for optimal resource allocation.
Update frequency: Daily data aggregation with weekly management reporting.
Purpose: Support of strategic decisions at executive level through linking operational excellence with business results.
Executiverelevant KPIs:
• EBIT-bridge analysis: showing how operational improvements (OEE, quality, efficiency) directly contribute to result improvement.
• Cashflow impact: showing how reduced throughput times improve working capital and thus liquidity.
• Market positions dashboard: delivery reliability and quality metrics as drivers for customer acquisition and retention.
• Investment ROI tracker: measuring success of productivity investments based on concrete OEE and efficiency improvements.
OEE (Overall Equipment Effectiveness) used strategically:
Performance component → Capacity planning: an OEE performance of 85% instead of 70% means 21% more production capacity without additional investment. This allows, for strategic planning, either market share gains through additional delivery capacity or cost reductions through better fixed cost absorption.
Availability component → Investment decisions: systematic availability analyses show which machines offer the greatest leverage for capacity expansion. Investments in predictive maintenance can often be more cost effective than new machine acquisitions.
Quality component → Customer trust: first pass yield improvements not only reduce internal rework costs, but through reliable delivery capability also strengthen the strategic market position as a trustworthy premium supplier.
Most manufacturing companies achieve OEE values between 65% and 85%; values above 85% belong to the top tier.
Throughput times and cashflow optimisation:
Shorter cycle times = better cash conversion: Reduced throughput times can improve working capital and positively influence the liquidity situation; the concrete extent depends on the specific company situation.
Strategic planning advantage: shorter and predictable throughput times enable more precise delivery time commitments. This strengthens the competitive position, especially in custom special machine business, where delivery reliability is often more decisive than price.
Energy metrics as competitive advantage:
Specific energy consumption per output unit is becoming increasingly strategically relevant. Companies with systematic energy monitoring can not only achieve cost reductions of 10-15%, but also strengthen their sustainability positioning for increasingly environmentally conscious large customers.
EBIT margin by product groups:
Profitability transparency: Many medium sized mechanical engineering companies lack sufficient transparency about the actual profitability of different product lines. Systematic fullcost accounting often shows that supposedly successful standard machines generate lower EBIT margins than complex special solutions.
Strategic focus decision: This transparency enables deliberate portfolio decisions. Should the company target volume business with 8-12% EBIT margin or focus on special applications with higher margins?
Service ratio vs. new machine business:
Recurring revenue potential: Service and spare part business typically generate higher margins than new machine sales, though the actual margins vary by company and service type.
Customer lifetime value: Systematic analysis of service penetration per customer shows strategic potential. Customers with high service usage usually also have higher repurchase probabilities for new machines.
Customer loyalty index:
Beyond customer satisfaction: classical customer satisfaction surveys often do not capture the business relevant loyalty drivers. More important are concrete behavioural indicators such as repurchase rate, service contract signups and reference readiness.
Predictive customer value: Combination of delivery reliability, service response time and complaint handling enables predictive statements about future customer decisions.
OEE calculation with strategic relevance:
• Availability = (Planned production time – unplanned downtime) / planned production time
• Performance = (Actual production quantity × ideal cycle time) / available production time
• Quality = Good parts / total production
• OEE = Availability × Performance × Quality
• Strategic evaluation: Each OEE percentage point corresponds in typical mechanical engineering companies to an annual profit potential of €100,000-140,000 per €10 million revenue.
Cash Conversion Cycle:
• CCC = Days in inventory + accounts receivable days – accounts payable days
• Working Capital Ratio = Working Capital / Revenue × 365 days
• Strategic significance: Improved throughput times can optimise working capital and positively influence liquidity.
Service penetration rate:
• Service penetration = Service revenue per customer / total revenue per customer
• Service margin = (Service revenue – service cost) / service revenue
• Benchmark target: >25% service share of customer revenue for sustainable profitability.
Visualisation and operational control:
The OEE dashboard combines realtime monitoring with strategic analysis via multitier visualisation. The main view shows a trafficlight matrix of all production plants with green>85% OEE, yellow 70-85% OEE and red <70% OEE.
Pareto-analysis of loss times systematically identifies the largest levers: unplanned downtime, setup times, speed losses or quality problems. This analysis is automatically updated weekly and prioritises improvement measures by cost benefit ratio.
Strategic linkage to business management:
An OEE improvement from 65% to 78% corresponds to a capacity release of 20% without additional investment. This additional capacity can be used strategically for revenue growth without proportionally higher fixed costs.
Concrete widget configuration:
• Realtime OEE values: update every 5 minutes with trend arrows for direction.
• Losstime ranking: top 5 productivity inhibitors with quantified improvement potentials.
• Trend charts: 13 week trend with displayed target corridors and benchmark values.
• Financial Impact Calculator: direct eurovaluation of OEE changes based on hourly rates and fixed cost degression.
Operational quality metrics with business relevance:
First Pass Yield (FPY) measures the share of defectfree products in the first cycle. Typical mechanical engineering companies reach FPY values between 92-98%, where each percentage point improvement directly saves rework costs and reduces throughput times.
Complaint rate and cost transparency: Systematic recording of customer complaints by fault type, cause and cost impact enables prioritized quality improvements. Rework costs are often underestimatedsince only direct labour time is recorded, not the indirect costs for material handling, quality inspection and delivery delays.
Strategic link to customer loyalty:
A high FPY and low complaint rate correlate directly with customer satisfaction and repurchase probability. Customers with less than 1% complaint rate show 40% higher reorder rates than customers with >3% complaint rate.
Practical costsaving example:
A medium sized mechanical engineering company reduced its complaint rate through a systematic quality dashboard from 2.5% to 0.8%. The resulting savings in rework, warranty efforts and logistics costs amounted to €180,000 annually at €8 million revenue.
Service as strategic differentiator:
Service response time and customer satisfaction are often more important than purchase price in mechanical engineering. Machine downtimes cause opportunity costs of often €500-2,000 per hour for customers, which is why rapid service response is highest priority.
Operational service KPIs:
• Machine downtime: average repair time after fault report as indicator of service efficiency.
• Service response time: time between customer call and technician arrival on site.
• Sparepart availability: immediately deliverable parts in relation to total enquiries.
• First-fix-rate: share of repairs fully resolved at first technician visit.
Strategic value through service focus:
Service business can generate higher margins than new machine sales and is less cyclical. Systematic service expansion can significantly improve the overall profitability of a mechanical engineering company, because service revenues are more predictable.
Recurring revenue through maintenance contracts: preventive maintenance contracts create plannable revenue streams and enable deeper customer relationships through regular contacts.
Operational capacity planning:
Machine occupancy in realtime shows not only current utilisation but also planned capacity situation for the next 48 weeks. This enables proactive bottleneck prevention and optimal job sequencing.
Order preview and resource optimisation: integration of ERP data shows which machines or workstations could become bottlenecks. Early detection of bottlenecks enables timely capacity expansion or outsourcing of critical operations.
Strategic impact on market position:
Delivery reliability as competitive factor: In mechanical engineering, reliable delivery dates are often more purchase decisive than moderate price differences. Systematic capacity planning allows realistic delivery time commitments and thus better customer acquisition.
Capacity planning for investment decisions: The dashboard shows when current capacities are exhausted and extension investments become necessary. This transparency allows timely budget planning and optimal timing decisions for new machinery acquisitions.
ERP system as data backbone: Modern ERP systems such as SAP Business One, pro ALPHA or Sage form the basis for financial and commercial metrics. The challenge lies in integrating with production related systems.
MES connection for production data: Manufacturing Execution Systems capture machine and production data in real time. Market leaders such as COPADATA zenOn, Wonderware or SIMATIC IT offer standardised interfaces for dashboard integration.
Direct connection of machine controls: Modern CNC controls and PLC systems can deliver direct machine data via OPCUA standards for OEE calculations. This enables realtime monitoring without additional sensor technology.
Microsoft Power BI for officeaffine companies:
• Advantages: seamless integration into Microsoft environment, familiar usability, moderate licence costs.
• Disadvantages: limited realtime capabilities, more complex data modelling for manufacturing specific requirements.
• Suitable for: companies with strong Excel/Office focus and primarily strategic dashboards.
• Current prices: Power BI Pro is currently USD 10 per user per month; starting in April 2025, it will be USD 14 (around EUR 13) per user per month.
Table for visualization focused companies:
• Advantages: very flexible data visualisation, strong analysis capabilities, good performance with large data volumes.
• Disadvantages: higher licence costs (50-75 €/user/month), steep learning curve for power users.
• Suitable for: companies with complex analysis needs and dedicated controlling.
Specialised manufacturing BI solutions:
• Examples: Delmia Apriso, Lighthouse Systems, FactoryTalk Analytics.
• Advantages: preconfigured manufacturing KPIs, direct machine connectivity, industry experience.
• Disadvantages: higher overall cost, less flexibility for individual requirements.
• Suitable for: companies with specific manufacturing focus and standardised processes.
Phase 1 (Days 1-30): Foundation & Planning
• Week 1: Stakeholder workshops for definition of the most important 8-12 KPIs.
• Week 2: Datasource inventory and interface analysis.
• Week 3: Tool selection and pilot area definition.
• Week 4: Data model design and first prototypes.
Phase 2 (Days 31-60): Development & Testing
• Weeks 5-6: Dashboard development for pilot area.
• Week 7: User testing with production managers and controlling.
• Week 8: Refinement based on user feedback.
Phase 3 (Days 61-90): Rollout & Optimisation
• Weeks 9-10: Full rollout to all relevant areas.
• Week 11: Training for endusers and power users.
• Week 12: Performance monitoring and first optimisation round.
Software licences for companies with 100-500 employees:
• Power BI: €15,000-35,000 per year for 50-100 active users (based on current and upcoming price structure).
• Table: €25,000-60,000 per year depending on user number and server equipment.
• Specialised solutions: €40,000-100,000 setup plus €20,000-40,000 annual maintenance.
Implementation and consulting costs:
• External consulting: 20-40 consulting days at €1,200-1,800 per day for setup and training.
• Internal resources: 0.5-1.0 fulltime equivalent for 3-6 months (controlling/IT)
• Hardware/infrastructure: €5,000-15,000 for server or cloud setup.
Total investment: Realistic budget planning should allow €40,000-80,000 for the first year, then €15,000-30,000 annual operating costs.
Operational dashboards need minute level updates for effective production control, whereas strategic dashboards suffice with weekly or monthly updates. Excessively frequent updates cause information overload; too infrequent updates reduce responsiveness to critical developments. The optimal frequency depends on the decision horizon of the user group.
Focus on 8-12 core KPIs: OEE by machine group, EBIT margin by product group, service penetration rate, order backlog in weeks, and liquidity metrics. More KPIs dilute the focus and overwhelm decision makers in mid-sized structures. Choose KPIs that directly impact business results and can be influenced by those responsible.
Modern nocode tools like Power BI enable implementation by controllers or production managers without deep IT expertise. The prerequisite is a clean, structured data basis in the ERP or MES. External support for setup (15-25 consulting days) is sensible; thereafter independent maintenance and further development is possible.
Quantify concrete improvements: faster decisions due to better data transparency, reduced reporting time (typically 40-60% less manual Excel work), earlier problem detection to limit damage. A 2-3% OEE improvement through better transparency corresponds at €10 million revenue to an additional €200,000-420,000 annually. Dashboard investments typically pay off in 6-18 months.
Choose German or EU server locations and GDPR compliant tools. Implement rolebased access rights: production staff see only their areas, controllers have full access, executive management focuses on strategic metrics. Protect production secrets through granular user groups. Decide between cloud vs. onpremises based on individual security requirements and IT resources.
The five most important success factors for dashboard projects:
Recommended action for the first 90-day sprint:
Start with a pilot area ideally a production line or machine group with available data sources. Focus on 3–4 core KPIs that are both operationally useful and strategically relevant. OEE (Overall Equipment Effectiveness) dashboards are usually the best starting point, as they enable direct operational improvements while creating a clear business case for further investment.
Vision: From Excel chaos to strategic data utilization
Successful mid-sized machine builders will expand their competitive position in the coming years through systematic data utilization. While your competitors are still producing weekly Excel reports, you’ll be making decisions based on current, interconnected metrics. This agility will become a decisive competitive advantage, especially in volatile market phases.
The journey to a data-driven company begins with the first dashboard. Start today — your future market position depends on it.
[S] NetSuite – Manufacturing KPIs Guide: How to Measure Manufacturing Performance (2024): https://www.netsuite.com/portal/resource/articles/erp/manufacturing-kpis-metrics.shtml
[S] Bernard Marr – What Is The Difference Between Strategic And Operational KPIs? (2024): https://bernardmarr.com/what-is-the-difference-between-strategic-and-operational-kpis/
[S] Symestic – What characterizes a good OEE score? (2024): https://www.symestic.com/en-us/blog/what-characterizes-a-good-oee-score
[S] Matics – OEE Value: How to Calculate the Financial Impact of OEE (2024): https://matics.live/blog/oee-value-how-to-calculate-the-financial-impact-of-oee/
[S] InsightSoftware – 30+ Manufacturing KPIs and Metric Examples (2024): https://insightsoftware.com/blog/30-manufacturing-kpis-and-metric-examples/
Copyright © 2025 Peter Littau
Copyright © 2025 Peter Littau
