7 steps to efficient production

This basic step has an immediate, visible impact. Fast, accurate communication about problems lets maintenance react sooner, which reduces downtime. Removing communication noise speeds up the whole fault-resolution process, leading to less lost time and higher machine availability.

Digital problem reporting replaces inefficient paper records and verbal communication. Operators can report a fault instantly via touch or mobile devices right at the machine in a few taps. The system automatically assigns the fault's priority and notifies the responsible maintenance staff. Every report is traceable from submission to resolution, creating a complete fault history and enabling analysis of recurring problems.

Visualizing line status and reported faults on screens in the plant ensures transparency and instant awareness for the whole team.

Digital checklists standardize processes and cover checks of cleanliness, equipment, lubrication, adjustment and other critical machine aspects. Operators actively monitor machine condition, which leads to catching minor problems before they grow into serious failures. This proactive approach significantly reduces the frequency of random breakdowns and extends equipment lifespan.

Autonomous maintenance is a cultural shift where operators take responsibility for basic equipment care. A key success factor is digitizing checklists, which eliminates the shortcomings of traditional paper lists.

Digital checklists bring a number of fundamental advantages over paper forms. They ensure consistent data entry, automatic saving into the system and instant availability for analysis. The system can alert the operator to skipped items or require confirmation of critical points. If an operator identifies a problem during a check that they can't solve themselves, they can escalate it straight from the checklist to the relevant people - maintenance, technicians or managers, who receive an instant notification and can address it. This eliminates errors caused by transcription, lost forms or illegible records.

Digital records also enable tracking trends over time, identifying problem machines and evaluating whether operators perform checks correctly and at the required intervals. Management has an instant overview of all checks and can react quickly to deviations.

The shift from reactive maintenance (repairs after a breakdown) to preventive maintenance is crucial. Digitizing maintenance plans ensures service work is performed at optimal intervals. Systematic preventive maintenance minimizes the risk of sudden breakdowns and extends equipment lifespan.

Preventive maintenance is a strategic approach to equipment care based on predefined intervals and manufacturer recommendations. Instead of waiting for a breakdown, critical components are replaced or serviced in regular cycles - daily, weekly, monthly or yearly. Digitizing these plans brings major advantages: automatic task generation, tracking the history of work performed, spare-parts records and the ability to optimize intervals based on real operational data.

A well-designed preventive maintenance system includes complete documentation of all machines, their critical components and recommended maintenance procedures. Maintenance staff have precise instructions on what to check, how often and by what methods. Transparent planning makes it possible to coordinate maintenance with the production plan, minimize conflicts and make the most of planned downtime. A record of completed work creates a valuable history that helps identify recurring problems and optimize the frequency of interventions.

The 5S methodology (Seiri, Seiton, Seiso, Seiketsu, Shitsuke) creates an organized, safe working environment. A clean, organized workplace reduces the risk of accidents, shortens the time spent looking for tools and improves operators' overall morale and productivity.

Process audits, especially LPA (Layered Process Audits), are a structured system of regular checks in which different management levels verify compliance with standards and processes directly at the workplace. LPA audits help identify deviations from standard procedures, ensure consistent quality and prevent problems before they affect production.

They also make it possible to optimize processes that aren't directly related to maintenance but affect overall production efficiency - such as correct machine setup, adherence to technological procedures or the efficiency of logistics flows.

OEE (Overall Equipment Effectiveness) is a key metric that shows a machine's overall effectiveness and reveals bottlenecks. Regular data evaluation makes it possible to identify the main causes of losses - downtime, reduced performance, defective output - and target improvement activities at them. Measurement and analysis alone don't deliver improvement, but they provide the data foundation for the right decisions and for prioritizing actions.

Visualizing OEE and its components in real time directly at the workplace lets the team react to a drop immediately. In-depth analysis and comparison of OEE over time (between shifts, months, workstations, projects) reveals trends, cyclical problems and the impact of changes made - and helps understand process dynamics and prevent the same mistakes from recurring.

TPM Analytics also provides overviews of key reliability and quality indicators. TOP 10 fault rankings by frequency and downtime duration reveal which faults burden production the most and where the greatest improvement potential lies. TOP 10 overviews of scrap counts and their reasons work the same way - they let you focus improvement activities where the biggest quality losses arise. MTBF (mean time between failures) and MTTR (mean time to repair) provide an objective measure of equipment reliability and maintenance efficiency over time. The analytics also track production-plan fulfillment and actual output, so management always has a complete, data-backed picture of production performance.

Reducing scrap requires a combination of process methods and data support. The key is to identify the real causes of scrap and remove them in a targeted way - not just to watch a summary figure.

Process methods

The foundation is analyzing scrap causes with the 5 Whys and Ishikawa methods, which reveal root causes instead of mere symptoms. Poka-Yoke measures eliminate errors through design or process barriers. Standardizing production procedures and enforcing them via LPA audits prevents the same mistakes from recurring. Where scrap arises, it's necessary to introduce statistical process control (SPC) and continuous monitoring of critical parameters directly in production.

Data support in TPM&M Analytics

TPM&M Analytics displays process scrap with a financial expression at every level of the production hierarchy. A hierarchical drill-down lets you break the summary figure down to individual stations and specific reports with the scrap reason - so it's immediately clear where the real losses arise. A current-day filter and a real-time cumulative chart per workstation provide visibility right within the running shift for operators, foremen and process engineers. The AI module continuously evaluates both sudden spikes and slow scrap creep and automatically generates alerts to the responsible users before a configured threshold is exceeded.

This isn't a one-off step but a philosophy. Continuous improvement (Kaizen) is the core of TPM and a natural evolution built on the correct implementation of all the previous steps. Once the tools above are in place and working effectively, the team constantly looks for new improvement opportunities. Small, incremental changes accumulate a large impact in the long term, leading to sustainable productivity growth.

The integrated link between the action-plan module and the TPM&M app closes the entire PDCA cycle. The action-plan module lets you create and assign responsibilities and deadlines to individual improvement steps and track their completion, including the effectiveness of implemented measures, by automatically loading data directly from TPM Analytics.