We strongly believe that the Internet Of Things will allow businesses to improve and automate their processes all the way up to autonomous execution.  In manufacturing one of the crucial components of the Total Productive Maintenance system is Autonomous maintenance. How does the Internet Of Things help the implementation of Total Productive Maintenance? How does the Internet Of Things contribute to the Overall Equipment Effectiveness? And how does predictive maintenance as something that the Internet Of Things enables have anything to do with Total Productive Maintenance?

Let’s go back to the beginning, Overall equipment effectiveness or OEE is a measure of how well a production unit works compared to its full potential. Overall equipment effectiveness is a term coined by Seiichi Nakajima in 1982 in his book TPM tenkaiHe was one of the founders of the Total Productive Maintenance system which main objective is to increase Overall Equipment Effectiveness by applying the concept of the 5S and promoting 8 supporting activities.



The first S aims to eliminate anything that is not truly needed in the work area as for example unnecessary materials and equipment.



The objective of the second S is to organize the remaining items in order to define and maintain clean locations for tools, machines and materials.



The third S refers to the implementation of regular cleaning practices by dividing the manufacturing floor into different cleaning areas and assigning a responsible team to each area.



The fourth S is focusing on creating standards for performing the previous three activities.


self discipline

The objective of the fifth S is to make the system sustainable by ensuring that all standards are regularly applied.

The main objective of Total Productive Maintenance is to increase Overall Equipment Effectiveness which has three factors that are multiplied to give one measure:

  • Performance, of the theoretical speed at what speed did the production unit run
  • Availability, of the theoretical time the machine can be used how much was the production unit running
  • Quality, of the theoretical sale-able products the production unit could produce how many did it produce


The actual speed of a machine compared to the maximum speed determines the performance


Breakdowns result in unplanned downtime and changeover is the time between moving from one unit to another unit, it includes re-configuring a machine and the time to get a ready for production.


The units produced minus the defective units determines the quality.

Overall equipment effectiveness is measured as a percentage between 0 and 100% and can be reported on a machine-by-machine, product-by-product or shift-by-shifts as long as you make sure you collect enough data to be statistically significant. The eight pillars of Total Productive Maintenance are mainly focused on proactive and preventative techniques for improving equipment reliability. The pillars help to improve all the three factors Performance, Availability and Quality of Overall equipment effectiveness.


Places responsibility for routine maintenance, such as cleaning, lubricating, and inspection, in the hands of operators.


Have small groups of employees work together proactively to achieve regular, incremental improvements in equipment operation.


Schedules maintenance tasks based on predicted and measured failure rates.


Design error detection and prevention into production processes. Apply Root Cause Analysis to eliminate recurring sources of quality defects.


Directs practical knowledge and understanding of manufacturing equipment towards improving the design of new equipment.


Fill in knowledge gaps necessary to achieve goals. Applies to operators, maintenance personnel and managers.


Maintain a safe and healthy working environment.


Apply Total Productive Maintenance techniques to administrative functions.

Let’s have a closer look on each of the components of Overall Equipment Effectiveness and how it is related to the Internet Of Things:


The actual speed of a production unit compared to the maximum speed determines the performance.

Improving speed starts with measuring speed and the factors that could influence speed. In a production line, speed of a single production unit is limited by the production units downstream and upstream. First thing to do is to determine unambiguously the reason of the speed, is the bottleneck the downstream production unit, the machine itself or the upstream production unit driving the speed at any given time.


Breakdowns result in unplanned downtime. Post breakdown analysis allows you to find root causes to plan downtime to prevent breakdowns by executing preventive maintenance.

The Internet Of Thing allows you to monitor your production unit as it is running. Using the data you can do several things to reduce the risk of unplanned downtime:

  • monitor actual run time of the production unit and its parts, instead of a calendar driven maintenance you apply a condition based maintenance schedule
  • detect anomalies in behavior of the production units, temperature, sound, humidity and vibration are all indicators which potentially can indicate a potential problem
  • once you have collected enough data of your production unit you can have data scientists try to discover if they can predict when your production unit needs maintenance, when does a vibration indicate a problem etc
  • detect incorrect use of the production unit, incorrect configurations both in the control parameters as in the hardware configuration

Changeover is the time between moving from one unit to another unit, it includes re-configuring a machine and the time to get a ready for production. Changeover is typically reduced by better scheduling of the production (larger batch sizes, saw-tooth changeover times etc) and by offline improvements to make it easier and faster to make the adjustments. The Internet Of Things allows you to unambiguously measure changeover times.


The units produced minus the defective units determines the quality.

Improving quality is typically done by cold analysis, so it starts with monitoring and after the data has been collected try to find correlations. The correlations will identify opportunities for you to improve:

  • Is there a relation between defective reasons and shifts, this exposes opportunities to improve by training
  • Is there a relation between type of unit and defective reason, this exposes opportunities to improve in configuration of the production unit and training
  • Is there a trend in the quality and time of production, this exposes fatigue and opportunities to improve by changes schedule

The Internet Of Things allows allows you to real time monitor the number of defective units which allows you to react immediately.

How to start with the Internet Of Things

When dealing with the Internet Of Things security is extremely important. So the first thing you need to cover is to convince yourself your risks are sufficiently covered, production can and may not be interrupted due to measuring. We understand the choices and the consequences.

Starting with the Internet Of Things can be as easy as using existing programmable logic controllers (PLC) and connect them to the internet using one of the many available gateways. An alternative to using your PLC’s is to use external sensors and attach them to your production units. Both scenarios are relatively straightforward to execute and the starting point of collecting, monitoring and reacting in real-time to your reality.

Our experience is that once the hardware problems is covered you can use the data in existing Total Productive Maintenance system immediately. We make it safe to connect, collect, visualize and react.