Over the years, maintenance operations – as defined by standard plant accounting procedures – have been recognized as major expenses for manufacturing plants. Since 1981, the industry has seen a significant increase in overall maintenance costs for manufacturing companies. In 2000, costs had reached over $1 Trillion. It was during these years that the saying “maintenance is a necessary evil” was coined due to insufficient technological advances to address the inefficiencies causing manufacturing organizations great loss. For many years, manufacturers have been trying to overcome reactive maintenance expenses with different approaches to maintenance operations, such as preventative maintenance. Today, we are seeing a transformation in the way manufacturers handle maintenance operations with an application of the Internet of Things (IoT).

Before technological advances, reactive maintenance played a major role in the industry’s increase in overall maintenance costs. The reactive or corrective maintenance approach came at a great expense for manufacturing organizations. By performing “just-in-time” maintenance events at the time an issue was observed, manufacturers increased their equipment downtime and cost.

The preventative maintenance approach improved maintenance processes. Instead of basing maintenance schedules on measured, reliable machine-health data, preventative maintenance used arbitrary points in time with the intention of servicing manufacturing machinery before any failure could occur. By measuring hours of operation statistically and using historical data to determine a routine schedule for maintenance operations, manufacturers were able to reduce reactive maintenance events. Preventative maintenance had several benefits for manufacturers such as increased machine uptime, reduced use of energy and lengthened life span of equipment. Although a preventive maintenance strategy was a move in the right direction, this approach also had a few drawbacks, such as over-maintenance of machinery that led to a greater demand for personnel in order to keep up with the increased frequency of regular inspections.

Both practices of reactive and preventative maintenance paved the way for newer, smarter maintenance processes today. A core concept of these industrial advancements is the ability for machines to communicate with a data analysis engine; enabling computers to make automated manufacturing decisions. Systems that perform data analysis and automated decision making to provide immediate, actionable process feedback are setting the stage for a new industrial revolution. This is a specific application of the idea that is commonly referred to as the IoT. According to the Internet of Things Global Standards Initiative, IoT is defined as the global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving inter-operable information and communication technologies.

By integrating data analysis systems with enterprise asset management systems (EAM) or computerized maintenance management systems (CMMS), manufacturers are able to use IoT to better manage maintenance operations. For instance, a Life Sciences company may want the ability to send data measured on a piece of equipment to an analytical system, coupled with asset management software. When the data coming from a specific part of machinery indicates that it is in need of maintenance, or that it is on the road to failure, the analysis system sends an alarm to the asset management software; automatically generating a work order to service that asset.

In the following articles, I will address the technology driving IoT, as well as how the Life Sciences industry fits into the picture.