Managing physical spaces for a large sprawling company is a monumental task. That is one of the main responsibilities of facility management. The discipline has to manage resources, people, machines, processes, and technology.
Using data and computational tools makes life easier for the facility management team by streamlining information, automating processes, and forecasting the next steps. Today, with the increasing penetration of the Industrial Internet of Things (IIoT), data is available in huge volumes – which is why it is called big data.
Big data and facility management
Big data generated from all the machinery and equipment in a facility can be used for effective facility management. The following sections explain briefly some of the ways big data is being used to improve processes, reduce cost, and do things that used to be impossible.
Large businesses have a huge number of employees, locations, machinery, experts, etc. Coordinating between multiple of these variables to create schedules for tasks and processes is cumbersome. The centralized data management system of the company has all the necessary information. Big data tools can be used to create schedules in a fraction of the time manual scheduling would have taken.
Tracking thousands of employees, hundreds of assets, and processes across different locations is near impossible. Modern IoT devices used in manufacturing are equipped with sensors and network connectivity features that can be used to track everything happening in a facility. This can often be done in real-time, from remote locations.
Large organizations handle different resources to run their operations. This ranges from raw material to finished inventory. The supply chain of the organization has to deal with multiple vendors, suppliers, sales teams, and other intermediaries to perform their tasks. Managing all the stakeholders to bring together the resources required to operate a facility is made easier by big data. Tasks like order management, inventory control, and reserve material management can be automated and streamlined using the insights gained from big data.
All processes are recorded and logged by all devices at the central data management system. The data will span across multiple years and conditions. The large volume of data can be used to analyze the gaps in manufacturing processes happening in the facility. This can lead to strategies to optimize various processes and resource utilization.
‘A stitch in time saves nine’ is the principle behind predictive maintenance. The data about the machine conditions and operating factors are analyzed to forecast the next failure. This information is used to conduct maintenance to pre-empt such incidents.
Conventional statistical methods are not sufficient for such forecasting. The data that can be handled by such methods are limited and the result will also have significant limitations. Big data and artificial intelligence can be used to make reliable predictions of machine failure. Reliable predictions help in timely intervention measures to avoid machine failure.
Machine lifetime optimization
The infrastructure required for manufacturing operations is acquired by a business with significant capital outlay. The useful life of such equipment should be maximized to distribute the fixed cost of the capital outlay. Failures or suboptimal use of the infrastructure will reduce their lifetime.
Predictive maintenance and other proactive management measures facilitated by big data help organizations to optimize the utilization of their assets. This, in turn, increases the lifetime of expensive machinery and equipment.
Big data = cost savings
Facility maintenance looks at reducing the cost of running a facility. The techniques range from process optimizations to predictive maintenance. The use of big data in facility management helps in each of these factors.
Big data leverages cheap computing infrastructure and powerful algorithms that can scale dynamically. This is cost-efficient as it helps to bring down costs in other areas too. The cost-saving due to big data can far outstrip the cost of its implementation.
Bryan Christiansen is the founder and CEO of Limble CMMS. Limble is a modern, easy-to-use mobile CMMS software that takes the stress and chaos out of maintenance by helping managers organize, automate, and streamline their maintenance operations.