Operational Efficiency is the ability of a firm to deliver its products and services to the customers with the adequate use of the available resources, incurring low cost of operation.
Setting up a business is one thing and running it successfully through long is whole another story. After establishing a firm foothold in the market, one fundamental question that we, as the owners need to ask to ourselves is that “Is the Business as efficient as it can be?” Particularly vouching for the manufacturing industries, the main task is to recognize the wastage of the resources. Doing this manually is quite a job and the probabilities for anomalies are higher. The most reliable solution to address this is the Big Data.
Predictive machinery are the new bold step towards improvising the Operational Efficiency. Energy is seldom the largest outlay for a manufacturing firm, at the same time it is the most ambiguous concern. In the last decade, the energy consumption is recorded to rise up to 28% and is expected to hit the scale of 50% in the near future. Hence there is a hard necessity to monitor the energy usage and reduce the pseudo usage.
What data do we actually need to gather to analyse the loss of energy? It’s not just one or two machines that are under operation in an industry. More than 10 plus machines simultaneously operate performing different jobs. Since the influence of the IIoT, all of these machines can be interconnected and the data sets such as the power consumption, specific parameters like the current and voltage should be obtained and made sure that all of these are in the range given for operation.
Once we are able to accumulate the data, with the aid of powerful analytical tools, we should be able to read the machines with the inputs that they give regarding their operations and necessary steps can be taken accordingly. Energy meters that are initially installed to the machines now come handy with an inbuilt facility and are directly compatible for the IoT platform. On the other hand, real time energy profiles are available to achieve the same. These altogether, help the manufacturers understand the proper schedules to run the machines on.
Integrating Industrial IoT also helps in eliminating the slug energy consumption being caused by the over worked machines that need to be serviced. Just one mobile application on your smartphone can remind you the low efficiency of a particular machine and you can cater to it at the right time. On a weird note, this technique of autonomous system doesn’t actually make the human resource negligent; in fact, it makes them more cautious. It’s mostly like the owner monitors the machine and the machines monitor its attendees.
Rolls Royce, a leading manufacturer of aircraft engines, has innovated through its amazing technology for managing the Health of the Engines through their Engine Health Management System. The EHMS System, a combination of tools for powerful analytics and sensors generates and maintains data in terabytes for every flight, which in turn allows Rolls-Royce for identifying issues and planning maintenance in advance. This has resulted in minimizing passenger delays and keeping operating costs down for its customer’s airlines, thereby improving the Operational Efficiency.
The main aim is to address the problems even before they spiral in. The need to address to these problems that will hinder the profitability of an industry is accurately analysed by the concept of IIoT and deploying one such thing is always on the positive hand!