Data Analytics in Manufacturing – Driving Business Results
Companies in the manufacturing sector can reduce costs, improve quality, and bring about improvements in their supply chain by adopting the new data analytics techniques that are rapidly becoming available. In fact, those firms that do not implement these new technologies stand to lose their competitive advantage and could see a deterioration in their profitability and market share.
Which are the areas in which manufacturing companies can hope to better their performance by using data analytics? In addition to the production process itself, analytics can help to improve financial management, reduce warranty liabilities, and help in managing sales and marketing expenditure.
Most importantly, data analytics can help firms establish a direct link between investments and profitability. Which new projects deliver the maximum benefit to customers? Are certain quality improvement expenditures justified? By using analytics, companies can base their decisions on hard facts instead of relying only on their judgement.
An increasing focus on data analytics
The top management in many manufacturing companies has realised that analytics could play a large role in helping them to meet their organisational goals. A survey found that two out of three firms are going ahead with investments in data analytics while cutting back on other expenditure that they consider less important.
The survey was carried out by Honeywell, a company that is involved in several businesses including engineering services, and KRC Research Inc. The survey, which polled 200 executives, found that the greatest problems that face manufacturing plants include machine downtime, unscheduled stoppages of work, and equipment breakdown.
Data analytics can help firms to address these issues. Telematics-enabled sensors can be installed in the plant to monitor activity and transmit data in real time. This allows condition-based maintenance to be undertaken. If a firm uses this technology, it will not be necessary to shut down the plant at regular intervals for maintenance.
Instead, the company’s engineers will be able to keep a constant watch on the efficiency level of the machinery being monitored. Scheduled maintenance could be undertaken only when it is necessary. This will reduce downtime and also ensure that productivity is enhanced.
Specifically, data analytics can provide the ability to undertake the following activities.
- Trend analysis – is productivity rising or falling? Monitoring output at the various stages of production can help a manufacturing plant spot trends and take pre-emptive action.
- Pattern recognition – is there a cause and effect relationship between two different events? The company’s engineers can use the data generated by sensors to establish if there are unintended consequences of certain activities.
- Critical range and limits – data can be used to set limits for the length of time for which a particular operation is conducted. This could replace guesswork or a process that involves trial and error.
How General Electric used data analytics
General Electric (GE), is a multinational company with manufacturing activities that span aviation, renewable energy, healthcare, and transportation. It is making a massive push into data analytics and the Industrial Internet of Things (IIoT).
By installing sensors that monitor activity in its machinery, the company has already gained significant benefits. GE estimates that improving productivity by 1% will give it as much as US$500 million in savings every year. If you analyse the numbers at a global level, a similar productivity gain by the world’s factories could result in an additional US$10-15 trillion in GDP over a 15-year period.
Take the example of GE’s Durathon battery factory in Schenectady, New York. Sensors are installed in every battery as well as all along the production line. Managers can monitor activity as it happens and initiate corrective action immediately.
GE holds the view that data analytics will play an increasingly large role in manufacturing activities in the future. It has developed the Predix System, a service that can help manufacturing companies incorporate analytics into its own operations.
Predix provides the software architecture and services to help companies manage their data and use predictive analysis for their processes.
This technology has been successfully used in the airline industry to monitor a plane’s landing gear to detect problems. Sensors are installed in 34 locations in a plane’s landing gear for monitoring hydraulic pressure and brake temperature. This allows the airline to spot potential malfunctions before they occur.
GE’s digital division and its Predix software has been widely accepted by many manufacturing firms. The technology is already being used in the production of jet engines, wind turbines, and oil-drilling equipment. GE hopes to offer Predix to a host of other industries in the future. Sales at its digital division could touch US$15 billion by 2020.
Data analytics is useful across a range of industries and activities
While traditional manufacturing companies can use analytics to their advantage, this technology has applications in many other areas too. A biopharmaceutical firm has successfully used analytics to improve the process that it was using to manufacture vaccines and blood components.
The company was experiencing a wide variation in the yield of the vaccines that it made despite the use of identical processes. It then broke up the manufacturing activity into many different batches and monitored the results. It was able to identify several parameters that affected yield. Using this information, the company made significant gains in the volume of vaccines that it could produce.
Many manufacturing firms also use analytics to monitor their supply chains. By doing this, they can ensure that materials are received in time. It is also possible to get to know about delays as they happen, allowing firms to arrange materials from alternative sources.
Using data analytics to select the best supplier
The information gathered from remote sensors can also help firms coordinate in a better manner with suppliers. Buyers can remotely view product quality and delivery schedules at any time. By using this data, a company can alter its future buying pattern to favour those suppliers that best meet the criteria that is of the greatest importance.
For example, for a certain purchase, a firm may want delivery in the fastest possible time frame. In another situation, it may want raw materials of the highest quality. Access to data analytics could help a firm in this decision-making process.