02 Aug Seven ways that an anomaly detection system will make your business more competitive
August 2nd, 2021
Anomaly detection – also known as outlier detection or outlier analysis – is the process of identifying data that deviate from the acceptable behavior of a dataset. It constitutes a core element in data mining, in order to discover patterns that indicate an abnormal behavior and can be a powerful tool for any ambitious business that strives for sustainable operations and a competitive edge.
Whether you run a manufacturing plant or a big facility, it is obvious that improving the quality of your products or services and reducing your production and operational costs can help your business thrive. This has always been the name of the game, but in today’s world, with a globalized economy inviting competition from across the globe and resources scarcity pumping your utility bills, keeping things under control becomes increasingly challenging. And since we cannot change the nature of the global economy, the focus should be on how we can increase your profit margin potential, while you retain high quality.
Having an anomaly detection strategy can significantly help in this direction. With all the available technology, the multichannel communications, and the emerging IoT solutions, the implementation possibilities can be endless. However, there are some key elements that are crucial to an anomaly detection system’s success:
- Seamless integration with existing systems
- Communication protocol and device agnostic
- Artificial intelligence (AI) and machine learning (ML) capabilities
- Both edge and cloud-based data analysis
- Fast response time
- Autonomous decision making
- Customizable and easy to re-configure
- Easy to scale
Assuming that the above are in place, let’s see some practical ways that an appropriately implemented anomaly detection system can help your business increase its productivity KPIs and remain competitive:
- In the everyday function of a big plant or a busy building, some technical problems are difficult to notice when they occur. They can be creeping for months unnoticed, losing your business money, either by lowering the quality of product or service or by consuming more energy than needed. By applying AI algorithms to critical equipment datasets, these problems can be identified in the very beginning and dealt with.
- In such environments, a machine will rarely operate independently. A possible malfunction will usually affect the performance of neighboring equipment and correlated operations as well, leading to even bigger operational costs. By noticing and analyzing multi-point abnormal behaviors early, you could save much more than a single machine’s repair cost.
- Early anomaly detection can help you maintain full control of the operational behavior of a facility. A variety of techniques can be applied to monitor a broad spectrum of failures, without having to invest in dedicated and expensive instrumentation, whose purchase and maintenance would add to your business costs.
- Downtime is the killer of business profitability. When a process machine fails, the direct and indirect costs can go through the roof and are usually much greater than the purchasing cost itself. Awareness of an imminent state of failure before it arises will give the operators the advantage of acting proactively and putting in place the best countermeasures in terms of costs and timing.
- In the absence of anomaly detection, real-time monitoring, and autonomous decision-making system, there should be more people employed for maintenance and damage repair, in order to secure high levels of operability. As such, predictive analytics can also save you from having to occupy excessive personnel.
- The importance attributed to energy efficiency is increasing. And rightfully so. Energy efficiency can be a major factor for a company in becoming more competitive in the global marketplace. In facilities where lots of machines and people operate, and conditions frequently change, it is critical to have a reliable system in place to monitor energy consumption patterns. By using the intelligence of machine learning techniques, you can stay assured that energy use and respective costs will be kept to a minimum.
- What has also proved to be a very important, yet underrated, consequence, is that downtime could severely damage a company’s reputation. Either by delaying orders to clients or having unsatisfied tenants, machine failures can have a huge financial impact on a business and challenge its sustainability.
From any of the above reasons or all of them together, it should be clear why today you need an anomaly detection system in your facilities. Many methods could be applied, while different hardware equipment and software solutions could be installed.