In order to win the 2018 World Cup, France had to coordinate themselves, rely on each player’s strengths, and function like a cohesive unit. Just like with football teams, technological systems need to be in synch with each other. By bringing together a network of software and hardware solutions, system integrators (SIs) act like a coach, helping companies coordinate their technology. Algorithms act as a team player, bringing machine learning capabilities to the team, diversifying the capabilities of an SI, ultimately better servicing the end-user’s transition to automated operations.
Many times, machine operators and OEMs want to automate their machinery, but don’t know where to begin. This is where SIs step in, introducing the best combination of products and services to help their customers network their systems to optimise their value.
The capabilities of SIs allow operators to capture tons of machine data. By turning data into solutions and automated processes, OEMs and machine operators can operate more efficiently. For example, an SI could implement a real-time Facilities Monitoring System (FMS) in a facility, whether that be worth millions or billions of dollars. After connecting all of the technology systems within the facility (HVAC, machinery, lighting, etc.) through a centralised platform supported by various algorithms, overhead costs could decrease, interruptions and downtime of operations could minimise, and OEE could improves. To further capitalise on their capabilities and improve value for themselves and their customers, all SIs can incorporate algorithms into their services to automate machine decision making.
Algorithms have the power to fill in the gaps where other software is lacking. If an existing maintenance software is only able to monitor current statuses of machinery, algorithms can take it one step further and accurately predict machine failure by analysing historical data. Similarly, if a facility is able to measure and collect data on its production environment, algorithms can be used to create the optimal environment to achieve maximum yield.
When figuring out how to close the gap between production and the growing demand for mushrooms, Delphy helped mushroom growers optimise the growing process with advanced growing rooms, climate control technology, and intelligent algorithms. As supervised learning algorithms updated optimal growing guidelines and ideal climate settings based on predicted output, the other technologies responded and adapted based on the algorithms’ predictions. As a result, yield increased, product quality improved, and water and energy consumption declined.
As more machine operators seek adopting Industry 4.0 and IoT-based technology, finding the right service to centralise and automate their operating system becomes necessary to remain competitive. When SIs are paired with intelligent algorithms, their services become more valuable and customer-focused. With algorithms included in SI capabilities, their customers’ transition to automation becomes easier and quicker, allowing machines to make more effective and accurate data-driven decisions sooner.
Forward looking models and intelligent machinery are the basis of future success in the technologically-driven world. Algorithms create a sustainable competitive advantage for SIs. Combining current data collection abilities with machine learning algorithms, SIs can help their customers easily transition to the future.
Widget Brain and our partners can help companies set up the data infrastructure they need to automate their decision making. We provide the platform (ALFA) and the algorithms to make machines smarter and more efficient with plugins easy to introduce to your current systems.
Want to know more about the industrial automation possibilities for your business and our intelligent algorithms? Contact us today at www.widgetbrain.com/getstarted/ to stay ahead of the pack.
Knowledge is power, particularly for algorithms. Understanding the basics of how algorithms need to be trained and managed will allow you to avoid scalability issues that can seriously affect your company. It will ultimately give you the edge over your competition who might not have read this article.
Labour demand forecasting is the first step in the workforce optimisation process where accurate demand is predicted based on different demand signals for any time interval. It correspondingly translates that demand into the appropriate labour headcount based on advanced labour standard models.