Raising the Stakes in Asset Management SoftwareSat, 09/01/2018 - 10:39
The need to keep tight control of resources and maintain updated information on a company’s assets spurred the creation of ERP systems. For the industry to move forward in its quest to improve efficiency, predictive analysis based on trustworthy and updated data is key, says Rafael Funes, Executive Chairman of LOVIS.
Business technology firm LOVIS has placed its bet on Enterprise Operating Systems (EOS) to solve ERPs’ data collection problems. “ERPs assume plans are executed as originally outlined,” says Funes. “But 99.9 percent of all companies have variations in their processes that prevent them from following these plans to the letter.”
Funes lists continuous downtime, unreliability of information for predictive analysis and not allowing data to be registered in real time as some of the disadvantages that ERPs present. According to a survey conducted by Nielsen Research, automakers lose an average of US$22,000 per minute due to unscheduled downtime and data collection can play a crucial role in these incidents. “ERPs must close their operating cycles periodically, usually three days a month, while they run administrative processes. This prevents continuous event registration from taking place,” he says. “Data is captured in the system hours or days after events occur, which leads managers to constantly work with outdated information that cannot be fully trusted.” The ability to capture the necessary data through a timely and on-the spot process is among the advantages EOS offers over ERPs, Funes says. “EOS does not stop so there is no loss of time and operations continue naturally,” he says. “Furthermore, while ERP takes about four minutes to register an event, EOS only needs 20 seconds to manage the same information, which effectively boosts registration efficiency twelvefold.”
As the industry moves toward Industry 4.0 practices, predictive and prescriptive analysis is fundamental and EOS’ capabilities to register data continuously is a significant advantage for potential clients, according to Funes. “I do not agree with the general concept the industry has of Big Data because it implies having to infer something from data contaminated with noise,” he says. “The challenge is creating what I call ‘Great Data’ from the start, for example, information without noise.” For him, Great Data makes analyzing and predicting behaviors a more precise process. Noise in data forces companies to clean their registries before building a correlation that may fail or that provide inferences and conclusions that are wrong.
Contrary to ERPs, EOS places information quality at its core and the company’s plan only as a function. Funes says the importance of this difference is that trustworthy data enables companies to plan well, reach their goals and better deal with variations because registration is performed according to reality rather than to previous conceptualization. Since EOS registers every operation, consumption and delivery the moment it happens, the data it offers reflects reality online and in real time. “Creating trustworthy and opportune information makes it possible to perform highly precise analytics and correlate results with exogenous variables,” Funes says.
Beyond what the industry knows as Industry 4.0, LOVIS’ goal is to boost the Enterprise 4.0 vision through its EOS solutions. “While Industry and Manufacturing 4.0 are oriented toward manufacturing processes and based on cyber-physic systems and robotics, Enterprise 4.0 is based on a platform that focuses on the end consumer,” says Funes. Just like its manufacturing counterpart, Enterprise 4.0 is based on optimal online communication and real-time data collection. However, the ultimate goal of this concept is to connect a company’s entire operation, including its administrative processes, its interaction with suppliers and its relationship with the end consumer.
While Enterprise 4.0 has yet to become a reality, EOS is fundamental to articulating the whole chain, according to Funes, and LOVIS’ next step is to launch a solution designed for the automotive industry that will boost efficiency throughout the supply chain. “It would be ideal for companies to know how resources are consumed in certain regions,” he says. “This information would enable producers to pre-supply components and then deliver them locally, cutting the need for suppliers to wait for an order and then ship it.”