Optimizing the function of boilers, turbines and other capital equipment used to generate power requires a careful balance of fuel, heat, pressure, operator proficiency and many other variables. Managing the process on a day-to-day, or minute-to-minute basis, is like performing a skilled and well-orchestrated dance—partly based on data, but also based on operator expertise. Yet, adding more accurate information to the equation can potentially save millions of dollars, cut emissions significantly and even expand the working life of your equipment.
Several trends in the food and beverage industry are leading to challenges for manufacturers that can be best addressed with data analytics. With growing digitalization, more companies have access to the kinds of data that can transform their processes to meet the latest consumer demands as well as to shorten time to market, reduce costs, and shrink health and safety risks.
In agrochemical, pharmaceutical and other industries that manufacture complex chemicals, finding ways to reduce waste and improve inefficiencies often hinges on selecting the right chemical compounds. Data analytics can help manufacturers find alternative compounds that meet complex requirements, decrease raw material usage or enable more cost-effective, sustainable processes.
Whether it’s fake olive oil, coffee bulked up with husks and twigs, or honey tainted with antibiotics, food fraud is a growing problem worldwide. The Australian research organization CSIRO states that the economic damage alone from food fraud has reached $35 billion (in US dollars) in 2018. The underlying cause is nearly always financial gain and economic pressures to save money by using inferior (or mislabeled) products. Predictive analytics is one tool manufacturers are using to combat food fraud.