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Data based operating model goes beyond the transparency and leverages on big data as well as on a flexible organization across the value chain. Leveraging new technologies in a qualitative way that responds to complex market requirements and prepare for unforeseen events even more appropriately. Some of the key initiatives with general use cases highlighting Real Aware ability and approach to create customized solutions.
With the explosion of availability of primary & secondary data, gives birth to new operating models aim at leveraging new possibilities of data gathering, saving and analysis in order to generate value by better understanding, forecasting and managing the behavior of suppliers, customers and competitors in the chemical industry amongst others. The data based operating model does not aim at collecting as much data as possible but using the right information.
North American diversified chemical company which employed big data to integrate marketing information with production strategy that helped better contract negotiations and arrive at competitive pricing with an enhanced procurement strategy with a net annual saving of close to 18% in cost of procurement of critical commodities.
Typically, a range of factors does influence the output of the chemical production process, amongst others the concentration, temperature, volume and quality of materials as well as speed, lengths, intensity and technology of the process. Finding the factors that offers best product quality and highest commercial viability requires a modeling supported by big data.
A leading Asian Hydrocarbon conglomerate used Hadoop to seamlessly integrate multiple data sources on a real time basis along with Data Analytics to help in monitoring the amount of energy consumed, bring down the volume of waste, and boost the overall ROI by around 23% on an annualized basis.
The quality of production facilities determines maintenance efforts. Predictive maintenance leverages on low-cost sensors and pattern recognition to better understanding the life cycle of production facilities and its maintenance needs, opposed to time-based maintenance. This ensures that resources are applied to the critical elements of the plant’s operations on time. And it makes asset simulations much easier.
Pattern recognition helps to understand the development of chemical markets by identifying patterns of customer behavior or enhancing forecast accuracy with means of a predictive S&OP. While the demand of volume products is easy to predict, the focus of predictive analytics is on niche and opportunity products.
Predictive analytics drive performance of procurement in terms of both developments of category strategies (e.g. forecasting commodity risks and internal demand) as well as internal sourcing procedures (e.g. supporting material re-classification and reducing maverick buying.)
Operational Performance Management workflow with dashboard using analytics on real time data and legacy data sources. When it comes to business agility and optimizing your operations, information silos can be one of the biggest obstacles to delivering profitability. With siloes of IT and OT systems, your teams do not have full visibility to the data that drives real-time decisions, and ultimately drives your business. Operational inefficiencies make it difficult to align your processes or collaborate effectively. Eventually leading to frustrated teams and reduced profit margins.
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