A report published by McKinsey in 2019 estimates that digital technologies could offer pulp and paper producers a 15 percent reduction in total cost and a five percent improvement in overall equipment effectiveness. A further report by McKinsey in 2021 reinforces that those digital technologies enable new levels of productivity in pulp and paper operations by leveraging large quantities of data to deliver better insights and outcomes. Successful digital innovators are seeing throughput gains of 5 to 10 percent, yield gains of up to five percentage points, and significant savings on materials, chemicals, and energy. For the industry, this represents a $4 to $6 billion opportunity, a realistic achievement here and now given there are already over 25 unique use cases generating value across the full pulp and paper value chain.
However, data is nothing without context. IBM Research estimates that up to 88% of IIoT data goes unused, while the Economist suggests that 99 percent of the value of manufacturing data is lost, with only 3 percent tagged and analyzed. Other hurdles include inaccessible legacy systems; a lack of real-time data; not labelling data logically; and the failure to effectively communicate data-derived conclusions.
An MES retrieves valuable process data seamlessly from fragmented control systems, interfaced with other data sources, ready to communicate with the Cloud and shop floor in real time.
Using the industrial internet of things (IIoT), the MES is fed with data on the real-time status or quality and processes, which can be used to help the operator identify inefficiencies and make informed, data-driven decisions that optimize performance and add value.
Bringing structure and standardization to data that is underutilized is therefore a vital step towards creating an integrated digital infrastructure that allows asset and operational data to be accessed, visualized, and analyzed for better performance.
ABB does this by using a layered approach, which starts with defining a strategy and the required data, mapping current network architecture, its bottlenecks, possible limitations and cyber security needs, and then determining and configuring data sources and interfaces.
The second layer examines physical distance and determines how best to collect data using Edge computing tools before mapping local applications within automation systems for later use. Finally, the third layer determines which data from the mill or facility, and which local applications and data, should be used and which of those applications should be transferred to the cloud.
Retrieving and harmonizing historical data from control systems and other sources in this way, using an integrated data management approach simultaneously with their MES, offers pulp and paper mill owners multiple benefits in terms of costs and efficiency. High-volume production data is organized hierarchically based on standardized models and structures, and data streaming between layers, to and from the cloud, is controlled and secured, and available in real-time at an optimized cost.