Enhancing efficiency through machine learning and digitisation

Why it's material

According to McKinsey, companies that are digital leaders in their sectors have faster revenue growth and higher productivity than their less digitised peers. They improve profit margins three times more rapidly than average and, more often than not, have been the fastest innovators and the disruptors and transformers of their sectors.1

How this issue links to other aspects of our business

Our global priority SDGs


Our emerging risks

Industry 4.0

Our strategic fundamentals

  • Grow our business
  • Sustain our financial health
  • Drive operational excellence

The global forces shaping our Thrive25 strategy

  • Globalisation and high levels of connectivity
  • The rapid pace of technological innovation, including AI

1 The McKinsey Global Institute: Digitization, AI, and the future of work: imperatives for Europe

Our approach

Machine learning (ML) is no longer the preserve of artificial-intelligence researchers and born-digital companies like Google or Netflix. Recognising this and in line with our agile operating approach, we work to integrate ML into our business processes.

Key developments in 2020

We continued to drive our data driven culture in order to drive productivity and profitability. This involves standardising and consolidating regional data science platforms and digital transformation strategies to support our global Industry 4.0 initiatives, including machine learning and advanced analytics technologies.

One workstream involves the extensive use of digital twins to promote discovery, interpretation, and communication of meaningful patterns in data. In the Sappi context, a digital twin is a virtual model of a process, or semi-finished or finished product. By pairing the virtual and physical worlds, we can analyse date and monitor systems, thereby anticipating and avoiding problems before they occur, preventing downtime, developing new opportunities and planning for the future through the use of simulations. As an example: we have created a digital twin for every dissolving pulp (DP) batch produced at Saiccor Mill. Each batch contains information relating to all the upstream processes that contributed to that batch, including timber, liquor and digester cook, washing and bleaching. This digital twin data ensures that process engineers have all the necessary data available in context to analyse issues in the plant.

In parallel to the development and continuous improvement of Digital Twins throughout all the regions, significant focus and effort has been invested in enabling our domain experts (such as engineers and research scientists) in the Data Science domain using a tool called RapidMiner. This RapidMiner enablement programme aims to democratise data science within our organisation with a view to expediting problem solving, encouraging innovation and empowering the broader Sappi community by equipping them with data, skills and tools. Through the programme, our people are encouraged to bring their ideas and business problems to the data science team.

Enabling our data-driven culture

Our aim is to provide an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. To achieve this, we use the Braincube data science software platform in addition to RapidMiner. We also provide our people with online and practical training and regular workshops are held to assist with developing ideas into a proof of concept.

All ideas pass through a series of funnels and gates, to ensure that the best ideas become operational and demonstrate value in terms of one or more of the following:

  • Process optimisation
  • Quality improvements
  • Reduced costs
  • Improved profitability
  • Time saving



McKinsey& Company say that machine learning (ML) is "based on algorithms that can learn from data without relying on rules-based programming".1 Stanford University suggests that ML is "the science of getting computers to act without being explicitly programmed".2 Carnegie Mellon' University's definition states that the field of ML seeks to answer the question 'How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?'"

Regardless of the definition, at its most basic level, the goal of machine learning is to adapt to new data independently and make decisions and recommendations based on thousands of calculations and analyses. It's done by infusing artificial intelligence machines or deep learning business applications from the data they're fed. The systems learn, identify patterns, and make decisions with minimal intervention from humans. Ideally, machines increase accuracy and efficiency and remove (or greatly reduce) the possibility of human error.4

1 https://mckinsey.com/industries/technology-media-and-telecommunications/our-insights/an-executives-guide-to-machine-learning
2 https://www.coursera.org/learn/machine-learning
3 http://www.cs.cmu.edu/~tom/pubs/MachineLearning.pdf
4 https://quantilus.com/why-is-machine-learning-important-and-how-will-it-impact-business/

Innovative solutions for a thriving

Promising initiatives currently underway include the following:

A real-time prediction model was developed in RapidMiner to predict yield utilising only intrinsic viscosity and the cellulose content of pulp. This RapidMiner artificial intelligence (AI)) evaluation allows us to identify the most important parameters influencing the pulp yield and tweak the operation conditions to maximise the pulp yield without compromising the pulp quality.