British Sugar is the sole processor of the United Kingdom’s sugar beet crop and has been an essential contributor to the country’s food industry for over 112 years. Supplying over 50% of the UK’s sugar, the company has a long-standing history of producing high-quality sugar sold to customers and consumers around the world.

With innovation in its DNA, British Sugar is continuously looking to build upon and adapt its operations to maintain its competitiveness on the global sugar market. To take the business to the next level, it became pivotal to use the latest digitization capabilities, alongside overcoming a few key challenges around the capture of embedded organizational and technical knowledge.

To start, the company wanted to change how decisions were made by becoming more reliant on thorough analyses and data-driven choices—a process many organizations are undergoing. And, although the company had already integrated automation and 4G technology in its operations, some processes were still manual. For example, locating valuable knowledge and matching information that is required to make decisions was manually time-consuming, which lead to errors and missed opportunities.

Historically, decision-making at the company relied predominantly on the expertise, intuition and experiential knowledge of practiced employees who had been with the business for decades. This technical knowledge was often passed down through training schemes, shared experiences and stories. And, while this approach has been effective for more than a century, contemporary technology and the approaching retirement of many employees presented an opportunity for change. To stay ahead, British Sugar needed to embrace new frameworks that codify and scale experience across all data, leveraging its ability to adapt and thrive in a rapidly changing market.

Moreover, the company also faced departmental isolation, where different teams worked independently, resulting in repeated work and slowed processes. In terms of mechanical malfunctions, while they were few and far between, the company recognized the potential for unplanned downtime to impact production. To mitigate this risk, British Sugar considered implementing predictive maintenance strategies. At last, another aspect the company wanted to address was the escalation of costs that were intensified by erratic climate effects, impacting crop outputs and transportation effectiveness.

British Sugar recognized that technology held the key to unlock its full potential. So, the company partnered with IBM to create a data-driven organization powered by Artificial Intelligence (AI) and build on existing automation and 4G infrastructure. Working closely with IBM Research® and the IBM Consulting® AI and Analytics practice, they deployed a business transformation framework to address its challenges.

The first step was to develop a digital twin of the early stages of its supply chain lifecycle, powered by Microsoft Azure OpenAI service and hosted on the Microsoft Azure Cloud Platform. While British Sugar intended to apply this process to the entire business network, it currently focused on the “make” part of the supply chain. Through this smart factory digital twin, the company was able to simulate real-world scenarios and optimize production without disrupting operations. By leveraging advanced data modelling and analytics, British Sugar significantly improved its extraction and energy processes, material management and quality control. The company also optimized water management, production scheduling and scenario planning.

Simultaneously, British Sugar went on to create a minimum viable product (MVP) for predictive maintenance and prevention of equipment failures by using IoT sensors, machine learning algorithms and data analytics. As a result of this proof of concept, the company reduced failures and downtime from rotating equipment.

As a next step, British Sugar plans to incorporate the MVP into a full-scale digital twin control tower across all factories. The goal is to establish a centralized control tower that is powered by AI by 2025 and monitor predictive maintenance, crop volumes, supply chain plans and operational performance.

By integrating its models directly into data control systems, British Sugar managed to combine operational data with AI. This approach strengthened organizational design and empowered fewer experts across the board by augmenting knowledge search and quickly summarizing thousands of data points for rapid interpretation of complex documentation.