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IBM Manufacturing United Kingdom

British Sugar

Predictive Maintenance · Supply Chain Optimization

British Sugar needed to preserve expert knowledge, improve production and supply-chain decisions, and reduce downtime and cost by moving from intuition and siloed processes to data-driven operations.

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10% reduction in costs of raw material for purification processes
20% reduction in the average time to resolve production issues
20% reduction in the duration spent manually understanding data and information
25% reduction in decision-making time

Reported impact

Business outcome

British Sugar improved decision speed, reduced downtime and raw-material costs and created a scalable knowledge and simulation framework that helps new staff ramp faster and operations adapt more quickly.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

British Sugar partnered with IBM Research and IBM Consulting to build an AI-driven operating model around a supply-chain digital twin and predictive-maintenance capabilities. The first stage created a digital twin of the early supply-chain lifecycle, powered by Microsoft Azure OpenAI Service on Microsoft Azure, so the company could simulate scenarios without disrupting production. In parallel, British Sugar built a predictive-maintenance MVP using IoT sensors, machine learning and data analytics to identify equipment-failure patterns and reduce downtime. The company then integrated these models into control systems and decision workflows so experts could summarize thousands of data points quickly, improve supplier visibility and make real-time adjustments across production, water usage and purification processes. The resulting architecture combines simulation, operational telemetry and AI-assisted knowledge capture to drive faster decisions and preserve technical know-how.

Data flow

Operational and supplier data flow from IoT sensors and business systems into a digital twin and predictive models, where simulations and analytics are used to adjust production, maintenance and supply-chain decisions in near real time.

Solution architecture

4 components · 3 layers
  1. Ingest
    • IoT sensors Feed operating data into the predictive-maintenance and process-optimization models.
  2. Compute
    • Microsoft Azure OpenAI Service Powers the supply-chain digital twin used for simulation and scenario analysis.
  3. Uncategorized
    • IBM Research Co-developed the transformation approach and AI-enabled operating model with British Sugar.
    • IBM Consulting Implemented the business transformation framework spanning digital twin, predictive maintenance and knowledge workflows.

Architecture clues

  • AI models are integrated directly into data control systems
  • Digital twin currently focused on the “make” part of the supply chain
  • Predictive-maintenance MVP uses IoT sensors, machine learning and data analytics

Evidence from the source

As a result of this proof of concept, the company reduced failures and downtime from rotating equipment.
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.
Working closely with IBM Research® and the IBM Consulting® AI and Analytics practice, they deployed a business transformation framework to address its challenges.
Transparency Evidence & methodology 100/100 reference value · 84% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence84%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence15/20
    3 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. IBM

    British Sugar customer story

    First-party
    https://www.ibm.com/case-studies/british-sugar
    Source type
    Vendor-published customer story
    Published
    Dec 3, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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