Advanced Analytics for the Factory of the Future: A Case Study on Ashland Manufacturing
Technology Category
- Analytics & Modeling - Real Time Analytics
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Cement
- Pharmaceuticals
Applicable Functions
- Quality Assurance
Use Cases
- Additive Manufacturing
- Manufacturing Process Simulation
Services
- Testing & Certification
About The Customer
Ashland Manufacturing is a $5 billion US-based provider of specialty chemical solutions. The company employs 7,000 people worldwide and consists of two commercial units: performance materials and specialty (pharmaceutical) ingredients. The company has been manufacturing construction materials since the seventies and has slowly shifted its product focus towards personal care and pharmaceuticals. This transition has sparked fresh challenges for Ashland, requiring a shift towards achieving higher added value, lower product throughput, and more control over its production processes. The company's plant in Doel, Belgium handles the specialty ingredients.
The Challenge
Ashland Manufacturing, a $5 billion US-based provider of specialty chemical solutions, was facing a series of challenges. The company was shifting its focus from construction materials to pharmaceuticals, a transition that brought about new challenges. This shift required higher added value, lower product throughput, and more control over production processes. The company was also dealing with seemingly 'unsolvable' production issues and a need for operational efficiency to boost quality and profitability. There was also pressure to increase Good Manufacturing Practice (GMP) production throughput. Furthermore, the company had to navigate the new territory of strict GMP standards relating to pharmaceutical product quality.
The Solution
To address these challenges, Ashland turned to TrendMiner’s self-service industrial analytics solution. This platform provided actionable insights from processing data, analyzed automated plant production data in near real-time, and visualized this data for the team to apply proven methodologies such as Six Sigma and the DMAIC cycle. The platform was easily integrated with existing AspenTech IP.21 historian software, allowing product engineers to use it with familiar tooling like computer aided engineering (CAE) and advanced analytics without needing a data scientist. TrendMiner helped engineers discover which factors in the process were influencing the quality of the finished product and enabled process and production engineers to raise certain 'red flags' and prevent incidents from happening in the future.
Operational Impact
Quantitative Benefit
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