5 Challenges The Chemical Supply Chain and How AI Can Help

Oct 13, 2021 8:15:00 AM / by ThinkIQ

What’s a modern car without an ECU? A pile of parts that won’t move. What’s a computer without a CPU? A blank screen. What’s an HVAC system without a thermostat? Thousands of dollars of equipment that won’t stop cooling or heating once the ideal temperature has been achieved. In other words, technology turns pretty useless without a brain.

When it comes to the chemical industry, a $4 trillion behemoth that 96 percent of all manufactured goods depend on, the stakes couldn’t be higher. Like any other industry, chemical manufacturers butt up against challenges in their supply chain all the time. The solution to most of those challenges lies in intelligence. At this scale, artificial intelligence (AI) is required to manage the complexity of data and systems. Considering materials can be hazardous and perishable, and batch manufacturing leads to varying product characteristics, it becomes that much more vital that AI is utilized. Thin margins, globalization, and high consumer demand raises the ante even higher.

Here Are The 5 Challenges Facing The Chemical Supply Chain: 

  1. Asset Tracking
    Manufacturers throughout the chemical supply chain need visibility into inventory and material flows. It is critical to ensure product consistency, potency, color, and composition. Any variation may compromise quality, profitability, and/or safety to a batch. By employing a real-time material-centric view, chemical manufacturers can collect data across the entire value chain from existing internet of things (IoT) sensors and business systems, model material movements and correlations to find root causes, create new fact-based trustable provenance data, and deliver actionable insights and decisioning to guide the value chain toward smarter choices.

  2. Production Planning
    Chemical production processes are complex. Starting materials, reactants, and solvents comprise recipes of exacting standards. While enterprise resource planning (ERP) systems have historically helped, the growing complexity of these recipes has risen beyond the limitations of ERPs. AI-powered supply chain management software can optimize production schedules, identify market changes, and anticipate changes in customer demand. Transformational intelligence through AI leads to smarter purchasing mapped to inventory needs, procurement knowing how suppliers are performing and their impact on production, and planning that is more granular by nature. The result is a more reliable and consistent supply chain overall.

  3. Ingesting Large Amounts of Data
    With all the variables at play, players in the chemical supply chain, and nuances in the production process, huge volumes of data are produced. This data is so profound, proper analysis is far beyond manual scan. AI becomes the only way to ingest it all without error (or going cross-eyed). Combined with machine learning (ML), factory digital-twin modeling makes possible great intelligence from far less real-world trial and error. Smart manufacturing powered by AI and ML also connects dots previously left separated amongst a universe of data. A larger overview can be achieved with greater insight into workforce design, operating expense, and safety thanks to data history, semantic modeling, data querying, and material ledger.

  4. Meeting Quality Assurance Standards
    Manufacturers and suppliers cannot allow the minutia of chemical manufacturing to result in lower quality. Compounds are expected to be delivered to rigorous standards and without delay. To maintain potency, color, and composition, raw materials must be tracked and accounted for at a granular level as much as equipment needs to be maintained and kept operational without interruption. AI allows for detection of anomalies that can be caused by imminent equipment failure. Being able to recognize and head off diminishing returns using predictive maintenance is an invaluable tool in the chemical supply chain manager’s toolbelt.

  5. Proactive Supply Chain Management
    Figuring out the calculous behind customer demand and pricing trends in relation to production scheduling is the real trick to the whole thing. All manufacturers want to be proactive rather than reactive. Proactivity requires real-time adjustments, AI analysis, and ML algorithms to anticipate technical difficulties, equipment failure, and otherwise unexpected costs. This kind of intelligence is also the stuff of sustainability, environmental responsibility, and compliance.

Digital Manufacturing Transformation 

These five chemical supply chain challenges are just the tip of the iceberg. The complexities are numerous, and the solution is greater than any single concept. A Digital Manufacturing Transformation SaaS that includes AI, ML, a material ledger and more represents a quantum leap in smart manufacturing that is long overdue in the chemical space. ThinkIQ’s raw material intelligence platform produces unprecedented sensor data visibility, significant yield improvement, and virtual elimination of recalls (99.999 percent) that has led to tens of millions of dollars in operational savings.

Be sure your technology has a brain. Embrace digitalization and Industry 4.0 smart manufacturing in your chemical supply chain. Talk to a ThinkIQ expert today to learn more about how we can overcome your challenges together. We also have a new selection guide eBook to help you better understand the questions you should be asking. Download your copy today.


ThinkIQ Guide to Smart Manufacturing

Tags: Industry 4.0, Chemical Supply Chain, AI/ML


Written by ThinkIQ

Think IQ

The Industry 4.0 Data Revolution

Proven to improve manufacturing yield, safety, quality, and compliance by making sense of your data.

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