03 May 2021


6 Ways Chemical Manufacturers Can Use AI for Immediate ROI

The chemical manufacturing industry is highly volatile (no pun intended). Players in this sector are constantly faced with new challenges, including unanticipated variations in commodity prices, increased recalls and quality audits, and pressure to cut costs and create new efficiencies. Because of this, companies that fail to adapt and evolve may quickly lose ground to more agile competition.

The good news, though, is that chemical manufacturers can confront these challenges head on with artificial intelligence, machine learning, and advanced analytics. In fact, nearly half (49%) of manufacturers say these technologies will be the single most important factor in their competitiveness in the next two to three years according to Accelerate Your Data-Driven Transformation, a commissioned study conducted by Forrester Consulting on behalf of RapidMiner.

Simply put, AI helps manufacturers of all kinds move smarter and faster. And no emerging technology is better suited for supporting disruptive transformations in performance and value generation.

6 Ways Chemical Manufacturers Can See Immediate ROI With AI

Like any other manufacturing industry, chemical manufacturing has already been impacted by increased digitization and related Industry 4.0 technology trends. AI is simply the culmination of this transformation—and the most effective way to optimize operations and improve bottom lines. Let’s look at six specific examples of how AI is being leveraged today.

1. Predicting product grade

By detecting low-quality outputs early in the production process, AI can eliminate waste, ensure high-quality products, and reduce energy usage. 

Chemical manufacturers have traditionally flagged product quality issues by having experienced employees manually compare production data against various benchmarks. This comparison naturally happens after the product has been manufactured. Deep learning algorithms, though, can catch problems early by analyzing input data (like quantity and type) and real-time production factors (like temperature, pressure, and flow).

In a recent pilot program in Japan, for example, Mitsui Chemicals used deep learning to accurately predict the quality of gas products during chemical production a full 20 minutes before the final product was created. This early detection means you can intervene earlier to either save problematic products, or scratch a batch before it costs you more money.


2. Optimizing yield

Normal production variations are estimated to cause 85% of production problems. This is important to remember, because normal variations (like equipment being out of order) are exactly the things AI can help control for and adjust to optimize yield. 

Chemical manufacturing is, of course, highly dependent on external factors like temperature and pressure. These operational variables can be captured by sensors, tracked over time, and compared against outputs to make predictive models. AI can then use this data to both prescriptively optimize settings (before the manufacturing process begins) and make adjustments in real time (like changing the rate of flow or condenser temperature) as necessary.

This automated approach is a substantial improvement over human-led manual operational adjustments, which ultimately rely on intuition and experience and are by definition reactive. With AI you can implement adjustments before any problems are introduced, resulting in a consistent and predictable yield—along with increased quality and revenue.

3. Implementing predictive maintenance

The chemical manufacturing process relies on heavy industrial equipment, and unforeseen equipment breakdowns can cause disruptive delays. Maintenance is thus critical, and services and repairs are usually performed based on schedules and intuition (or following machine failure). Even scheduled maintenance downtimes can cost millions of dollars a day at large facilities. And in the event of repairs, replacement parts need to be on-hand in order to not cause further delays.

The whole process is, to put it simply, less than efficient—especially when you consider that the average cost of unplanned equipment downtime is $260,000 per hour.

Thankfully, AI offers a better option: predictive maintenance. With this approach, both historical and operational IIoT sensor data is constantly analyzed by AI, thus providing a comprehensive overview of (a) how machines are operating at the moment, and (b) how they are expected to perform in the future

This allows you to only repair machines when you really need to—meaning at the first sign of component failure or when usage criteria is met—while minimizing unplanned downtime and dramatically lowering maintenance costs. IIoT sensors also specify which component is at risk of malfunction, allowing for the automated order of replacement parts (rather than keeping unnecessary inventory on hand).

4. Producing more accurate forecasts

Many companies still employ classic forecasting models for managing their supply chains. The “old way” (like still using Excel as a forecasting tool) may be familiar, sure, but it’s also semi-manual, inflexible, and likely inaccurate—especially given ongoing uncertainty around COVID-19 and other economic indicators.

Getting your forecast correct is obviously important to your bottom line. Over-forecasts incur excessive storage and inventory holding costs (not to mention labor). Under-forecasts miss revenue opportunities. Either way, when your forecasts are consistently inaccurate or unrealistic, investors and stakeholders will quickly lose confidence in your leadership.

Once again, AI is a better choice to handle this challenge.

Existing forecasting models can easily be rebuilt on AI platforms, and then optimized to leverage advanced algorithms (like deep learning) that can identify variables that change demand and then auto-adjust forecasts as updated information is received. These models will run automatically—there’s no need to rebuild them every week.

The result is a substantial (a) reduction in cost and (b) increase in accuracy.

5. Improving health and safety compliance

Chemical manufacturing is one of the world’s most tightly regulated industries, with a mix of national and international protocols governing not only core operational procedures (like fabrication, handling, and distribution) but health, safety, and environmental (HSE) factors as well.

By combining Industrial internet of things (IIoT) technologies, real-time data collection, and advanced analytics, AI can help to both improve the safety of personnel and physical assets and strictly comply with any regulatory requirements related to data collection and documentation.

This process begins with sensors and other machine-augmented data, which together capture information from the physical world (like quality management data about chemical inputs and products) and transform it into a digital record of operations and supply networks. This monitoring data provides greater visibility of both assets and personnel—even at remote sites. When systems are fully integrated, AI can spot everything from employees not wearing proper PPE to leaks and other environmental hazards.

6. Boosting new product development

AI-technologies can be leveraged early within product and process development stages to expedite innovation and facilitate a more efficient “idea-to-market” process. For example, McKinsey believes chemical companies can use advanced analytics and machine learning to data-mine information from past experiments, simulate further ones, and systematically optimize formulations for performance and costs.

BASF, meanwhile, is investing to find out how AI can be used to predict chemical combinations and processes through the use of customized mathematical models and algorithms. This will help them forecast “the solubility of complex mixtures or dyes, as well as the aging processes of catalysts, consequently bringing about concrete industrial benefits.”

Wrapping Up

The chemical manufacturing industry has been at the forefront of previous technological transformations, as chemicals and related products have been heavily dependent upon scientific research and product development. That’s why artificial intelligence is such a natural fit—it adds real value to only R&D (as described above), but all aspects of production, marketing, and distribution. And it’s your best bet for becoming more operationally efficient and better able to leverage opportunities for disruption.

If you want to get a sense of how to measure the value of your AI projects, take a look at our whitepaper Talking Value: Impactful Machine Learning Models for Industry 4.0. We’ll walk you through how to think about assessing value and applying solutions to complex manufacturing problems step by step.


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