Chemical Supply Chain: Challenges and Opportunities in the Era of AI

The chemical industry is one of the most important industries in the world. With a chemical supply chain that spans from chemical manufacturing to chemical distribution, it is essential for this industry to have a tight ship. However, with many moving parts and many stakeholders involved - including chemical manufacturers, distributors, retailers, and consumers - there are challenges in keeping operations running smoothly.

TL;DR

• The chemical industry is a vital sector with a complex supply chain involving manufacturers, distributors, and retailers.
• Artificial intelligence (AI) is being adopted in the chemical industry for various purposes, including production optimization, risk assessment, inventory control, and more.
• AI can enhance supply chain management by providing visibility into inventory levels, predicting production needs, and optimizing performance across all levels.
• Chemical manufacturers face challenges such as lack of visibility, quality assurance standards, planning production, coordinating material delivery, and managing inventory for cost efficiency.
• AI can optimize truck loadings, route selection, contract negotiation, and help companies comply with environmental regulations.
• Want to implement AI in your company and improve your business? Don’t wait, contact us !

The supply chains for chemicals have evolved and adopted new digital technologies in the past few decades. Artificial intelligence (AI) is no exception to this trend, with chemical companies turning to AI tools for several purposes - from chemical production optimization to chemical allocation management, risk assessment, and mitigation, as well as inventory control. AI is revutionalizing the supply chain management already. To improve the chemical supply chain performance and increase profits through more efficient processes while still protecting human health and environmental quality, artificial intelligence can optimize performance across all levels of the supply chain. This article discusses how chemical suppliers can leverage artificial intelligence for their supply chain management needs.

The Shape of Chemical Industry

The chemical industry is one of the largest economic sectors in the world, 96% of all manufactured goods depend on chemicals in one way or another and chemical manufacturers produce over $4 trillion worth of chemicals each year (global revenue of the chemical industry). It is a highly competitive, global industry that has a diverse product portfolio. It can be divided into several distinct groups:

Petrochemicals - chemical products from chemical reactions derived from petroleum and other fossil fuels (gas, coil) or renewable fuels (palm oil, maze) through refinery process.

Agrochemicals and Fertilizers - chemical products used in agriculture and gardening used for growth and health (fertilizers) or safety (pesticides).

Commodity Chemicals (or Bulk Chemicals)- chemical products such as rubber, plastics, chemical fibers, detergents, solvents, adhesives, or cleaning compounds mainly used for industrial purposes.

Specialty Chemicals - chemical products that serve a specific function or industry such as paints, coatings, colors, cosmetic additives, flavors, food additives, fragrances, etc.

Pharmaceuticals - a particular branch due to the specifics of the final product and strict regulations. The SCM for pharmaceuticals has different challenges and opportunities from the chemical industry. You can read more about the drug supply chain here.

Industries such as chemical manufacturing and its downstream processing industries depend on chemical supply chains for raw materials, products, and facilities to process their goods. The supply chain is primarily focused on getting the right amount of inventory to where it needs to be at any given time while also minimizing costs.

Chemical Supply Chain - Current State

Stakeholders working in the chemicals can be divided into three sectors: chemical distribution, chemical manufacturing, and chemical engineering.

Chemical distributors are intermediate traders that purchase chemical products from chemical manufacturers or other chemical distributors (breaking bulk) to fill orders for consumers or customers such as retailers, wholesalers, governments, etc. They profit by the difference between what they spend buying a product and selling it at retail price.

Chemical engineers are chemical experts that provide chemical solutions to real-life problems. They have a chemical background and work with chemical professionals, engineers, scientists, etc., in various fields for the development of new chemicals or improvement/optimization of existing ones, including upgrading production processes, reducing environmental impact, or developing more environmentally friendly products.

Chemical manufacturers are responsible for manufacturing chemical products. They utilize raw materials (such as chemical compounds, natural materials, or biological-based substances) to produce chemical products using a chemical reaction process. Chemical manufacturers can be divided into producers of various chemical types.

So, how the supply chain for chemicals currently works? The existing supply chain combines chemical manufacturers, producers, and distributors to fulfill product needs. The process starts with the raw materials that chemical companies purchase from suppliers and converts them into chemical products. These chemical products are then distributed to distributors who sell the chemicals to customers in various industries. Chemical manufacturers struggle to manage their complex supply chains that involve dealing with hazardous and perishable ingredients. The chemical manufacturing process is prone to variations in product characteristics (like potency, color, composition, etc.), varying from batch to batch. These variations mean they have a higher risk of providing compounds with the wrong properties to customers, compromising business outcomes regarding safety, quality, and profitability. Adjusting formulas based on available ingredients and ensuring finished products meet the exacting tolerances of customers and regulating agencies requires flexibility and superior planning capabilities.

Challenges of the Chemical Supply Chain and how AI can help

Today, the chemical industry is looking good on the surface. There is an uptick in earnings and revenues. But underneath, hidden pressures are mounting. Razor-thin margins, SKU-proliferation, globalization, long lead times to increase capacity, and products tend to be heavy and expensive to move. You start to understand the complexities involved with running an efficient process supply chain for chemicals. What are the major supply chain challenges in the chemical industry and how can they be approached with advanced artificial intelligence technologies? What are the current trends in the supply chain of the chemical industry? Where AI solutions fit best and bring the biggest value?

Lack of Visibility

One of the primary challenges suppliers of chemicals face is a lack of visibility into their supply chain. Knowing where inventory stands and how it’s being used can be challenging if chemical companies only look at internal data. In this case, predictive analytics can also be useful.

It’s important that chemical producers have an accurate picture of what they have on hand when it runs out and whether or not there are opportunities to make substitutions to reduce costs without sacrificing quality.

This information enables them to better plan for production schedules - decreasing inventories as needed while still meeting customer demand; increasing safety by reducing risk exposure (i.e., a higher likelihood of running out); assuring product consistency with less variability between batches; optimizing performance across all levels of the supply chain through intelligent customization of chemical needs, and making informed decisions to address market changes.

AI systems can provide the chemical industry with a more accurate assessment of their inventory levels and what is needed to meet customer demand. It can also make better predictions about future production needs, which will help chemical suppliers maintain the appropriate level of safety stock and reduce risk exposure.

AI systems can automatically generate reports and insights on raw material usage and finished goods in downstream chemical processes based on historical data so that chemical supply chains can anticipate shortages or excesses if they arise and react accordingly; avoid overstocking (wasted money); optimize performance across all levels by making intelligent decisions when market changes necessitate new formulas for customers’ products; assure product consistency through superior planning capabilities; adjust formulas based on available ingredients without compromising business outcomes like quality, safety, profitability.

Planning Production

Manufacturing chemicals is a complex process with many steps, and chemical suppliers need to plan production carefully. They have to know what they’ll use in their recipes - the chemicals that will be used as starting materials, reactants, or solvents; which chemical products will be needed downstream for manufacturing processes; where these chemical products are located throughout the supply chain (i.e., how far along is each product); how much of those chemical products should go into this particular plant, etc.; What resources are available?

To manage all this complexity efficiently, chemical producers rely heavily on information technology (IT) systems like enterprise resource planning (ERP). Still, it’s difficult for ERPs to keep up with the often rapidly changing state of affairs in chemical manufacturing without AI enhancements.

AI-enhanced chemical supply chain management with advanced analytics will allow chemical suppliers to optimize planning production schedules, make informed decisions to address market changes and provide products that meet customer demand while optimizing performance across all levels of the supply chain through intelligent customization of chemical needs.

Related case study: Active Ingredients Production Process Predictive MonitoringTo improve the current repeatable production processes, a producer of active pharmaceutical ingredients approached us to implement AI models and utilize predictive modeling.Our challenge? Building a system that analyzes real-time data streams from the production process and identifies potential outliers that may lead to deterioration of quality, based on historical data. The benefits are improved effectiveness, predictability, and efficiency of production processes.

Quality Assurance Standards

The chemical industry needs a system for managing quality assurance standards that increase their chances of providing compounds with the correct properties on time and in full quantity.

The problem is establishing an accurate understanding of what constitutes “good.” To do this, it’s necessary to consider product characteristics such as potency (or concentration), color composition, etc. Still, there are variations from batch to batch due to both external factors like raw materials or plant equipment malfunctions. Chemical manufacturers are using a variety of different processes to produce products which further complicates things.

AI-enhanced chemical supply chain management will enable chemical companies to accurately establish quality assurance standards by considering external and internal factors, enabling them to provide compounds with the correct properties on time and in full quantity, while also making informed decisions about product compositions whenever necessary.

For some materials, image recognition and analysis systems are used on the production line to evaluate the chemical composition and quality. In anomaly detection systems, AI will detect various types of events or patterns - such as equipment failure - at an early stage to prevent any issues from occurring. This will enable chemical suppliers to maintain a high level of customer satisfaction while at the same time meeting their own internal quality standards.

For other materials, chemical companies use analytical instruments to measure chemical properties. AI-enhanced chemical supply chain management will allow chemical industry organizations to make decisions that are informed by both data directly measured from the process and any circumstantial evidence related to external factors like raw material or plant equipment malfunctions.

In chemical supply chains with more than one chemical company working together on a particular project- or in an industry with chemical plants of different sizes within the same area - standardization is important to produce the same quality end product.

This means many chemical companies may need to collaborate when employing artificial intelligence as part of their chemical value chain management strategy; if two sites produce similar products but have large variations in production quantity, they’ll be able to learn from one another’s successes and failures by analyzing data sets through deep learning algorithms.

Coordinating Material Delivery

Material delivery coordination is another complex task for chemical supply chains since most compounds require multiple ingredients - such as reactants and solvents and starting chemicals or downstream products - manufactured at different times using differing technologies. Chemical manufacturers have traditionally relied on ERPs to manage this complexity. Still, these systems don’t always keep up with chemical production and manufacturing, which is often rapidly changing in chemical supply chains.

AI-enhanced chemical SCM will enable chemical manufacturers to optimize materials delivery by coordinating their deliveries based on a dynamically updated understanding of available resources and resource usage throughout the process.

This type of coordination not only optimizes material delivery but also raises quality assurance standards for chemical products since it prevents interruptions or delays that could compromise safety, performance, or yield. It can also reduce costs by optimizing last-mile transportation efforts through predictive logistics systems and fleet optimization software. These systems use AI algorithms combined with real-time data from IoT devices installed at all points of the production site to track the process, optimize actions and reduce the need for the excess waste.

Managing Inventory for Cost Efficiency

Running an efficient supply chain is contingent on maintaining accurate inventory levels - not too much or not too little. Chemical producers need just enough raw materials and intermediate (inactive) chemicals within a chemical plant before further chemical production can occur. Too much chemical inventory can lead to chemical waste or spoilage, too little chemical inventory, and the company will not meet customer demand for their product.

Unmanaged inventories are costly, but they also create capital restraints that inhibit growth opportunities and increase production time delays, decreasing profitability. Inventory management within a complex supply chain with varying products requires flexibility, scalability, agility, and responsiveness to ensure efficient processes while still meeting safety standards and environmental regulations.

Warehousing needs should be optimized for chemical production to avoid excess inventory, which is often costly for chemical companies. AI-enhanced chemical value chain management utilizes machine learning algorithms that incorporate near real-time data from production lines to determine the optimal amount of chemical raw material or intermediate chemicals needed at any given point in the process.

Inventory optimization for both finished products and raw materials can be done with far more precision when there’s an understanding of what inventory should look like at any given point in time throughout a complex network of suppliers who provide goods to multiple customers across different markets regions around the world. AI’s ability to rapidly share information between all levels of a chemical manufacturer’s enterprise allows them to make better decisions about their production schedules by coordinating chemical production with customer demand.

From Reactive to Proactive Chemical Supply Chain Management

Chemical companies may also take advantage of AI-powered predictive analytics systems that help them better understand their production schedules, customer demand patterns, pricing trends, or other factors they can’t always monitor themselves. This type of chemical supply chain management is a proactive approach; it anticipates potential problems before they arise and can develop strategies for the chemical company.

AI-driven chemical production optimization software, which may also use neural networks or machine learning algorithms, adjusts in real-time when data from IoT devices on plant floors indicates that a system might not be running as efficiently as anticipated - whether because of equipment failure or technical difficulties with suppliers. Once AI systems have analyzed this information, necessary adjustments are made to increase cost efficiency within chemical production so manufacturers don’t have to spend unnecessary capital on new assets later down the line.

Load Optimization

Optimizing truck loadings is another chemical supply chain challenge. The chemical industry often relies on a just-in-time (JIT) inventory system, making load optimization difficult, if not impossible. Chemical manufacturers have to consider the number of products required for each customer and the weight restrictions and ADRs sometimes placed by shipping carriers when determining how best to fill a truck with their product orders. Bulk shipments should be planned whenever possible to reduce shipping costs and increase chemical efficiency.

AI can work to manage inventory and optimize loadings for shipments. AI can find new insight that will enable chemical supply chains to optimize chemical production and logistics.

Route Optimization

With chemical supply chains rapidly changing due to globalization and technology improvements, there is a need for route optimization software that can optimize routes and help chemical suppliers anticipate future demand patterns based on past trends, so they’ll be ready before customers actually place their orders. Whether the logistics involve ships, trains, planes, or trucks, route selection and careful planning are key to chemical supply chains.

AI-driven route optimization software can create a shipment plan based on available routes, cost, and timing constraints to help chemical suppliers cut down on transportation costs and increase their efficiency.

Contract Optimization

Chemical companies also face challenges in contract negotiation with customers - whether they’re wholesalers or chemical plants.

AI-driven contract optimization software may help chemical companies better negotiate with customers. It can quickly analyze a customer’s purchasing patterns and offer the best possible price for chemical products. This type of AI contract negotiation is also proactive because it allows chemical suppliers to make changes when necessary based on what would produce the most favorable outcome - whether lowering prices or increasing production flexibility to remain competitive within a changing marketplace.

Increased Environmental Regulations

Environmental regulations are a challenge the chemical industry faces. As manufacturers and consumers, chemical companies must comply with environmental rules that govern how they produce their products and dispose of those products when they are no longer needed. It is estimated that chemical industries will need to spend more than $300 billion over the next few years for pollution prevention equipment and compliance costs due to this regulation alone.

AI will play a key role in chemical supply chains as chemical organizations work to meet environmental regulations and reduce their carbon footprint. AI can be used to optimize the production process, from minimizing emissions during chemical manufacturing and transportation of products all the way through disposal when they are no longer needed.

Chemical manufacturers have been able to use AI for predictive analytics that helps them identify potential issues before they arise - whether it’s an equipment failure or a need for more products because of increased demand. This proactive approach has helped chemical producers avoid waste, increase efficiency and decrease costs while meeting environmental compliance requirements at the same time.

Furthermore, with so many different chemicals being produced by chemical suppliers today, there is always some risk involved, leading to expensive fines or other penalties. AI can help chemical suppliers avoid risks by anticipating potential compliance issues before they actually happen, saving chemical companies a lot of time and money in the long run.

Supporting Circular Economy

Chemical companies can help reduce chemical waste by using AI to increase recycling efforts, which is an aspect of the circular economy. As a result, chemical suppliers can recover more raw materials from chemical products before they get thrown away or go into landfills and minimize their carbon footprint and environmental impact.

AI solutions are helping in recycling chemical products by analyzing chemical recipes and identifying areas where chemical recycling can be a cost-effective solution. AI also helps chemical companies identify opportunities to use recycled chemicals as raw materials when possible, which not only reduces environmental impact but saves them money too in the long run.

The Future of the Chemical Supply Chain

What will the future look like? What should be the long-term vision and high-level focus of the chemical supply chain?

To provide a feasible chemical system in the future, chemical companies need to consider how they can better manage their resources, reduce wastage, and address market and customer needs. It should pivot towards a more sustainable circular economy that focuses on creating value for customers via operational change:

• Driving a stream of innovation into the market.
• Working with customers to improve products and tailoring to particular needs and possible applications.
• Providing a portfolio of products and services to the end market.
• Supplying products to a broader set of markets or customers.

Benefits of Artificial Intelligence for Chemical Companies and Supply Chains

• AI can anticipate potential risks and make changes before they happen.
• AI helps chemical companies reduce environmental impact.
• Chemical companies can optimize production processes through AI.
• AI helps the chemical industry identify opportunities to use recycled chemicals as raw materials.
• Chemical manufacturers can increase efficiency through AI and predictive analytics.
• Chemical suppliers can reduce risks by utilizing AI for contract negotiation software that analyzes a customer’s purchasing patterns and offers the best possible price for chemical products when it comes time to negotiate with them.
• AI forecasting of compliance issues allows chemical producers to avoid fines or other penalties associated with not meeting environmental regulations on their product disposal process, production process, or during transportation of chemical products from one place to another.

Conclusions

The chemical industry faces many challenges that have been difficult to overcome with their current processes struggling to move further with their digital transformation. However, with AI’s help, there is room for improvement. Chemical businesses can optimize production processes, increase efficiency, reduce risks for potential environmental violations or penalties, identify opportunities to use recycled chemicals as raw materials when possible, and more. If you want to learn more about how your company could benefit from AI in its chemical supply chain process, schedule a consultation today!

References

Catalyzing the New - Accenture

Chemical Industry Worldwide - Statista

How chemical players can win in the transition to digital platforms - McKinsey & Company