Technology has become the most crucial catalyst in bringing about supply chain transformation and optimizing its future performance better than ever before. Artificial Intelligence (AI) simulates human intelligence in the most accurate way possible.

It collects data, analyzes information, and provides cognitive decisions to enhance the overall supply chain performance. Machine Learning (ML) is a subset of AI and is very specific to how a machine performs on the basis of past experiences. AI and ML together come as a great help when it comes to optimizing logistics and supply chain processes.

NEED FOR TECHNOLOGY IN CHEMICAL SECTOR

When it comes to the chemical sector, the logistics and supply chain is particularly different from other industries’ processes. This is mainly because of three reasons:

(i)Production and consumption of the chemical products are mostly at distant and separate locations. Hence, manufacturing, storage, transit, and distribution need to be aligned well to ensure the chemicals hold their composition throughout.

(ii) 96% of manufacturing goods depend on chemicals for direct or indirect usage. A slight delay in the unavailability of products can affect the operations of other major industries. Hence, the industry needs to work round the clock to meet the market requirements.

(iii) The product if not handled properly can itself be hazardous in nature and can impact humans, machinery, and other assets involved in the management of the products. So for obvious reasons, one needs to be extra cautious while working with chemical products.

In order to bring in logistics efficiency, technology should be targeted and implemented across people, places, and processes over the entire chemical supply chain.

SUPPLY CHAIN REVOLUTION IN CHEMICAL INDUSTRY

The nature of the chemical industry products and by-products demands highly efficient solutions to manage the entire supply chain. Safety, quality, and reliability of people and processes need to be kept in mind throughout the logistics and supply chain journey. Starting from the manufacturing operation, the target of technology is to predict the demand and produce the right quantity of products at the right time. AI-enabled supply chain along with machine learning algorithms then capture real-time data along with information from the production lines to ensure that a balance of demand and supply is achieved.

Machine learning drives process automation for manufacturing the required chemical composition with 100% accuracy. Petro[1]chemicals and refineries in particular have specific composition requirements and they thus need ML support to operate and manage the production of the required products.

For e.g. BASF, a German producer of paints, recently adapted machine learning technology to interpret customer requirements and give inputs to manufacturing units to prepare the paints in the composition required by the clients.

 When it comes to chemical transportation, the entire movement of products needs to be planned carefully. Some of the companies in the sector may even need a cold chain network to manage the storage and freight of the products. Temperature monitoring solutions along with highly advanced GPS telematics software provide real-time information on products and vehicles in transit.

Nowadays a variety of devices are available in the market with both wired and portable setups that can be used for own as well as market vehicles to ensure complete transportation visibility.

 Safety of the drivers and vehicles is another important factor when considering tracking solutions. AI and ML-based solutions continuously gather data and anticipate potential risks even before the incidents hap[1]pen. Few other features that cater to safety are SOS button installation, instant alerts and communication, night and continuous driving alerts for drivers, maps enabled with nearby safety services, and many more. AI-led supply chain optimisation software is the best solution when it comes to route and load management of vehicles.

Managing the distribution of chemicals involves strong planning of packaging, loading, and even optimising the routes for faster and timely deliveries. All these technologies when implemented well can definitely con[1]tribute to reduced logistics costs and better operating efficiencies of the supply chains.

 Another important area that needs technological assistance in the chemical industry is inventory management. Unmanaged inventory is costly and in this case, even hazardous to nature if not disposed of properly.

So, both higher and lower production gets difficult to manage physically and financially. Almost all companies have individual ERPs and WMS software that work to maintain optimum inventory levels, what is crucial here is the successful software integration of all the management solutions on one single platform to handle product schedules from predicting production to achieving deliveries.

AI AND ML FOR CHEMICAL SUPPLY CHAINS

The role of technology doesn't end here with process automation in chemical supply chains. AI and ML driven software continuously stores data of all products and processes, conducts in-depth analysis on process performance and provides intelligent actionable insights to improvise overall supply chain performance. Along with operational efficiencies, these solutions can also help maintain compliance regulations throughout chemical logistics. Such solutions provide access to real-time data, dashboards and instant reports to monitor and manage logistics and supply chain operations. By adopting cloud technologies, companies can work on the existing data while capturing new information, thus saving time to create complex compositions of chemicals. Control tower solutions for chemical industries can thus be great enablers in saving logistics costs and increasing the overall supply chain productivity. Hence, all the key focus areas of the chemical supply chain - efficient production models, lower transportation costs, and greater safety and reliability, can be undeniably achieved with the help of AI-enabled solutions.