If the headline of this article seems like gobbledygook, let me decode it for you. It means: You can make money using artificial intelligence to help meet environmental goals across your global supply chain, from supplier to shop floor.

Everyone wants to help the environment and cut costs. Both can be accomplished by addressing environmental factors in a supply chain optimization model across planning and scheduling processes using AI. 

Environmental issues are one aspect of environmental, social and governance (ESG). The most common environmental objectives for ESG programs are reducing energy and water consumption, decreasing CO₂ emissions, and lowering a company’s overall carbon footprint. 

Companies typically establish environmental goals that align with one of many standards created by science-based targets, ISO 14001 and 14064, and the Task Force on Climate-Related Financial Disclosures. These greenhouse gas initiatives are derived from the Paris Agreement, which aims to achieve net-zero CO₂ supply chain emissions by 2050 to limit global temperature increase to under 2°C. Businesses that implement ESG programs can improve profits through cost-cutting and waste reduction while maintaining service levels — a win-win.

To run their ESG initiatives, manufacturers, distributors and retailers typically hire a chief purchasing officer, chief sustainability officer, or chief ESG officer. This highly visible role prioritizes process efficiencies and global cost reductions via reporting. Monthly reviews of pertinent key performance indicators include total amount of CO₂ used, total energy used by fuel type, total water consumed, and other climate-related metrics. 

However, reporting is not the end of the story. Automation and optimization incorporating AI can be critical to balancing a company’s environmental goals with corporate financial objectives.

How AI Benefits Planning

Given all the manual challenges, the use of AI to evaluate all the various inputs and determine the most profitable plan and shop floor schedule is critical. An effective supply plan starts with machine learning, which allows forecasters to predict customer demand, taking into account events, promotions, seasonality, attribute and product trends and much more. 

The demand plan drives supply plan generation. Supply planning should employ an AI model to automatically create a holistic time-phased, constraint-based plan for each SKU, location, resource group, day or week. This solver should extend beyond the typical inputs — like safety stock strategy, service level, days of cover, min and max — to further include costs of transportation by route and mode, cost of production, cost of storage in warehouse, cost and margin of products, storage capacity, vehicle capacity, resource groups, maintenance, labor availability, lead times, shelf life and more. 

An AI-based model allows companies to optimize the plan further, by considering the usage of electricity, water and natural gas, by transportation routes and modes.

Say that your company has invested in electric vehicles alongside gas vehicles for some routes between locations or suppliers. The solver would profitably balance the impact on environment while minimizing cost to deliver, all automatically.

AI can also help companies incorporate their supplier network’s impact on the environment. Each supplier’s carbon emissions and energy and water usage could be inputted into the AI model, allowing the solver to source a product or raw material based on how it balances cost and environmental impact.

While AI will automatically solve for many inputs, planners may prefer to test and review the effect of different scenarios on cost, profit, and carbon footprint to make strategic decisions. Planners can then prioritize and penalize the various levers for the AI model to automatically consider when building a new supply plan.

Extending AI to shop floor scheduling will draw out even more value by reducing the impact on the environment and making the best use of machines, people and materials. By leveraging optimization in production scheduling and sequencing, more efficient machines and resources with less energy consumption can have production orders assigned first. The AI model consolidates rules around sequencing (such as allergens versus non, organic versus non, light to dark and other attribute interactions), as well as changeover, clean in place, capacity of resources (such as tanks, inflow and outflow rates), downtime, and labor availability.

Incorporating ESG Into Production Scheduling

When it comes to complying with ESG standards, energy and CO₂ emissions should be inputs into the scheduling solver. Inherent in the optimizer are the production decisions of longer versus shorter batch runs, resulting in less wastage, inventory, storage costs and byproducts, as well as more efficient use of machines, people and materials. When more sustainable formulas are available, solvers can evaluate resource and material availability to make positive impacts on production. With millions of factors to consider, from shop floor production to emissions, humans can be overwhelmed, which is where AI brings efficiency thus profits.

An AI-powered approach impacts the profitability and environment by:

• Minimizing inventory through optimizing order frequency, transfer of goods between facilities, manufacturing and raw material usage, and supplier orders. 
• Reducing costs through optimizing transportation modes and routes; inventory storage; resource efficiency; utilities related to energy consumption and carbon emissions; and setup, clean-in-place and downtimes related to resources.
• Reducing wastage through optimizing timing of orders, inventory levels, batch sequencing and incorporation of shelf life.
• Increasing efficiency of people through optimizing and automating millions of combinations of variables, management by exception and elimination of manual tasks.

AI can drive profits, help achieve environmental goals, and assist in reporting and transparency. Every industry can profit. People become more efficient through automation using AI by allowing unlimited factors to be evaluated with most profitable outcomes created. Invest in AI today, to drive a “go green” mission that delivers dollars to your net income.

Michael Dale is senior principal solution consultant with Infor.