How Artificial Intelligence, Machine Learning and Proprietary Algorithms Drive Sustainability Across the Supply Chain
The past 20 months have placed the global supply chain through unprecedented large-scale disruptions. We saw the global pandemic cause border closures, lockdowns, production halts, plant closures and disruptions to businesses across the chain. On both the supply and demand sides, global air cargo was severely compromised with capacity in some trade lanes falling over 50% leading to increased use of multi-modal alternative shipping options.
More recently, we saw the 20,000 TEU Evergreen container vessel, Ever Given, run aground at Egypt’s Suez Canal, a key global shipping channel. Lloyds estimated that the incident cost 12% of global trade, holding up trade valued at over $9 billion per day. Natural disasters further disrupted an already weary supply chain. What became most evident was the need for more resilient business practices that better address and recognize the full scope of sustainability and its far-reaching impacts.
For supply chain businesses, developing sustainable business practices will require they become more resilient. They will need resilient planning to make their operations more robust and efficient. In demanding periods, this means adding more time schedule buffers and excess inventory to cover shortages created by disruptions. This, however, comes at significant costs and capital expenditure. By applying artificial intelligence (AI), machine learning and proprietary algorithms, businesses can elevate resiliency without compromising profitability. efficiency. These technologies drive enhanced decision making and more agile operations enabling companies to achieve their sustainability goals.
The business paradigm is shifting to where environmental stewardship is becoming a non-negotiable responsibility of every organization. It is essential that businesses deploy new practices that address matters ranging from greenhouse gas emissions, air quality and ecological impacts, to labor practices and employee well-being, asset management and supply chain operations, (e.g., shorter, more localized supply chains to reduce exposures and streamline logistic processes; mitigate other risks such as cyberattacks on manufacturers and logistics operators).
The 17 United National (UN) sustainable development goals (SDGs) reflects the role of businesses as essential partners for addressing SDGs and making essential investments to transform practices in order to do so.
Businesses that focus on sustainability and addressing climate change issues are also likely to attract and retain employees. Today, more than ever, employees are driving sustainability issues, advocating for their employers’ greater sustainability, and even making employment decisions based on a company’s green policies. Deloitte Global’s 2019 Millennial survey reported that Millennials and Gen Zs are more loyal to employers that tackle environmental and social issues.
The definition of sustainability goes beyond environmental issues to making businesses more viable for decades. How a company treats its employees, nurtures human relationships with employees, customers and business partners and defines leadership are all aspects of sustainability. The right culture wherein employees are given broad responsibilities and the autonomy to be creative and problem-solve empowers them and contributes to more sustainable operations. This reduces employee turnover, increases retention and builds positive brand recognition.
Among the primary areas to consider for sustainability improvements are staff and equipment planning, operational processes, CO2 emissions and fuel consumption. Using AI-driven optimization software for planning staff assignments and equipment allocation is a prime area where sustainability can be supported. For example, AI can be applied to support planners through automated optimization of staff shift and workload demands.
