How pooling trade data will allow businesses to build an intelligent, sustainable supply chain

When it comes to our environmental impact, it’s likely we’ll look back on the 2010s — a decade in which consumption and waste reached new heights, despite an increased awareness of what’s at stake — with an acute sense of missed opportunity. Today, sustainability has become a major part of the political and social agenda, and businesses have a significant role to play in rectifying the situation.

For example, the UN General Assembly published a report in late 2015 entitled “Transforming our world: the 2030 Agenda for Sustainable Development”. If these sustainable development goals (and the expectations of an increasingly eco-conscious public) are to be met, organisations need to look at ways of reducing both waste and their carbon footprint. One thing that’s clear is that the supply chain has room for improvement.

New technologies have emerged that will allow supply chain businesses to rein in their contribution to the climate crisis. By effectively analysing real-time data using AI and advanced analytics and building automation into the supply chain, businesses can increase energy efficiencies and achieve a dramatic reduction in waste.

So, what are the barriers to creating an environmentally and economically sustainable supply chain, and how can businesses utilise new analytical techniques to overcome them?

Waste and excess

Inventory mismanagement and erroneous forecasting are among the most typical causes of waste in the supply chain, because they can result in the overproduction and over-purchasing of materials. It’s estimated that between 5–7% of total annual waste stems from this factor alone, according to WRAP.

This failing originates from an inability to properly align supply with demand, which is damaging to the bottom line, as well as the environment. In fact, according to Hackett Group’s Working Capital Survey, $443 billion worth of excess inventory is stranded in supply chains as a result of these inefficiencies.

The extent of the waste problem is best illustrated by the food supply chain, which is characterised by an abominable amount of waste. Currently, roughly one third of the food produced for human consumption is wasted each year, which equates to approximately 1.3 billion tonnes. It is thought that improving supply chain efficiency could cut food waste by $700bn globally, while a report from Champions 12.3 found that over half of businesses that invested in reducing food waste earned a 14-fold or greater return on their investment.

It’s clear that this volume of wastage is unacceptable. The additional business opportunity attached to minimising food waste means that all parties should be incentivised to bring about the necessary changes.

Unnecessary emissions

The unnecessary transport of goods and poorly optimised routing are also significant barriers to building a green supply chain. Any logistics activity that results in the needless transport of inventory increases the carbon footprint of the supply chain without cause.

According to a report from the Environmental Defense Fund, a typical long-haul truck drives empty for more than 14,000 miles each year, consuming 2,400 gallons of diesel and emitting more than 26.4 tons of carbon dioxide. This highlights a clear opportunity: minimising unnecessary miles travelled, whether holding cargo or not, could result in a significant reduction in unnecessary emissions.

More intelligent, AI-driven supply chain management can make processes more efficient by optimising total tons-per-mile performance. The ability to synthesise data from across the entire supply chain can help to reduce total trips, as well as the proportion of miles driven without cargo, reducing greenhouse gas emissions, improving air quality and relieving congestion.

The technological solution

The issues with today’s supply chain are there for all to see. So, what steps can businesses take to bolster their environmental and economic sustainability?

One route to remedying the emissions and waste caused by poor optimisation can be found in the application of big data analytics, a form of analytics that enables supply chain stakeholders to uncover patterns and correlations concealed within vast datasets.

By applying powerful statistical methods to data that is created across global supply chains, businesses can better align supply with demand. Businesses will gain fresh insight into customer buying behaviours, which will tell them which products should populate retail shelves, and which stock keeping units (SKUs) may no longer be needed, or needed in the same volume.

AI can also be deployed to optimise the transport of inventory, and eliminate unnecessary miles travelled. Driving behaviours and route choices can be assessed and dynamically optimised to both drive picking productivity, and reduce emissions attached to the unnecessary movement of cargo and inventory.

In order to achieve the greatest gains, stakeholders will need to pool their resources and information to create a b2b data cooperative. By collecting information from across data silos and disparate organisations, and applying advanced analytical techniques to the resulting datasets, participating stakeholders can gain access to a treasure trove of insights that will help them operate in a more efficient and sustainable manner.

The gravity of the climate crisis means supply chain stakeholders must take immediate steps to put a stop to unsustainable practices. First and foremost, this means cutting down on waste and unnecessary emissions.

If businesses are going to hit the sustainable development targets set by the UN General Assembly and other leading environmental bodies, they must turn to machine learning and advanced analytics to better align supply with demand and eliminate unnecessary emissions. The commitment of supply chain businesses to becoming sustainable will have a galvanising effect, propelling us into a future in which our toll on the environment is unrecognisable in comparison with today.