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Supply Chain AI

Artificial Intelligence in the supply chain - are we there yet?

We are noticing an increasing level of excitement about the possible applications of artificial intelligence (AI) in supply chain, along with some scepticism and lack of enthusiasm from the traditionalists. AI definitely has the potential to enrich our everyday business activities, but how far have we got? Some global organizations are trying out various solutions but the results have been generally disappointing so far.

What is AI in the supply chain?

AI is intelligence exhibited by machines, or when machines mimic or can replace intelligent human behaviour, such as problem-solving or learning.

  1. Automating processes and actions so they can operate without the need for human intervention
  2. Assisting the human decision-making process in day-to-day operations by reducing errors and identifying bias, especially in data analysis<

Supply chain planning

ML can provide the best possible demand scenarios based on intelligent algorithms and machine-to-machine analysis of big data sets, using work tools that run in a continuous loop. This kind of capability could optimize the delivery of goods while balancing supply and demand, and wouldn’t require human analysis except for the setting of parameters.

AI challenges in supply chain

AI adoption faces many challenges. It requires major capital investments, updating of I.T systems and making organizational changes. As a result, only the largest players can afford this. Organizations with aged legacy systems face many other substantial obstacles to deploying and reaping the benefits of AI.

Despite its potential for adding value, there are already concerns that AI may replace routine and manual tasks resulting in job losses. Companies still need to develop strategies to address how workers’ roles will change as AI systems automate some manual functions. In addition, there are also security and safety concerns for IT infrastructure and human life.

What is attractive about AI-based solutions is that they learn and will drive continuous improvement over time. We just need to manage the human interface.

Here are 4 examples of AI and how it’s changing supply chain management for the better.

Autonomous transport

There’s nothing more exciting than the field of autonomous transport for SCM. We’ve all known for many years that driverless trucks have major potential to affect supply chain management and logisitics.

We aren’t there yet – and “anyone employed as a driver today will be able to retire as a driver” — but if autonomous trucking can be developed to its potential, the technology would effectively double the output of the U.S. transportation network at 25 percent of the cost.

The conversation is no longer simply talking about vehicles on the road either. Google and Rolls-Royce recently partnered to build autonomous ships too.

Final-mile delivery route efficiency

One doesn’t have to have a driverless vehicle, however, to use AI to optimize delivery logistics.

For example, in the “devilishly complex” task of delivering 25 packages by van, the number of possible routes adds up to around 15 septillion (that’s a trillion trillion).

That’s where route optimization software and AI-powered GPS tools like ORION — which UPS uses to create the most efficient routes for its fleet — are making their mark. With ORION, customers, drivers and vehicles submit data to the machine, which then uses algorithms to creates the most up-to-date optimal routes depending on road conditions and other factors

Demand forecasting, particularly for warehouse management and SCM strategy

Machine learning has the ability to quickly discover patterns in supply chain data by relying on algorithms and constraint-based modeling to find the most influential factors. This ability to find data patterns without human intervention has applications in EVERY aspect of SCM, but demand forecasting is a particularly influential component beneficiary.

Warehouse management and SCM strategy rely heavily on correct supply, demand, and inventory-based management. Forecasting engines with machine learning offer an entirely new level of intelligence and predictive analysis of big data sets that provides an endless (and constantly self-improving) loop of forecasting, overhauling the way we manage inventory and the way we create new strategies for our industries.

Chatbots for marketing and operational procurement

The increasing popularity of chatbots is making it harder to ignore how AI is helping shape not just the daily logistics but also the B2B marketing landscape and operational procurement for supply chain industries.

A chatbot is a computer program that simulates human conversation using auditory or textual methods. It communicates with your customer inside a messaging app, like Facebook Messenger, and is similar to email marketing without landing in an inbox. Mimicking a human conversation, chatbots currently allow for increased customer engagement through messaging app technology that isn’t yet saturated with marketing. They are just one of the many ways to integrate AI and marketing.

There’s so much more than these 4 examples to consider when discussing AI and the supply chain: prediction of delivery arrival times to the warehouse and to the customer, cargo sensors, automated purchasing, financial applications…the list literally may be endless.