As supply chains tighten, logistics must optimize with AI

We’ve all felt the tightening of supply chains in recent months. From skyrocketing fuel prices to supply shortages not meeting pent-up demand, the world is still trying to adjust to the new normal. Unfortunately, many companies in different parts of the global supply chain are failing to keep up the pace, especially as e-commerce continues to grow at historic figures.

With this in mind, it’s unsurprising that many logistics companies are turning to technology to achieve much-needed optimization. Artificial intelligence (AI) is quickly making its way into every supply chain logistics link – from demand forecast to robot delivery and route optimization in the last mile – to meet today’s buyer demands and delivery expectations. In fact, the global logistics automation market has the highest compound annual growth rate of the entire supply chain with current projections at more than 12%. 

[aditude-amp id="flyingcarpet" targeting='{"env":"staging","page_type":"article","post_id":2784333,"post_type":"community","post_chan":"none","tags":"category-business-industrial-business-operations-management,category-business-industrial-transportation-logistics,category-computers-electronics","ai":true,"category":"none","all_categories":"ai,business,datadecisionmakers,","session":"D"}']

It must be said, though, that technology alone is no silver bullet. Rather, companies in logistics must first identify where the technology needs to be used and spot its bias before executing a decision. So, how can companies ensure their AI technology improves the fundamentals and doesn’t replicate human bias? Let’s explore.

Start with a tech-agnostic business analysis 

There’s no one-size-fits-all when it comes to logistics. A furniture retailer might need to improve their asset or vehicle utilization, while a food retailer would benefit from improved demand prediction, visibility and shorter transit times. Given these varying needs, the first step in building a modern, highly effective logistics network is to take a step back and analyze what optimization technology is needed and where. 

Two considerations are key here:

Understand your core business: Before jumping the gun, identify your bottlenecks, understand the delivery systems available and discover the root cause of the congestion. Factors to analyze are the capacity of your shipping mediums, your warehouse management, average delivery time and the accuracy of your demand predictions. Only by understanding your current capabilities and inefficiencies will you be able to deploy the appropriate technology.

Build your systems in an orderly manner: Build out your technology step by step. This is vital since some companies assume that adding multiple solutions and automating everything at once will reap the best results. This is not the case. You won’t be doing yourself any favors by simultaneously deploying several different solutions to different problems along the logistics journey, risking siloed systems, or repeating mistakes made earlier. Throughout a step-by-step process, you can easily root out whether errors have been caused by legacy systems or human input.

Embed KPIs in your strategy

Once your goals are set, it’s important to establish your key performance indicators (KPIs). Examples include the number of deliveries, inventory costs, transportation costs and average delivery times. These KPIs are fundamental to the use of AI – they help define the expected outcomes when we use and train the models to improve a supply chain process and logistics.

The performance metrics must include the related datasets that the machine learning models will analyze so that the data points can be meaningfully linked. Let’s say one of your goals is shortening last-mile delivery times. Approximately 50% of delivery costs for consumers and businesses are incurred in the last mile – presenting by far the most complex challenge for optimization. In this case, AI projects must connect various datasets: distances between multiple delivery locations, delivery time windows, vehicle capacity, individual customers preferences, traffic, and so on. Technology can then give the driver the best possible route to take every time – considering it first has access to all datasets that affect last-mile delivery time.

Overall, applying AI analytics to problems will help you optimize elements like your optimal warehouse capacity, transportation utilization and delivery times. At some point, however, business leaders have to choose between tradeoffs. Is the main goal to keep costs low or to increase delivery speed? Are long transport distances to be avoided due to emissions? While AI can show which alternatives are more cost-effective or climate-friendly, companies will have to make the ultimate decision about their business trajectory.

[aditude-amp id="medium1" targeting='{"env":"staging","page_type":"article","post_id":2784333,"post_type":"community","post_chan":"none","tags":"category-business-industrial-business-operations-management,category-business-industrial-transportation-logistics,category-computers-electronics","ai":true,"category":"none","all_categories":"ai,business,datadecisionmakers,","session":"D"}']

Build a customer-centric business model

The ultimate goal for logistics companies should be to create a positive end-to-end experience for the customer. Logistics are now part of the brand experience for ecommerce customers and fast, reliable deliveries have become more important to a good shopping experience than cheap products. Automating tasks such as invoicing and notifying warehouse staff can reduce inefficiencies, and predicting demand or potential disruptions helps reduce the likelihood of disappointing a customer. 

But, sometimes, even the best prediction fails. That’s why companies need to build trust with their customers. From sending real-time messages about order status to improving personal customer service, transparency is key. So, when adopting a customer-centric vision, it’s vital to build your technology around it. 

This also requires the ability to learn from machine learning projects and be flexible in your overall approach. Figuring out which factors are driving the most significant changes in KPIs and adjusting machine learning over time with new training and evolved KPIs can yield even greater benefits. It will take some trial and error, but logistics companies need to be flexible in this process and carefully chart what works and what doesn’t – and thus what they can and can’t deliver. 

The importance of human input

It must be said that a technology-enabled supply chain generates plenty of information and insight – but it’s only useful if the team behind it can adequately interpret the data and act. Therefore, as with any technological overhaul of business processes, one should never lose sight of the human element. 

[aditude-amp id="medium2" targeting='{"env":"staging","page_type":"article","post_id":2784333,"post_type":"community","post_chan":"none","tags":"category-business-industrial-business-operations-management,category-business-industrial-transportation-logistics,category-computers-electronics","ai":true,"category":"none","all_categories":"ai,business,datadecisionmakers,","session":"D"}']

For a business to be truly successful, it needs to blend AI with the necessary critical thinking skills that come from people. AI can analyze millions of data points to draw meaningful conclusions. Still, the human element can take these data points to understand the big picture, set definable goals and check for recurring errors. 

Together, man and machine must work in sync to optimize global supply chains like never before.

Ivan Ariza is CEO and cofounder of Cargamos.