As supply chains tighten, logistics must improve with AI

As supply chains tighten, logistics must improve with AI ...

In recent months, weve all felt the tightening of supply chains. From rising fuel prices to supply shortages that aren''t satisfying pent-up demand, the world is still attempting to adjust to the new normal. Unfortunately, many companies in different areas of the global supply chain are failing to maintain the pace, especially as e-commerce continues to increase at historic figures.

With this in mind, it is unsurprising that many logistics companies are turning to technology to achieve much-needed optimization. Artificial intelligence (AI) is rapidly 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 buyer demand and delivery expectations. In fact, the worldwide logistics automation market has the highest compound annual growth rate of the entire supply chain with current projections at more than 12%.

Rather, logistics must first identify where the technology must be used and identify its bias before making a decision. So, how can businesses ensure their AI technology improve the fundamentals and does not replicate human bias? Lets go.

Start with a tech-agnostic business analysis

When it comes to logistics, there is no one-size-fits-all. A furniture store might require to improve their asset or vehicle usage, while a food store might benefit from improved demand prediction, visibility, and shorter transit times. The first step in building a modern, highly efficient logistics network is to take a step back and investigate what optimization technology is required.

There are two important points to consider here:

Before you get to grips, identify your bottlenecks, understand the delivery methods available, and identify the root cause of the congestion. Factors to consider 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 limitations, you may use the appropriate technology.

Build your systems in an orderly manner: step by step. This is why some companies believe that combining several solutions and automating everything at once will deliver the best results. This is not the case. You will not be doing yourself any harm by simultaneously deploying several different solutions to different situations along the logistics journey, risking siloed systems, or repeating mistakes earlier. Throughout a step-by-step process, you may easily determine whether or not legacy systems have been caused.

Embed KPIs in your strategy

Once your goals are achieved, it is important to establish your key performance indicators (KPIs) including the number of deliveries, inventory costs, transportation costs, and average delivery times. These KPIs are fundamental to the use of AI and 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 machine learning algorithms will analyze so that the data points can be meaningfully linked. Lets say one of your goals is shortening last-mile delivery times. The majority of individual customers'' needs are addressed in this case, posing a threat to optimization. Technology can then provide the driver with the best possible route to take every time considering it first has access to all datasets that affect last-mile delivery time.

Business leaders will need to make the ultimate decision on their business trajectory as well as using AI analytics to solve problems. At some point, however, they must choose between tradeoffs. Is the main objective to keep costs low or to increase delivery speed? Are long transportation distances to be avoided due to emissions? While AI may indicate which alternatives are more cost-effective or climate-friendly, businesses will need to make the ultimate decision.

Build a customer-centric business model

Logistics companies should strive to provide a positive end-to-end experience for their customers. Logistics are now part of the brand experience for ecommerce customers, and rapid, reliable deliveries have become more important to a reliable shopping experience than cheap goods. Inefficiencies, and predicting demand or potential disruptions can reduce the likelihood of disappointment.

Even the best prediction fails. Thats why companies must build trust with their customers. From sending real-time reports about order status to improving personal customer service, transparency is key. So, when it comes to adopting a customer-centric vision, it is vital to build your technology around it.

This requires the ability to learn from machine learning projects and be flexible in your overall approach. KPIs are critical to making informed decisions and increasing machine learning over time, as well as improved training and development. It can also result in increased benefits, however, logistics firms need to be flexible in this process and carefully prioritize what works and what does.

The importance of human input

There must be a requirement that a technology-enabled supply chain generates plenty of information and insight, but it is only useful if the organization behind it can adequately interpret data and act. Therefore, as with any technological revamp of business procedures, one should never lose sight of the human element.

To be successful, a business must combine AI with the necessary critical thinking skills from individuals. Despite the fact that AI can analyze millions of data points to make clear conclusions, it must set clear objectives and check for recurring errors.

As a result of the international supply chains, man and machine must work in sync.

Ivan Ariza has been appointed CEO and cofounder of Cargamos.

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