As supply chains tighten, logistics must be improved with artificial intelligence

We’ve all been feeling the tightening of supply chains in recent months. from The skyrocketing fuel price For lack of supplies do not meet pent-up demandThe 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 pace, especially as e-commerce continues to grow. Grow in historical figures.

With this in mind, it is not surprising that many logistics companies are turning to technology for much-needed improvement. Artificial Intelligence (AI) is fast make its way At every logistics link of the supply chain – from demand forecasting to robot delivery and last-mile path optimization – to meet buyer demands and delivery expectations today. In fact, the global logistics automation market has the highest compound annual growth rate for the entire supply chain with the current forecast at More than 12%.

It must be said, however, that technology alone is not a panacea. Instead, companies working in the logistics industry must first determine where the technology should be used and identify its bias before implementing any decision. So, how can companies guarantee artificial intelligence technology Improves the basics and doesn’t replicate human bias? Let’s explore.

Start with a technology-neutral business analysis

There is no one size fits all when it comes to logistics. A furniture retailer may need to make better use of its assets or vehicles, while a food retailer will benefit from better demand forecasting, visibility and shorter transportation times. Given these varying needs, the first step in building a modern and highly efficient logistics network is to take a step back and analyze what and where the optimization technology is needed.

There are two main considerations here:

Understand your core business: Before you jump 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 media, your warehouse management, average delivery time and the accuracy of your order forecast. Only by understanding your current capabilities and inefficiencies will you be able to deploy the right technology.

Build your systems in an organized way: Build your technology step by step. This is vital since some companies assume that adding multiple solutions and automating everything at once will yield the best results. This is not the case. You will do yourself no favors by simultaneously deploying many different solutions to different problems along the logistics journey, risking lonely systems, or repeating mistakes made previously. With a step-by-step process, you can easily detect whether the errors were caused by outdated systems or human input.

Include KPIs in your strategy

Once you have defined your goals, it is important to define 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 expected outcomes when we use and train models to improve the supply chain and logistics process.

Performance measures should include the relevant data sets that the machine learning models will analyze so that the data points can be usefully linked. Let’s say one of your goals is to shorten last mile delivery times. Approximately 50% of delivery costs For consumers and businesses it gets sucked into the last mile – by far the most complex challenge to improvement. In this case, AI projects must relate different datasets: distances between multiple delivery locations, delivery time windows, vehicle capacity, individual customer preferences, traffic, etc. The technology can then give the driver the best possible route to take every time – considering that they first have access to all the data sets that affect last-mile delivery time.

Overall, applying AI analytics to problems will help you improve items such as optimum warehouse capacity, transportation utilization, and delivery times. However, at some point, business leaders have to choose between trade-offs. Is the main goal to keep costs down or increase delivery speed? Should long transportation distances be avoided due to emissions? While AI can demonstrate the most cost-effective or climate-friendly alternatives, companies will have to make the final decision about their business course.

Building a customer-centric business model

The ultimate goal of logistics companies should be to create an overall positive customer experience. Logistics is now part of the brand experience E-Commerce Customers, fast and reliable deliveries are becoming more and more important to A Good shopping experience Cheap products. Automating tasks such as invoicing and notifying warehouse personnel can reduce inefficiencies, and anticipating potential demand or disruptions helps reduce the possibility of customer disappointment.

But sometimes, even the best predictions fail. That’s why businesses need to build trust with their customers. From sending real-time messages about the status of an order to improving personalized customer service, transparency is key. Therefore, when embracing a customer-centric vision, it is essential to build your technology around it.

This also requires the ability to learn from machine learning projects and to be flexible in your overall approach. Knowing what factors drive the most significant changes in KPIs and adjusting machine learning over time through new training and evolving KPIs can lead to even greater benefits. It will take some trial and error, but logistics companies need to be flexible in the process and carefully map out what works and what doesn’t – and thus what they can and cannot provide.

The importance of human input

It must be said that a technology-enabled supply chain generates a lot of information and insights – but it’s only useful if the team behind it is able to adequately interpret the data and act on it. Therefore, as with any technological reform of business processes, no one should lose sight of the human element.

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

Human and machine must work together simultaneously to improve global supply chains like never before.

Evan Ariza is CEO and co-founder of shipments.

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