The Future of Supply Chain is AI and Machine Learning: What do Businesses Need to Understand?

McKinsey’s research suggests AI-enabled supply chain systems reduce costs by 15%, resulting in increased revenues. How can businesses use AI and ML to make smarter supply chain systems?

Artificial intelligence (AI) and Machine Learning are being utilized to improve company operations and outcomes by seamlessly integrating into Business Functions. Currently, the most common uses are within Customer Engagement and Marketing. Customer Engagement uses chatbots to aid in customer service. AI suggests products a consumer may want and make suggestions while they are shopping online, and use that information to send personalized automated email campaigns. AI is starting to find its way into every Business Function.

According to a survey performed in early 2020 by Oxford Economics and NTT DATA of 1,000 company leaders, 96% of organizations were at minimum researching AI solutions, more than 70% had either ultimately adopted or at least tested the technology. Nearly half of those surveyed claimed they would lose consumers if they didn’t incorporate AI, and 44% indicated their company’s bottom line would take a hit.

Here’s what businesses need to know about adopting AI and machine learning for their supply chain systems.

Tools reduce costs, increase revenue

According to McKinsey’s research, AI-enabled supply chain systems reduce cost by 15% and increase service level by 65%, which results in increased revenues. High-volume shippers can save a lot of money in the supply chain by cutting inventory, reducing transportation, and reducing labor expenses. In addition, artificial intelligence (AI) increases supply chain management income in sales, forecasting, spend analytics, and optimization of the logistics networks.

There are a lot of empty miles or “deadhead” trips made by trucking companies and other freight carriers when they return to their home base with an empty trailer following the delivery of a load, and AI is being used to help reduce these trips.

A company called Doft has designed solutions to resolve empty truck return problems. With the app, truckers can arrange backhauls right away when a load is reserved, or they can take multiple orders to make a more extensive non-empty return trip. To reduce CO2 emissions, enterprises must ensure that their trucks are correctly loaded and regularly book backhauls early in the voyage.

Artificial Intelligence also uncovers other hidden patterns in historical transportation data, such as determining the best modes of freight transportation for the most efficient use of labor resources, determining the best truck loading and stopping sequences, rationalizing rates, and other process improvements.

Businesses may use Machine Learning (ML) to improve routing and prepare for weather-related disruptions using this new technology. For example, ML can help transportation management experts learn how weather patterns affected the time it took to haul loads in the past and then consider current data sets to make forecast recommendations based on those trends.

Pandemic accelerated the adoption of AI and ML

Although the COVID-19 Coronavirus Disease (COVID-19) imposed a significant strain on many industries, including the transportation industry, there was a silver lining: the potential for transformation. Due to increasing pressure from customers, companies are more willing than ever to discard inefficient legacy technologies in favor of investing in new processes and technological tools to serve their demands better.

For transportation management experts, applying AI and ML to pandemic-related concerns can differ in accelerating or halting growth. With the proper implementation, these technologies serve to boost the visibility of logistics, provide data-driven planning insights, and aid in the successful automation of processes.

Artificial intelligence (AI) and machine learning (ML) have been overhyped or misrepresented as a catch-all for industry problems, as has been the case with many other promising new technologies. When it comes to AI and ML, transportation logistics companies must be cautious and thorough in their decision-making process.

Hiring data scientists in a panic to deploy expensive, intricate technologies and over-engineered processes may be a costly folly and can spoil the impression of the viability of these powerful and beneficial tech tools. 

Instead, data scientists should devote time to learning about the technology and how it has already benefited those in the transportation logistics industry who have previously implemented it. What are some of the procedures a logistics company should follow before implementing AI/ML?

Data quality should come first

Keep in mind that the speed of your AI journey will be dictated by the quality of your data. Data cleanliness and management are critical to a successful AI program (or any considerable data effort). Fortunately, this data can be compiled, organized, and accessed by many people, but the process is cumbersome. 70% of respondents to an O’Reilly poll say that incorrectly labeled and unlabeled data are a significant problem. Respondents also mentioned low quality from third-party sources (42%), fragmented data repositories and lack of metadata (50%), and unstructured, difficult-to-manage data (44%).

A recent MHI and Deloitte poll found that 60% of transportation sector respondents anticipate using AI in the next five years. Historically, the transportation industry has been sluggish to accept new technology. Stream data and analytics infrastructures are expected to grow five times faster than current levels by the end of 2024, according to Gartner.

An essential initial step for many transportation management organizations will be to access, clean up, and integrate the appropriate data to maximize artificial intelligence (AI). When it comes to artificial intelligence, a tremendous and wide variety of data sources are necessary for a dead-on outcome.

Examine your abilities and think about consulting an expert

After conducting a thorough assessment of the quality of their data and current technology stacks, firms can identify what intelligence capabilities they already have.

You should also choose AI-driven solutions that don’t necessitate that you become a data scientist while investing in newer technologies for digital transformation.

If you need assistance in transforming your supply chain business to AI, experts at Cooperative Computing can help you with it.