As artificial intelligence continues to play an increasing role in businesses around the world, it's becoming ever more clear that AI will either replace or significantly augment many human jobs over the next several years. While most of the public's attention is fixed on AI use cases in medicine, finance, and marketing, the supply chain management, and logistics sector is quietly undergoing an artificial intelligence revolution of its own. With the capability to optimize delivery times, decrease fuel costs, forecast surges in demand, and much more, AI is being rapidly adopted by logistics and supply chain companies looking to future-proof current revenue streams.
How AI is Changing Supply Chain and Logistics Today
- Unlocking the Power of Robotics
It seems inevitable that the majority of the world's assembly lines will be run by robots sooner rather than later, and artificial intelligence is already playing a prominent role in helping companies make this transition. As arguably the most noted example thus far, electric vehicle company Tesla uses robotics and automation to manage over 75% of its production line. To develop human-like vision capabilities and adapt to changes in temperature, the robots used in such manufacturing processes rely on AI and machine learning algorithms.
However, the development of enhanced computer vision systems to help robots handle more supply chain duties is just one example among many. Other relevant applications of AI in robotics include:
- Using embedded analytics to locate inventory faster
- Training robot workers to identify items in less time
- Deploying deep learning algorithms to develop item-specific packaging protocols and dramatically increase inventory throughput
- Automating Warehouse Management
The ability to accurately forecast upcoming demand is crucial for avoiding inventory overruns, overstaffing costs, lost revenue due to customer dissatisfaction, and employee safety hazards. Today's shipping and logistics management software relies almost exclusively on AI-driven solutions to aggregate data industry data from multiple sources and develops robust, predictive analytics that can help avoid the common problems mentioned above.
With the ability to reliably forecast demand firmly in place, companies can plan for surges in purchase orders, stock flows, and employee scheduling needs. Although big data analytics have traditionally been used only by large enterprises, more logistics software for small businesses has become available in recent years. Salesforce, for example, now offers a complete suite of logistics management features that utilize artificial intelligence to help onboard new delivery drivers, send proactive alerts to customers, and drill down on every aspect of the supply chain.
- Improving the B2C and B2B Customer Experience
Many B2C consumers only interact with logistics companies once at checkout and then once again upon receiving delivery of the physical goods they have purchased. Occasionally, a traditional consumer will interact with a shipping company during a product return. However, large logistics companies like UPS and DHL are increasingly deploying predictive AI solutions to let customers know when delays are likely. Increasing the number of touchpoints that a B2C consumer has with the logistics company delivering their order helps increase trust and build long-term brand loyalty.
B2B customers, on the other hand, usually have many more touchpoints with their logistics companies of choice. Common touchpoints on the B2B side include:
- Signing service level agreements (SLAs)
- Negotiating long-term contracts and favorable purchasing terms
- Managing numerous providers and coordinating multiple steps in the supply chain
When artificial intelligence is used to predict B2B customer requests and anticipate common questions that a customer may have, the logistics company deploying such a solution benefits from decreased customer service costs and improved customer satisfaction. In the most extreme cases, AI solutions can integrate with a B2B customer's existing shipping and logistics management software to generate automated purchase orders before stock officially runs out.
The Biggest Benefits of AI in Supply Chain and Logistics
- Decreased Wage Expenses and Training Costs
Unlike human employees, AI logistics platforms operate 24 hours per day without taking a moment off. Furthermore, AI solutions don't require health insurance, employee perks, or other employment-related expenses that significantly impact operating margins. Moreover, the cost of continually training third-party artificial intelligence solutions is already built into the price in the vast majority of cases.
Since the training algorithms are handled by the vendor and usually included as part of the initial deployment expense, AI solutions are often able to improve themselves at no extra cost. Therefore, businesses that choose shipping logistics software with an artificial intelligence component enjoy ever-increasing dividends on the initial capital outlay. When compared to the costs associated with continued training for human employees, the overhead of maintaining an AI solution can be orders of magnitude more attractive.
The Biggest Challenges of Deploying AI Solutions in Logistics and Supply Chain Management
- Decreased Transparency
Many AI algorithms are proprietary, so it may not always be clear why an automated solution is performing a certain action. While human employees can be more expensive to train, they can also articulate the reasons for their actions much better than many AI agents. When problems arise, it's useful to be able to drill down on exactly what went wrong and make sure the issue will not happen again. Without unfettered access to an AI solution's algorithm that created the problem, a user often finds themselves completely at the mercy of the vendor.
- Cascading Complexity
Especially in the logistics industry, evaluating an AI solution's performance is very straightforward. However, the increased automation that AI provides can be a double-edged sword. As an AI solution starts iterating upon its experience and generating algorithms of increasing complexity, what starts as a minor problem can often spiral out of control. Overfitting a dataset, for example, can result in a predictive analytics platform accidentally spamming customers with erroneous delay notifications in an attempt to be proactive.
In Conclusion
Although deploying artificial intelligence in logistics and supply chain management can come with some challenges, companies often find the benefits to be well worth the risk. As with most new technologies, organizations that take a disciplined, methodological approach to the rollout will significantly increase their chances for success.
Bespoke AI Solutions for Logistics and Supply Chain
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