5 Innovative Ways to Use AI for Supply Chain Optimization

by | Industry

The supply chain industry is on the brink of a renaissance. As artificial intelligence becomes more accessible, it will bring freight forwarders, warehouse operators, NVOCCs, and other logistics services providers to new heights in speed, efficiency, and service. 

Today, only 12 percent of supply chain professionals say their organizations are currently using artificial intelligence (AI) in their operations. However, 6 in 10 of those same professionals expect to be doing so five years from now, according to a recent survey. Another report comes to a similar conclusion, with Gartner predicting that the level of machine automation in supply chain processes will double in the next five years.

Innovations in big data and cloud computing have led to improvements in efficiency in the business world at large. Supply chains and logistics are some of the areas that have immense potential for positive impact by these technologies A growing number of companies are using machines to augment (and, yes, sometimes replace) their supply chain operations human resources.

This shift has led to benefits such as improved productivity, reduced costs, and lower margins of error. These advantages are significant in the field of supply chain and logistics, where shaving off just one minute or one inch per pallet can add up to massive cumulative results. Therefore, it’s more important than ever before for companies to understand how they can use AI technology to keep up with – or, ultimately, surpass – the competition.


What is artificial intelligence (AI)?

Gartner defines artificial intelligence as technology that “applies advanced analysis and logic-based techniques, including machine learning (ML), to interpret events, support and automate decisions, and take actions.”

Simply put, however, artificial intelligence is intelligence displayed by machines, rather than the human mind. AI comes in many shapes and colors with a broad array of applications across many verticals.

Accenture recently summarized artificial intelligence nicely, “AI is about intelligent automation, augmentation, and innovation.”

  • Automation: the use of machines and computers to automate tasks that would otherwise have been accomplished manually.
  • Augmentation: using machine learning and technology to assist humans in making better decisions.
  • Innovation: scaling with the use of technology where it would have been impossible for humans to keep up with a function.

From the “recommended for you” queue in your Netflix stream to the most complex medical, governmental, and technological applications, AI has found its way into nearly every aspect of our personal and professional lives: even when we don’t notice it.

That’s not to say that there isn’t still room for growth. We’re only in the early stages of artificial intelligence proliferation: there are still so many opportunities for businesses to put the technology to use to aid in growth.

Here are five ways supply chain businesses can use AI to maximize productivity, lower costs, and decrease the margin of error.

1. Use AI for Predictive Maintenance

Various kinds of equipment are used in supply chains to move and manipulate goods. According to Mocan, Draghici & Mocan, material handling equipment in logistics helps to improve productivity and lower injury. Heavy equipment such as forklifts or cardboard balers are mainstays in the warehouse and could bring operations to a rapid halt if they were to suddenly break down.

The equipment has to undergo regular maintenance to guarantee its safety and proper use. These important periods of service may lead to downtimes, and AI can help businesses to contain and mitigate this challenge. In fact, a recent study found that 29 percent of AI implementations in manufacturing were for maintaining machinery and production assets.

AI is particularly useful for predictive maintenance, whereby businesses can deploy sensors to examine the condition of equipment on a continuous basis to automatically determine the optimal timing for servicing. This allows enterprises to conduct maintenance when it is most needed, rather than using scheduled times, which have a higher potential of reducing the productivity of equipment and personnel.

An AI system can even be used to build predictive analytics and machine learning algorithms that enable organizations to predict the potential of equipment failure for corrective actions, staving off disaster before it strikes.


2. Optimize Manufacturing Processes With Artificial Intelligence

AI can also boost manufacturing productivity through the monitoring of various performance measures in supply chains and logistics. From iterative product design that uses AI to find the perfect combination of quality, cost, and value to the assembly line, artificial intelligence has the potential to make improvements throughout the product development lifecycle.

For example, companies are concerned about cycle times, lead times, downtimes, margins of error, costs, supplier reliability, and quantities of goods. AI can be used to improve these measures through its application in a variety of operations.

Artificial intelligence can run in the background to gather information, analyze it, and suggest improvements for manufacturing equipment operators. Furthermore, AI can even learn the way people make decisions to understand the workings of the human mind.

Accordingly, AI allows businesses to achieve a higher level of operational efficiency, boost productivity, and cut costs.


3. Improve Accuracy of Inventory Management with AI

The accuracy of inventory management affects elements such as the cost of operations and productivity. The flow of goods in and out of warehouses also affects the picking and packing of goods and order processing. These processes take considerable time and the sheer volume of movement makes it easy for mistakes to slip in.

AI can be highly effective in enhancing inventory management. Intelligent systems that use data to analyze and interpret inventory and sales information (and sometimes incorporate external elements like weather or fashion trends) to guide demand planning and forecasting decisions have become standard in the retail space, for example.

Furthermore, the use of AI allows businesses to anticipate the demand for goods to lower the costs and errors associated with stagnant or overstocked inventory. Machine learning can also be used in supply chain planning to recommend goods that should be counted more frequently in cyclical physical counts, keeping inventory accurate.

The most successful businesses today are relying on AI to access accurate data in real-time to effectively manage supply and demand, keeping the most optimal inventory levels for profitable operations.


4. Enhance Safety Through AI

Warehouse injuries have been rising annually for several years now. Automated tools based on AI offer better planning and management of warehouses to guarantee the safety of workers and materials. In this case, businesses can employ AI to analyze data on safety and inform managers about the potential dangers in the workplace.

The technology is also used to record the parameters for maintaining inventories and to provide updated information on operations. This data can help managers and supervisors to keep warehouses secure through the development and implementation of predictive models that lead to enhanced safety measures. For instance, the constant supervision of risk areas and observance of safety standards can be scaled more easily with robotics and AI.

Companies should use AI to identify areas that are prone to safety issues and implement recommended ways to reduce work-related injuries.

5. Reduce the Margin of Error With AI

Hellingrath & Lechtenberg provide an example of the way companies can use AI to lower the margin of error in supply chains and logistics. Some logistics and supply chain process operations occur regularly, for instance, the movement of goods in and out of warehouses. The operations lead to various warehouse management actions such as the analysis of manifests. AI can be applied as a deep learning network that examines the manifests associated with various storage containers.

The data can be compared to the actual content of the containers (this information is obtained from radiography images). Businesses can thus collect containers that are consistent with their manifests, reducing errors in supply chains and logistics.


The Way Forward with AI

These examples are only the tip of the iceberg. From natural language processing to robotics, AI capabilities in logistics are endless.

At the end of the day, supply chain and logistics companies are always looking for ways to maximize productivity, lower costs, and decrease the margin of error. The potential for widespread AI adoption in this area is clear: McKinsey recently surveyed supply chain executives and found that 61 percent of respondents reported decreased costs, while 53 percent reported increased revenues as a direct result of introducing artificial intelligence into their supply chains.

These measures affect the overall performance of supply chains and logistics operations, enhancing enterprise performance as a whole. A carefully chosen AI solution that aligns with macro goals can benefit the entire organization through the gathering and analysis of big data that allows automation, error reduction, and process optimization. Now is the time for supply chain leaders to evaluate and adopt AI to address the pain points and design an intelligent supply chain that’s ripe for success.

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