Super Easy AI for Your Supply Chain

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The fast track connecting AI, Logistics, and Visibility
is easier than you think 

All supply chain problems boil down to one issue: something was not delivered as expected. That’s a logistics problem. You wanna know what else is a logistics problem? Supply chain resilience. Visibility. Shipping costs. Returns. Warranty recalls. Surviving the current transportation crunch.    

Do you want to solve these problems? End-to-end visibility is the solution, and you’ll need Artificial Intelligence (AI) to make it happen.   

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AI Enables Supply Chain Visibility  

The last several posts examined the importance of increasing supply chain visibility, introducing situational awareness into the process. Aware of the optimal outcome for every shipment, the conditions affecting this outcome, and having the ability to achieve it by changing execution. AI enables this situational awareness, allowing a supply chain to automatically: 

  • Prevent problems from occurring before negatively impacting business and;   

  • Optimize workflow to increase value, performance, and profitability. For example, AI directs workflow to take advantage of opportunities like shipment consolidations, securing excess capacity in transportation spot markets, and fulfilling last-minute customer changes. 

  • Minimize risks and costs when there’s volatility, scarce resources, and tight constraints - like fighting the current wildfire engulfing the global supply chain. 

 

Defining AI, Machine Learning, Data Analytics 

Definitions matter when implementing technology; stakeholder expectations, technical requirements, functional specifications, and data mapping must be thorough and accurate. Categories for technology, such as Artificial Intelligence (AI), Machine Learning (ML), Data Analytics (DA), and Business Intelligence (BI), are far less important than the former definitions.   

I point this out because there’s a lot of confusion over these terms used interchangeably. These technologies are shrouded in vendor buzz, many are unfamiliar with them, and functionality often overlaps. For the sake of clarity, here’s my summary of these terms: 

  • Artificial Intelligence enables a system to operate autonomously with processes that understand, think, learn, and respond like humans. 

  • Machine Learning is a subset of AI that helps an AI system learn quicker. 

  • Data Analytics analyzes data for insights and predictions to enhance decision-making by humans and AI systems.  Data analytics is not a single product but an ecosystem of programs for processing data. 

  • Business Intelligence is a class of software that uses AI, DA, and ML to provide process improvements and productivity solutions for businesses. 

There are many better, more detailed definitions than mine. The differences are not always clear to me either, and I say this after designing logistics software with AI and machine learning. I think there are far more important matters for supply chains to focus on than eloquence here. That said, we will keep things simple by generically referring to these technologies as “AI.”   

 

What’s Most Important to Know About AI

The most important thing to keep in mind when considering AI solutions is what’s always been most important:   

  1. Thoroughly and accurately define stakeholder expectations. 

  2. Thoroughly and accurately define functional and data requirements. 

  3. Confirm the technology, solution providers, and implementation plan will meet the preceding requirements and expectations.  


Fast Track to ROI with AI 

The quickest path for increasing supply chain visibility is integrating shipping and receiving processes with transportation carriers. Warehousing activities offer an easy starting point for AI solutions and supply chain visibility, with repetitive transactions that link into transportation networks.  

  • Pick, pack, and putaway transactions are ideal for robotic automation and optimization. AI can optimize workflow with changes in future demand, transportation availability, shipping costs, current risks, constraints, operational costs, and shipment consolidation opportunities.

  • Autonomous vehicles like warehouse robots and forklifts, automated sorting, and robotic inventory systems can work error-free, 24x7 without human intervention.

  • Fleet operations can optimize the delivery of orders with a dynamic routing solution like the flexis ProfiTour. ProfiTour uses AI to automate scheduling and navigation of multi-stop delivery plans for each vehicle in a fleet, accounting for many complex variables such as appointments, order consolidations, minimizing costs, and greenhouse gases.    

 

Conclusion 

The potential for AI appears limitless, and there are immediate gains available for your company.  Like supply chain visibility, its full potential awaits the digital supply chain. That will not happen until the Supply Chain 4.0 riddle is solved.   

AI looks like it may be another race for the tortoises, but it’s also an easier race to win if you take the right path.  Think transportation and logistics.   

 

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