Context is everything with supply chain AI, and it's the key to supply chain optimization. This small detail plays a big role in the success of AI projects and, ultimately, a company’s competitiveness.
Why Context is Everything for Supply Chain AI and Success
Good news. AI will eliminate your current job.
90% of new enterprise applications will embed AI in their products by 2025 (IDC), and it will change your job. Your role will grow more valuable as it becomes more cross-functional and impacts the bigger supply chain process.
Context is important. A little more information can drastically change our interpretation. The two opening sentences appear to celebrate your future unemployed as good news, thanks to AI. The full context for these words becomes apparent in the next paragraph, revealing a very different meaning.
Supply chain AI has a context too. Like the example above, it’s easy to miss if we don’t confirm our comprehension. A small misunderstanding can significantly alter the outcome, and that’s why we need to look at this issue more closely for supply chain AI.
Why Context Matters
What is this context for supply chain AI and why is it important? The context is your perception - your thinking and beliefs about the supply chain and the model you use. Your success in implementing supply chain AI depends on your understanding of how AI works with the future supply chain process you envision.
Perhaps most important, a model guides process improvements to keep them aligned, and the potential optimization your supply chain can realize depends on how effectively your model (understanding) does this.
In the future, businesses will become a competition between supply chains, and this seemingly small factor will determine whether your company can participate. Here we find another example of a small step that leads to big gains.
Warning: 85% Failure for AI Projects
According to Gartner, 85% of AI and machine learning projects fail to deliver due to bias in data, algorithms, or the teams responsible for managing them.
The article notes problems that trace back to misunderstandings and offers a few remedies - all involve mental perception. I’m no AI expert, but I am an expert at misunderstanding things and am not surprised to find it contributes to such a high failure rate.
The Correct Context
Successful AI enables a business to achieve its goal, and that’s the correct context for supply chain AI. The ultimate goal for the supply chain is optimization. So, supply chain optimization is the correct context for understanding supply chain AI. Your supply chain model must present an optimized supply chain - lean processes aligned at all levels.
The model you use ensures improvements align with the goal of optimization. Defining more precise requirements makes a better model: goals, objectives, relationships, functionality, structure, technology, and stakeholder expectations provide a fuller context for understanding how supply chain AI enables optimization.
Fun Playing with Models
I realize many readers may not be familiar with supply chain models, which should not stop you from taking the first steps. AI makes a supply chain model more valuable, and here are a few reasons you should use a model:
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AI mirrors your thinking, and so does a supply chain model.
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One tool should reflect the other - this is the sort of alignment you should see when you map out your understanding in a model and with AI.
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A model lets you show your thoughts to others to encourage collaboration and consensus.
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A good model can be digitized for AI, letting your thoughts govern the supply chain process.
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Crazy good stuff. Even sketching a crude outline on paper can open your eyes. Just try it to see for yourself.
Stay Focused
Wait a minute. Who needs a pie in the sky model for an imaginary future? You’re fighting a supply chain wildfire right now.
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- Suppliers can’t get products like computer chips and other components. There’s a transportation crunch, record-high fuel prices, plus inflation.
- Proposals for shutting down more pipelines and mandates that would harm US trucking capacity (ATA) may cripple supply chains.
- Politicians are dumping more fuel on fires they started while supply chains and customers absorb 100% liability.
You need a fire extinguisher, and supply chain AI looks perfect for the job. Point applications like flexis Transportation Planning and flexis Demand Capacity Management are excellent for taking the heat off the present volatility. AI-enabled supply chain apps like these are good fire extinguishers, and they can still be implemented while staying focused with the magical lens.
Conclusion
Context is everything for much of life, including supply chain AI. It’s a small detail that plays a big role in the success of AI projects, the degree of optimization a supply chain can achieve, and ultimately a company’s competitiveness.
You control the context for supply chain AI. The best way to ensure a successful outcome is to create a good supply chain model.
You want to learn more? Get your Supply Chain Manager's Guide to AI
