The Simple Truth about Supply Chain AI

The Simple Truth about Supply Chain AI

Your Best AI Strategy is Continuous Improvement

Do you know the simple truth about Artificial Intelligence (AI) and supply chains? AI mimics our thinking, so our beliefs about the supply chain determine AI’s effectiveness. Think about that for a moment.

AI is not intelligent - it’s depending on us for that which is why effective thinking leads to effective AI.   What AI needs from us is to fulfill our expectations is:

  1. Clarity and accuracy of the instructions, processes, and outcomes.

  2. Control over business processes that produce the desired outcomes.

The good news is supply chain professionals have already prescribed a process that leads to highly effective AI. The answer is simple, but doing it may be harder than you think.  


Simple is Harder

In 1988, Steve Jobs famously told Business Week,

Simple can be harder than complex: You have to work hard to get your thinking clean to make it simple.

Authoring a high-impact short article is harder than verbosity. The best software engineers write concise source code in the fewest lines possible while preserving legibility and functionality. Best-in-class business processes are more efficient, producing greater value with fewer resources. Effective AI embodies all of these qualities.


Why Simple is Harder

Simplifying a complex system is hard work. Breaking down complicated processes to the fewest possible elements without sacrificing quality is time-consuming and demands clear thinking. Finding the point that maximizes value and avoids over-simplification is a discovery process that can involve alot of trial and error with models.

Albert Einstein weighed in on this challenge, saying, “Everything should be made as simple as possible, but no simpler.” He explained that simple does not mean simplistic and that an ability to simplify comes from expertise. It’s an innovative endeavor involving modeling, one part technical proficiency, and one part creative thinking. The result of this effort for a supply chain process should be greater clarity.

  • Greater clarity on business rules. Concise stakeholder requirements, dependencies, metrics, constraints, business objectives, mission-critical functionality needed for successful execution.

  • Greater data clarity. Concise data mapping with fewer data elements with more common properties and indexes.  

  • Greater clarity on functionality. You may see an increase in the number of software functions creating the business process but an aggregate code reduction for the same functions.

  • Clear connections between the rules, data, and functionality. Every part of the system is linked, working in concert to accomplish the desired result. There is no waste.


Continuous Improvement Simplifies the Process

Continuous improvement is ultimately about eliminating waste. Removing unnecessary work and resources leaves a leaner process, and this applies to every part of the process: software functions, workflow, machinery, source code, and data. The benefits include:  

  • Reduced time, errors, constraints, and the consumption of resources.  

  • Easier management, modification, and repurposing.

  • Less wasteful and more efficient.

  • More receptive to interoperability and systems integration.

  • Granular control and measurement.


Continuous Improvement Improves AI

One way simplification produces greater clarity is by improving data quality for a more accurate, comprehensive picture of the supply chain. Improvements lead to fewer data elements packing more extensive data properties, more timely and accurate sources, and more effective data relationships.  

What does this mean for AI? Data fuels AI, and this is higher quality data. It’s racing fuel for AI algorithms, powering faster and deeper learning for greater control over the process. The granularity of processes and data support finer measurement and expand potential integration points for AI to optimize decision-making and workflow.


A Simple Shipping Example

To see how process improvements precede and enhance AI, consider a business streamlining its warehouse shipping operation.

  • It combines separate shipping areas for small UPS parcels, heavier palletized freight, and in-house fleet into a single automated process.  

  • A cloud-based smart, dynamic routing solution is introduced to automate the selection of the least-costly delivery option for every shipment, according to each customer’s delivery preferences.  

  • A single process automates shipping for every mode of transportation.  

  • Shipping functions, workflow, and data elements are reduced to the fewest essential properties.

  • A superset of cross-enterprise data elements is defined, linking every detail across the full life cycle of each shipment. This data includes shipping instructions, order and inventory details, packaging, transportation provider, tracking numbers, pieces, weights, serialized containers, billing invoice, shipping charges, shipment location, stakeholder requirements, shipping documents, etc.

  • The data modeling resulting from this process improvement is made visible to a digital twin of the supply chain.

  • A repository of stakeholder requirements is created and enforced by shipping software functions to ensure compliance.

  • Strategic points in workflow and in-transit provide integration points for AI to initiate changes in execution.

  • Data and transportation functions mapped to each transportation vendors’ proprietary network allow AI to extend control beyond the enterprise.

  • AI in the routing solution evaluates data from the Transportation Planning app to identify cost-savings from consolidation opportunities and change shipment routing where needed.  



Artificial Intelligence is like supply chain visibility - they are both essential for agility, requiring concise, timely data and an ability to change execution for desired outcomes. This is only possible when stakeholder expectations, data models, and processes are aligned in a supply chain.

AI cannot do this for a business, but continuous improvements will. That requires thinking about the supply chain from a lean perspective.  

What AI can do is take a good process, a simpler process, and make it better.  That is the simple truth.    


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