Supply chain leaders love shiny objects. The shiny object syndrome happens when teams focus undue attention on an idea that is new, and trendy yet drop it in its entirety as soon as something new can take its place. Six years ago, the focus was on blockchain. Investments in supply chain using blockchain deployments failed. Tradelens, the largest and most successful supply chain blockchain deployment, discontinued operations in the first quarter of 2023.
Today, the shiny object is Artificial Intelligence (AI). In my recent travels to supply chain events, I find AI discussions everywhere, but the business use cases few and far between. The discussions on AI lack both a grounding in definitions and the delineation of a clear value proposition.
Here I focus on how supply chain leaders can rise above the shiny object syndrome to unleash new levels of value.
The Issue
Today, tension abounds. The most advanced analytics to enable AI are available from the top cloud computing companies of Amazon, Azure, and Google. In the adoption of these more advanced technologies, supply chain leaders face a dilemma.
Manufacturers buy software. They are not builders. The analytics from cloud service providers are best suited for a build strategy. Supply chain leaders saddled with maintenance costs of legacy packaged software are unsure what to do.
As shown in Figure 1, supply chain software innovation is not a normal distribution. In 2000, it was a bell curve with an equal number of early and late adopters. Today, the late adopters outnumber early adopters by a 3:1 factor. So, while many business leaders waft eloquently about shiny objects, they are late followers. As a result, when supply chain leaders speak on “innovation,” they give voice to late adopter perspective.
Figure 1. Software Adoption Cycle
As a result, the packaged software market moves much slower rate than the evolution of tech capabilities. The gap is growing. Is this a problem? Maybe. It depends on the problem being solved.
The Promise
Adoption of concepts like Large Language Models (LLM), Ontological Frameworks, Graph Databases, Vector Databases (Vector DB), NoSQL, and Schema on Read is slow. Existing technologies move structured data efficiently using relational database technologies to improve enterprise transactions, but the processing of unstructured data is an opportunity. Eighty percent of the data surrounding the supply chain is unstructured—text, images, and streaming—but is not used.
The buy strategy assumes that software providers will drive innovation, but it takes time.
The Tension
The answer is not easy. Forms of AI— machine learning, narrow AI, and pattern recognition—are evolving based on schema-on-read databases (relational database structures) using traditional packaged software solution taxonomy definitions, but this approach does not address the larger opportunity.
The problem? Software providers are automating traditional software. Innovation is low. The current focus is to improve transactional efficiency and market insights to improve processes within a function —sales, marketing, R&D, manufacturing, procurement, and transportation. The solutions are inside-out designed to better use enterprise data, but the market is shifting from inside-out process focus to an outside-in business process flow.
The use of unstructured data, in combination with emerging AI techniques, offers great promise to better sense and intelligently respond. The question for business leaders, “Is how to get started?” The conundrum includes:
- Old Tech Is Expensive to Maintain. While packaged software is cheaper for the initial install, it is more expensive over time. Today, based on studies by ASCM, 80% of software purchased is shelfware. (Purchased, but not used.) While the risk of employee turnover and failed projects is lower, maintenance costs (often 15-22% of the initial software purchase) is expensive. The cost of evolving on changing platforms is millions of dollars. (For example, the movement for SAP customers to adopt HANA is a platform change not covered by maintenance.) The question is, “How to maintain and evolve?” The answer is complicated.
- New Tech Is Not for Everything and Everyone. The use of newer technologies cannot be a broad-brushed approach. Avoid generic discussions of AI. Instead, focus on getting clear on definitions, improving capabilities and aligning the AI tactics with business value. The number of resources understanding both the supply chain potential and the promise of newer forms of technology is a constraint. The cloud-based technology providers provide tools, but not process automation.
- Faster and Hands-Free Processing of Existing Technology Is Not the Best Answer. The global supply chain was built on the assumption that demand and supply variability would be low and government policies would be rational. Neither assumption is valid today. As a result, the use of market data is growing in importance. Functional efficiency throws the supply chain out-of-balance reducing organizational effectiveness.
Wrap-up
In summary, side-step the shiny object syndrome. Get clear on definitions and value cases. Align your Information Technology strategy (IT) based on these insights. Delay the investment in major platform investments. Move forward if and only when the organization is clear.
