Vince Poon is CEO of Aratum. He works with enterprises and governments across Asia to achieve supply chain digital transformation.
Over the last few years, the Covid-19 pandemic, geopolitical tensions and economic instability, among other factors, have exposed vulnerabilities in supply chain management.
Because of these concerns, the supply chain industry has risen to the forefront of topics at the board level. In fact, a Verusen’s survey conducted in 2022 revealed that reducing supply chain risks is the top priority of global leaders over the next 12 months. With supply chain issues expected to persist moving forward, chief supply chain officers (CSCOs) are faced with a pressing need to reinvent their business strategies.
Alongside the functional focus in traditional supply chain strategy, leaders now recognize three new priorities:
• Resilience: Building up systems to withstand external threats in order to enable quick recoveries.
• Agility: Developing the crucial capacity to rapidly adapt to new market conditions.
• Sustainability: Taking steps toward long-term success by considering environmental factors and responsible usage of resources.
With an increased focus on these factors, legacy technology simply can’t ingest anywhere near the firehose of data that the modern supply chain emits, which is why digital transformation—in particular, artificial intelligence (AI)—is arguably the most crucial element in achieving these outcomes.
AI At The Forefront Of Supply Chain Transformation
In this era of data eruption, AI is at the forefront in supply chain transformation.
A Statista report shows that AI adoption is expected to grow to be crucial in the operations of worldwide businesses in the next two to three years. With the ability to process and analyze a large amount of data, AI can augment human decisions such as supply chain planning, demand forecasting, procurement, warehouse operations and delivery. It also offers the potential to reengineer business workflows for automation.
With AI-powered software, it is now possible to centralize data from all touchpoints. This makes leveraging cross-functional capacity easier and can harmonize used-to-be fragmented data and accelerate response time to market, which results in better decisions and higher profitability.
Succeeding With AI Adoption Despite Challenges
Despite the advantages of AI, companies still find themselves struggling when they try to execute. The availability of technologies is often not the real challenge; rather, the challenge lies in securing the buy-in from top management, key shareholders and other stakeholders.
In their book All in on AI, Thomas H. Davenport and Nitin Mittal describe how Deloitte’s audit innovation group follows a common process in developing use cases. This is a useful reference for any companies aspiring to transformation. This is how I would recommend applying AI to auditing procedures based on their framework:
1. Simplify and standardize: This first step is not to introduce any new technologies, but to simply document the current process flows and procedures. What is the description for a common overall workflow? What are the individual variations needed for particular jurisdictions?
2. Digitize and structure: Digitization means supporting a task with some form of technology that can collect data and monitor performance. This is a prerequisite to AI because AI learns from data. Organizations need to structure their activities in the order of which they are performed. Does the order make sense? Are there hidden dependencies? After identifying and combing the issues, accurate data can start to be collected.
3. Automate: Only when the tasks are digitized and structured can automation become a straightforward process. At this stage, you should understand, for example, where you can apply robotics to replace manual labor and improve consistency.
Even with this streamlined process, AI is still a new and strange field for many of us. In reality, applying such new technology is never three-steps-and-done. It is a journey that organizations need to optimize within and also with external parties such as customers and IT vendors.
Organizations must develop a data-driven strategy and a unified goal of digital transformation. Given that AI is a new field that evolves at lightning speed, getting trusted partners of technology to supplement internal expertise can also mean a much higher chance of success.
For many companies, supply chain transformation is simply not an option, but a must. As the world advances and disruptions remain constant, supply chains need to be resilient, agile and sustainable. Advances in technology such as AI have provided the tools that help enterprises find the path forward. Their roles are more as enablers while actions need to begin with educating organizations’ executives.
In other words, in order to excel in the world of machine learning, it’s essential to become an organizational learning machine.
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