Technology and Intelligent Decision Making

There is indisputable evidence that organizations adopting digital technologies powered with modern data sources for testing, learning, and adapting the technologies faster can outshine competitors. Artificial intelligence is at the center of it all, as AI-based systems help empower intelligent solutions and optimize decision-making. Gartner predicts that by 2023, 33% of large entities will be engaging data analysts for decision intelligence and decision modeling.

Data and analytics heads will need to prioritize their efforts on exploiting cloud capabilities, focusing on using technology to deal with change, innovation, resilience, efficiency, and just in time. Cost optimization remains a tangible goal but with a lesser focus than conventional business operations.

The Sudden Reality of Low-Probability Events

The business’ supply chain function is one of the early adopters of tech analytics in decision making. The function forms one of the most hard-hit areas by unforeseen global and regional events, hence the need for the systematic computational analysis of statistics or data to deal with the uncertainties.

The globalization of markets and digitization has forced companies to outsource materials worldwide and deliver products at low cost. However, the supply chain systems have constantly suffered major blows ranging from the dot com bubble, the 9/11 attack, the US vs. China trade wars, and most recently, the COVID-19 pandemic.

The supply chain sector has had two decades of pure disruption since the dot com bubble burst of 2000. The influential global and regional disruptions include: 

• Dot com bubble burst

• 9/11 Attack

• Hurricane Katrina

• Prime 2-day delivery

• iPhone's introduction

• Global financial crisis

• Iceland volcano eruption

• Earthquake and tsunami in Japan

• Brexit referendum

• Trade wars

• COVID-19

• War in Ukraine

In addition to these external disruptors, there have also been significant macroeconomic shifts that impact supply chain systems significantly. These shifts include the rise of e-commerce, cloud computing, AI/ML, and glocalization.

Amid these challenges, the supply chain systems have also faced the complexities associated with globalization. There have been more demands for faster fulfillment and pressures from corporate bottom lines and C-suites to operate just in time, lean, and efficiency.

The disruptions constantly revealed that the supply chain systems are not ready for low-probability events. Organizations struggled to establish efficient and resilient systems that could also address the complexities of markets. Most of their conventional systems had a deep focus on cost efficiencies but now acknowledge that they can easily misalign with resiliency. Organizations manage their systems using stable networks, policies, and transportation models that have proven not to work in a highly uncertain environment.

The Future of Supply Chain Systems

By leveraging the power of new technology and continuous design, more companies can convert these supply chain hurdles into opportunities for competitive advantage, minimize operational risks, and improve their resilience.

However, McKinsey notes that resiliency is not simply about finding solutions for the challenges and issues of the day. It is more about integrating a culture of technology and digital tools that allow organizations to stay ready for future challenges. According to Katy George, a senior partner at McKinsey, resilience means the same for organizations, whether in service delivery or physical supply chain systems. It means that a company can continue its operations even if a disruption occurs in its value chain.

In light of the challenges and the wake of the COVID-19 pandemic, realizing resilience through continuous improvement provides a competitive advantage for organizations that can effectively leverage modern technology. They can reduce functional and data silos and create an environment of continuous learning. Organization designs will adapt and gradually evolve, allowing them to make tradeoffs at any time, simulate possible situations with the help of digital twins, optimize policies, and speed up the time gap between making and executing decisions.

Companies can easily decide where to start and focus their efforts with a continually evolving design. An organization can start with a foundational network approach and then integrate additional use cases and scale them. They are confident that the design model can support different operations like the network strategy, streamlined logistics, optimized production, intelligent outsourcing, and customer-centric fulfillment.

Continuous Supply Chain Design; the Building Blocks

More companies are changing their supply chain models. According to Gartner’s predictions, over 50% of global companies will be using the Internet of Things, AI, and advanced analytics in their supply chains.

These advanced algorithms will allow companies to continually revise their decisions and adapt them to balance service, risk, profitability, and sustainability. Their need for a continuous design will necessitate components to support new generation platforms to allow the deployment of applications in a scalable manner. That way, organizations can address situations in different niches and make decisions as they arise. They can use AI and rich algorithms to assess situations using simulations and quickly respond.

The Power of Digital Twins

The manufacturing industry has long used digital twins in product design and engineering. They help optimize performance and cut costs, and the concept also helps in the continuous supply chain model. A digital twin is a dynamic, time-phased, and real-time digital representation of the different associations of the data objects that constitute the operations of the physical supply chain.

It forms a critical component of the dynamic supply chain design. It will allow companies to create virtual simulations of the physical supply chain and test different possible scenarios risk-free. They can learn how certain decisions will affect their operations beforehand and decide whether they are suitable for the organization. It is a significant leap towards intelligent decision-making and a competitive position in dealing with uncertain environments.

Wrapping Up

Conventional methods of organizational planning based on static assumptions around operational systems are incapable of keeping pace and surviving the uncertainties. The COVID-19 pandemic is the most recent disruption that proved most organizational systems are not designed with resilience in mind. Companies closed doors as global supply chains came to a standstill amid massive restrictions.

However, technology has come a great way, and in its current standing, companies have an easier way to redesign their supply chain and other operational systems for resilience. There are unprecedented rich algorithm engines, extensible data models, and AI. As companies integrate technology in their operations, they will be all about intelligent decision-making for the future.

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