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Taming Supply Chain Networks

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By Seshadri Guha & Syamala Srinivasan, CGN Global

As different analytical and data technologies emerge and mature over time, the innovations ranging from real time visibility to operational analytics, supply-chain modeling to predictive analytics, or advanced visualization to simulation will all provide the key ingredients for success.

Industrial supply chain networks are complex organisms with a multitude of interdependent factors and functions that can drive or derail business performance. Like a wild horse, each supply chain has its own unique and often irascible personality that is influenced by the particular firm’s culture, behaviors, and operations. For businesses’ that have the courage to map them, manage them, master them, and finally tame them, this enigma can turn into a unique competitive advantage.

The complexity and scope of supply chains are fertile ground to deploy analytics. However, a deep understanding of the structure, dynamics, and behavior of these supply chain networks are required to create sustainable advantage.

To better understand the potential of modern supply chains, it is beneficial to compare them to the functioning of the human body.

The Autonomous Function: The human body operates as an integrated autonomous system. The different functional organs like the heart, the lungs, and the kidneys operate as one system. They operate independently, yet work together to coordinate the flow and function of the human body without conscious effort. They constantly analyze feedback from different internal systems to regulate the functioning of the body.

Supply chains are very similar. The coordination of a multitude of functions that make up the supply chain need to be embedded within the structure. As supply chain architects and managers, we need to develop the operational feedback, analytics, and well-designed autonomous functions and integrate them seamlessly into the structure.

Technologies from RFID to visibility and IOT to collaboration, need to be utilized to identify sources of variability. Basic analytics will help us to quickly diagnose variability such as emerging shortage of material, disruption in supply, and changes in demand and effectively communicate to other internal functions. By piecing together information, the supply chains can then automatically recognize changes and adapt.

The Reflex Function: The body also responds to both internal and external stimulus. It demonstrates fight or flight response to perceived threat. For example, if we recognize an external threat we may utilize resources around us to protect or distance ourselves. The body has the innate ability to synthesize external information quickly, recognize threats efficiently, and respond swiftly to protect itself instinctively.

As supply chains evolve, we will need access to timely information from end to end. Filters, computations, and analytics will swiftly identify changes in the extended ecosystem. Reflexes that quickly operate levers and adjust operating conditions will need to be integrated into policies and behaviors that will protect the supply-chain and the business.

This can be achieved by integrating real time sensors, analytics, and responders across the extended enterprise. Processes within the business from S&OP to logistics, and planning to receiving will need to be instrumented to identify risks and opportunities. Analytical models will need to be flexible to recognize, respond, and escalate critical triggers.

The Cognitive Function: Humans continually learn, analyze implications, evaluate, and plan to generate a coordinated response. They synthesize external information, apply reason and judgment, evaluate scenarios, and predict potential sources of opportunity and challenge, all in all, key cognitive functions. Human endeavor encompasses thinking, forecasting, and strategy. The ability to look ahead, think ahead, plan ahead, and execute differentiates the high achievers among us.

Supply chains need to aggregate data and utilize advanced analytics to recognize emergent patterns in time, by segment, by region or the like. Some of these patterns may not be readily apparent or recognizable to us as individuals. The role of analytics will be to effectively and efficiently inform our decisions. Recognizing comprehensive and discriminating patterns will allow us as supply chain architects to strategically change structure, behavior, and capability of the supply chain to future proof and protect our business. Knowing something and doing the “right thing” are two completely different notions. Analytics helps provide comprehension and understanding of a situation.

By combining industrial science, data science, and execution science, this biological model may reveal both the future opportunities and possible direction of industrial supply chain evolution.

In addition, taming either a supply chain or a wild horse requires the deft use of a rope, but also necessitates a soft touch to garner trust and respect. As practitioners and operators—or supply chain whisperers—it would be unrealistic to expect acquiescence overnight. As different analytical and data technologies emerge and mature over time, the innovations ranging from real time visibility to operational analytics, supply-chain modeling to predictive analytics, or advanced visualization to simulation will all provide the key ingredients for success. The ability to identify these technological opportunities and quickly, efficiently and continually integrate them into operations, while unleashing the creative and competitive potential within the organization, will eventually tame even the most unruly supply chains.

For as Socrates once said, “If you want to be a good saddler, saddle the worst horse; for if you can tame one, you can tame all.”

About the Authors

Seshadri Guha is an Alumna of the Kellogg School of Business, and Dr. Syamala Srinivasan is a Senior Fellow of the CGN Global Business Analytics Team and an Adjunct Professor at Northwestern University School of Professional Studies; both are practitioners from the industry (

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