Rebuilding Supply Chains with Analytics

Updated: Apr 26


Rebuilding Supply Chains with Analytics

Supply chains and the professionals behind them have developed the capability to overcome historical challenges. Herculean efforts, tribal knowledge, collaboration, and extraordinary expense often save the day. However, true resiliency and optimal efficiency cannot sustainably be achieved this way.


Too often, supply chain professionals accept the status quo as it relates to the tools at their disposal. Spreadsheets or lesser forms of technology permeate the process and are often used in ways that exceed the intended purpose of the applications. Furthermore, the perception of what is possible is often clouded by extensive amounts of time working the same recurring problems that are often handcuffed by minimum visibility.



Introduce Analytics, Reduce Unknowns

Most contemporary supply chains are missing critical pieces of data that may have a transformative impact. This data is either not available or thought to be impossible to capture. This paradigm no longer remains valid. The supply chain can be infused with rich transactional data through the introduction of high-fidelity information captured autonomously.


The subsequent introduction of analytics can convert unknowns to knowns and revolutionize the capability to manage and optimize the supply chain. The answers to questions that could not be answered prior, become painfully evident through data. This has a tremendous impact on the workforce as they become directed by data-driven insights. Routes are optimized, waste is reduced, efficiency improves, and as a byproduct, spend reduces dramatically.


When executed correctly, the introduction of analytics to the supply chain has a truly transformative impact.



The Foundational Step is Not Optional

Moving from a tactical and reactionary mode of operation to an analytically driven model is a journey.

  • The data foundation needs to be well thought out and intelligently architected

  • Supporting business processes must be put in place to help the transition strategy

  • A connectivity plan must be established for legacy system communication and integration

One would not intentionally build a six-bedroom house on a foundation intended to support a two-bedroom house. The same is true for the buildout of an analytically driven supply chain model. A clear vision of the end state must be put in place before the process can commence in earnest.



Where to Start

It is best to think of the process in a linear fashion, risk the temptation to become overwhelmed or lost in the data, and broaden the perception of what is achievable.


1. Infuse the Supply Chain with Robust High-Fidelity Data

The supply chain is full of complexities, and its effective management requires high integrity data, including detailed chain-of-custody transactional data. The closer to real-time this data is, the better – no transaction should be looked at as too small or insignificant.


Often, transactional data on its own is not enough. It is essential to understand when further contextualization is required. For example, connecting a carrier to an outbound shipment transaction can have far-reaching benefits. It creates forensic data while simultaneously opening the door to a multitude of analytical opportunities when analyzed on its own or, better yet, infused with additional contextualized data.


2. Risk the Temptation to Get Lost in the Data

Once a data capture methodology has been determined and implemented, you will be presented with more data than previously thought possible. Prior ideas will be challenged, assumptions will be proved wrong, and numerous issues and their causes will be illuminated.

It is critical not to become overwhelmed. Accept what you are seeing and create a prioritized plan to work through what is being learned. All of the supply chain issues will not be solved overnight. Identify the highest impact issues and take steps to solve them.


3. Embrace the Art of What is Possible

One of the most significant risks to progressive change is the acceptance of current state when a dream state is obtainable. The tools to deliver that state are a part of today’s technology toolset.




Benefits of an Analytically Driven Supply Chain


Simply stated, the high-level benefits can be summarized into four over-arching categories:


1. Qualify and Challenge Assumptions Regarding the Supply Chain


The supply chain management process is fraught with assumptions:

  • What should be happening?

  • What is actually happening?

  • How much returnable container inventory is available?

  • Is my fleet right-sized, and how do I right-size it for the next model?

  • How quickly is material moving?

  • What production impacts are imminent due to a lack of inventory or current condition?

Without reliable transactional data throughout the supply chain, most of these questions can’t be answered. Without answers, the ability to effectively manage the supply chain is severely compromised.


2. Eliminate the Investigation Phase

So many supply chain professionals spend their day, if not their career, investigating what issues are present in the supply chain, where the problems are occurring, and determining their magnitude. An analytically driven supply chain model can eliminate the investigation phase. Users consume actionable insights, and activity becomes optimally directed.


3. Death of Supposition


Contextualized, highly accurate data creates the ability to deliver actionable insight. For example, when properly executed, the question of whether or not a container fleet is right-sized can be answered through an analytically driven supply chain model in a matter of seconds. The question then becomes how to fund the purchase of new containers, or where to repurpose the excess.


4. Begin the Conversation to a Proactive Management Model

Analytics convert a completely reactionary management model to a model where issues can be identified before they occur. Analytics infuse a significant, immediate benefit and are the stepping stone for machine learning and in-depth artificial intelligence.



Conclusion


Today’s most efficient supply chains have recognized the data accuracy required to drive a fact-based analytical model. Additionally, they have implemented infrastructure to capture this data with an extremely high level of accuracy.

Data eliminates assumptions and drives transformative change when it has unquestionable integrity. This data then becomes the fuel that drives the analytical engine that can be fully responsible for issue and sensitivity recognition throughout the supply chain. To truly “optimize,” those involved in the supply chain need to be change agents, not in their ability to identify issues, but instead permanently resolve them. This happens through the implementation of an analytically driven model that enables them to consume actionable insights and drive change as a result.

Surgere Interius™ Dashboard On Hand Quantity

To learn more about analytical supply chain digitization, please contact Surgere for an interactive demonstration by emailing connect@surgere.com.



ABOUT THE AUTHOR

Rob Fink, CSO, leads development, communication, and execution of Surgere’s strategic vision. He has experience in problem resolution, creation, customization, and deployment of technology to solve supply chain issues.


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