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HomeBusiness OperationsThe Supply ChainHow Real-World Data Analysis Aids Better Prediction of Disruptions and Threats to...

How Real-World Data Analysis Aids Better Prediction of Disruptions and Threats to Sustainable Supply Chains

Data Analysis Aids Better Prediction
Image by Freepik

Dmitry Antanovich, LeverX

A certain level of normalcy has returned to everyday life, and the global economic situation has become relatively stable. In May 2023, the Global Supply Chain Pressure Index (GSCPI) fell to its lowest level in 15 years as pandemic-driven innovations took hold.

There is no certainty in today’s political climate, meaning disruptions and supply chain threats are a constant worry for business owners. Sustainability, which has become a requirement due to public pressure and governmental restrictions, only exacerbates those risks as redundancies have been eliminated to cut emissions. 

Over 90% of emissions related to providing a company’s products and services are associated with the supply chain, but the balance of cost control and sustainability brings its own set of challenges. 

Fortunately, the same innovations and processes created to maximize costs in an uncertain supply chain can be applied to the issue of sustainability. Big data, and how we can leverage it, is now a tool used to predict disruptions while maintaining sustainable practices. 

The role of real-world data analysis in the supply chain

Real-world data analysis plays a significant role in improving the supply chain. 

  • Risk identification: Data analysis helps identify risks, like political instability or supplier bankruptcy before they become disruptive. 
  • Improvement of operational efficiency: Companies can identify inefficiencies and work towards improving them. This reduces costs and also reduces waste.
  • Scenario planning: Predictive simulations for various scenarios reveal potential impacts and can help enterprises plan for disruptions, threats, or changes in the marketplace. 
  • Environmental impact assessment: Real-world data can provide valuable insights into the environmental impact of different practices within the supply chain. 

In her Q4 earnings remarks, The Hershey Company CEO, Michele Buck, explained how there was increasing demand for their products despite supply chain challenges. There was an opportunity for growth but added risk if they simply ramped up their normal practices. 

Instead, the Hershey team rationalized select items by double- and triple-facing top velocity SKUs. This reduced their shipping complexity, allowing them to capitalize on the growth opportunity while minimizing potential supply chain disruptions. 

What are the main risks to supply chain continuity?

“There’s a reliability hangover,” Bill Seward, president of supply-chain solutions for UPS, told Bloomberg last year. The COVID-19 pandemic was one of the most stressful periods in history for businesses desperately trying to manage the supply chain. 

Many of those issues remain. 

  • Operational risks: Equipment breakdown, process failure, and labour issues.
  • Demand risks: Unpredictable changes in demand patterns.
  • Environmental risks: Natural disasters, geopolitical issues, and regulation changes.

But tech innovations in logistics, including leveraging big data, continue to change the landscape. Private investment in supply chain tech has risen steadily, leading to several avenues of risk mitigation. 

How to use real-world data analysis to predict supply chain disruptions

When companies harness big data in meaningful ways, they can understand their supply chain more comprehensively and make changes as necessary. 

There are four levels of data analysis:

Descriptive analytics

This looks into the data to understand what has happened in the past. For example, it might identify a trend that shipments from a particular supplier are frequently delayed, or that certain materials have a high carbon footprint. 

The results from a descriptive analysis should become the “single source of truth” for a company results that can drive business decisions with clarity.

Diagnostic analytics

If descriptive is the study of what happened, diagnostic attempts to answer why. For instance, when you identify a trend of delayed shipments, diagnostic analytics determine why those delays happened, be it poor weather conditions, infrastructure issues, or supplier inefficiencies. 

Predictive analytics

Predictive analytics estimate future events using the insights gleaned from descriptive and diagnostic analytics. 

Advanced algorithms and machine-learning techniques can predict an increased chance of disruptions due to geopolitical instability or forecast high emission levels due to excessive usage of certain materials.

Prescriptive analytics

The most advanced level, prescriptive analytics, recommends actions to either make the most of predicted occurrences or mitigate them. 

Using a linear programming method, the same processes used to minimize costs can be tweaked to minimize emissions or protect against potential disruptions. 

Just as company data could suggest purchasing key resources at the lowest price from overseas suppliers, the same process could be adjusted to optimize for factors such as carbon emissions. 

For example, businesses might want to keep their carbon emissions below a certain level in the climate change era. In this case, the programming might suggest using a local supplier that does not require air or sea freight shipping. 

Final thoughts

In essence, real-world data analysis can offer a systematic approach to any objective, such as optimizing costs, minimizing emissions, or safeguarding against potential risks in the current global business landscape. 

It also provides a roadmap for mitigating and adapting to the disruptions and threats faced by sustainable supply chains.

Dmitry Antanovich :

Solutions Director, Digital Supply Chain at LeverX North America

I am passionate about finding the right solution for a customer’s requirements using the portfolio of SAP solutions. I have over 17 years of SAP experience and an extensive technical understanding of SAP solutions, and architecture. And overall software architecture.

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