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Digital Supply Chain – Is Your Data ready?

Rahul Raj • September 02, 2021
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New-age businesses require new-age digital models to contain the accelerated growth of data and to maintain a competitive advantage. However, while most businesses set ‘data capturing’ as their number one priority, they do not know how to utilize data. This leads to faster ‘time to insights’ but the insights generated may be largely incorrect. Now, the question lies with the simplification of such complex data collections to derive relevant information and strategies out of it. Hence, executives must focus on managing the rapid growth of the supply chain data and data sources. Introducing newer technologies like big data analytics tools, cloud, IoT, etc. are leading large enterprises towards further challenges such as: 

Increasing cost: Companies are focusing on capturing everything in the hope to gain valuable insights, which leads to a much higher spend on data storage solutions.

Geographical Compliance Risk: Organizations have become greedy and capture everything that they get their hands on for analysis. This approach leads to the accumulation of sensitive data which is not in compliance with relevant regulations. 

Inaccurate reporting & Insights: The required business insights remain hidden within the pool of complex data as there are random correlations between the data sets that may not have an actual link, leading the organization towards a wrong path. 

Enterprise Data Strategy for the Supply Chain 

Now that we have our useful data, carefully collected, we must plan our way to move from chaos to insights. If any data tool does not align with corporate data strategy and goals, it should not be budgeted. Supply chain enterprises generating exponential data in silos result in various challenges as the silos across the supply chain impacts the operational costs, downtime, and reduce service levels. Without a strategy, organizations will have inconsistent performance measurements across the manufacturing units and warehouses due to a lack of data transparency. This inevitably results in the inability to analyze the impacts due to changing requirements.

Businesses with multiple data sources and incompatible data analysis tools must obtain a unified view of the disparate data systems through a data warehousing and analytics strategy. What do we get when we integrate the two? 

Firstly, we achieve an integrated supply chain ecosystem with a complete 360- degree view of the supply chain data. Secondly, we get self-service data management and operational agility resulting in minimized planning, improved accuracy, and more time to insights. And last but not the least, we attain enterprise-level planning and collaboration which successfully aligns the enterprise’s priorities and goals with a single source of truth.

In addition, data standardization becomes a must as it simplifies the data management and analytical process, including standardizing data taxonomies across the enterprise.

ML

As we progress towards the digital future, automation across any data-related domain is becoming the new normal, among which the most relevant is machine learning. It has an enormous potential to accelerate business insights when it comes to enterprise supply chain data, with the ability to identify parameters critical to plugging gaps and removing inefficiencies. The root causes of supply chain management challenges depend on multiple variables.

Machine learning models use algorithms that can quickly process large volumes of data, delivering real-time solutions through customized algorithms. It follows a streamlined process where at first there is disparate data collection, cleansing, and combining it from all relevant sources. It then identifies factors having maximum impact on transactional data and assigns higher weights to them. It can also adapt based on the problem and unique use case, by selecting dependent and independent variables.

When properly directed, machine learning technologies excel at the classification of unstructured data and matching similar data from disparate environments.

IoT

With the rise in automation, there is also a huge increase in connected devices which will subsequently flood companies with more data and more complexities. These devices will challenge the traditional supply chain models and will open new business models which are enabled by IoT (Internet of Things) data, flowing back from sensors within the supply chain network. With IoT coming into play, the supply chain ecosystem will eventually start higher utilization of predictive analytics and serving customers in real-time. 

Blockchain

Blockchain has also started making an impact on supply chain operations and it is just a matter of time before the ability to ‘automate trust’ through a distributed ledger database and automated transactions will significantly improve supply chain efficiency.

Experts envision a future in which blockchain technology results in most or all supply chains becoming publicly open, distributed networks.

Conclusion

It is a time when organizations must respond quickly to tackle data growth, its complexities, and the commotion that comes with it. It includes focusing, simplifying, and standardizing data analysis through an enterprise data management strategy and exploring the range of possibilities afforded by machine learning, IoT, and blockchain. 

Those who do will be getting meaningful insights that align with their business. As the more volatile and complex future rushes towards them, they will be the first to detect changing market conditions and trends, and most importantly they will be able to innovate and adapt quickly. They will continuously evolve their supply chains, business models, and operational processes from a position of strength derived from those insights, as compared to those who don’t as they will find themselves at a significant disadvantage. 

References

https://www.statista.com/statistics/871513/worldwide-data-created/

https://www.kearney.com/analytics/article?/a/big-data-and-the-creative-destruction-of-today-s-business-models

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