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InsurTalks Podcast with Deepak Singhvi: Covid-19 & the Disruption in Supply Chain Management

7 minutes, 14 seconds read

The outbreak of pandemic Covid-19 has disrupted global supply chains and international trade. Social Distancing norms and restrictions on cross-border travel have made supply chain management extremely difficult. This has set off a chain reaction where many small and medium industries have been hit. 

To discuss the impact of COVID-19 pandemic on the supply chain and how technology can help create better supply chain flow during and post-pandemic, we interviewed Mr. Deepak Singhvi from SAP.

Mr. Deepak Singhvi is a Software Architect for SAP Ariba; where he steers designs and solutions for B2B E-procurement marketplace. Deepak has more than 15 years of experience in Big Data, Analytics, and platform development for various industries like e-commerce, banking, and semiconductor. 

Connect with Mr. Deepak Singhvi – LinkedIn

Excerpt from the interview-

SCM Challenges

What are some of the challenges organizations are facing due to disruption in global supply chains?

Mr. Deepak: We can gauge from the current situation how this pandemic is different from the outbreak of SARS in 2002. That time, China was contributing around 4% of the world’s total GDP and now China contributes somewhere between 18%- 20%. China is like the world’s factory which is present in every aspect of the business and production process. China is manufacturing for the world. 

In the post-pandemic world, we will have to adapt to the volatile environment. Especially, the continuous change in consumer behavior is creating a Bullwhip effect which is troubling the manufacturers. This effect makes it difficult for them to estimate inventories, plan production, set logistics in place, etc. The impact is going to be huge mainly because of China’s major contribution towards world GDP.

Is money also a challenge for the organizations in the supply chain?

Mr. Deepak: Money is an issue, maybe not for big companies. However, for SMEs and startups, who are also contributing to the supply chain, cash liquidity has been a concern. They need to maintain cash liquidity by reducing their operating costs and plan for short-term needs. 

Many governments have introduced provisions for SMEs and start-ups like the Government of India through Small Industries Development Bank of India (SIDBI), France has announced a 4 billion euros package, Germany has introduced a 2 billion euros package and so on. 

Small scale industries need working capital/short-term capital/short-term cash liquidity. Manufacturing units will have to ensure that they have enough supplies because they are somehow surviving this quarter. But, in the next quarter, they’ll have difficulty putting together everything in place. 

Role of Technology in Supply Chain Management 

As per a March survey conducted by the ‘Institute For Supply Chain Management’, nearly 75 percent of companies reported supply chain disruptions due to coronavirus-related transportation restrictions — how is technology going to address these concerns moving forward?

Mr. Deepak: Technology will play a very important role in Supply Chain Management. We need to understand the kinds of problems that are there and how technology can solve it. There are three dimensions to it. 

First is Cost. The USA and many other countries moved their manufacturing units to China three decades ago. That time, Supply Chain was mostly about Cost —  how to reduce cost and improve operational efficiency.

Second is Risk. In the past 5-10 years, another factor has come into place — Risk. Government tariffs and restrictions had a huge impact on the supply chain. It is difficult to import goods from China as there was a lot of opposition to it. 

Third is Resilience. The outbreak of pandemic COVID-19 tested the resilience of organizations to the disruption. 

It if was only about cost, a single human could have managed the cost by taking the assistance of tools and technology. But with increasing dimensions, humans alone cannot manage it. Therefore, technology is helping humans in holistic and better decision making. The supply chain dimensions will keep on increasing and will get more complex. Hence, technology will be important to adapt to the dynamic environment. 

AR and VR in Supply Chain

Do you see technologies like AR & VR playing a greater role in the procurement process?

Mr. Deepak: Technologies such as Augmented Reality and Virtual Reality will help enhance the User Experience. Social Distancing will be in practice for a long time. People might not get the same in-person experience as before. Therefore, AR and VR can help provide a similar experience to their customers. Moreover, these technologies will help significantly in the Customer Support function. VR can help in employee training and real-time assistance in manufacturing plants or offices. There will be a wider application of these technologies in various business units across different industries.

[Also read – 25 Disruptive AR Use Cases]

AI and Automation in Supply Chain Management

How does AI-based technologies like Machine Learning, Automation play a role in supply chain management? Is the investment in AI still relevant for enterprises during this Pandemic crisis?

Mr. Deepak: Artificial Intelligence will be needed more than ever. As the dimensions — apart from Cost, risk and Resilience will increase, it will create a need for an Intelligence System which can use rule-based computing. The system should be able to handle the effects of the bullwhip and enable automatic stock verification. AI will have a bigger role in developing Supply Chain solutions in the New Normal to automate manual operations and increase operational efficiency for Business Continuity

What are the main pain points in supply chain management which AI technologies or automation can address?

Mr. Deepak: Today, most of the systems and processes are in place. Suppliers and Buyers can collaborate on a system. Even SAP Ariba has a Supplier Collaboration platform. Everything works seamlessly under normal circumstances and there are intelligent solutions that make the Supply Chain a bit more flexible. 

Technology can automate manual collaborations. Normal rule-based decision making works most of the time but now we need more complex decision making. Systems need to consider external factors of COVID-19 such as disruption in any specific country or manufacturing plants. These factors can act as inputs to enable better decision-making.

Supply Chain in the New Normal

What lessons can you share for buyers and suppliers operating in the New Normal?

Mr. Deepak: One of the important lessons we learn here is to keep innovating. We need to introduce technologies like AI, AR, VR, RPA, etc. to automate manual processes wherever possible. 

Employees need to be trained in new technologies. Stakeholders need to get ready for the change. Those collaborating on systems should make their systems more agile. Also, businesses need to plan their cash flows to survive in the long term. 

The Future of Personalization

The growth of ‘web content management systems’ is driven by the demand of organizations to deliver personalized content and increase the interactions with customers present online — what does the future of personalization look like?

Mr. Deepak: We have been seeing personalized content for the past couple of years now. There are two aspects to this.

First, in the current situation, we need some kind of personalization. Companies that are ready to deliver personalized content will make a mark for themselves. Whereas, those who were not ready for it will be left behind. 

Second, in the post-pandemic world, many new people will be using digital platforms for the first time. They need better experiences that go beyond the traditional way of buying and selling. The companies that will add personalization into their products and services will have an edge over those who don’t. 

Start-ups in the Post-Pandemic World

It will be a survival issue for the start-ups for the next 1-2 years. Which sectors should start-ups focus their technological innovations which could add value not just to them but society at large?

Mr. Deepak: Many start-ups have been hit due to this pandemic. But now they need to evaluate whether to continue in the existing line of business or make a shift to an area where there’s opportunity in the post-pandemic world. 

The technology-led business model will be critical. It will have a key role in defining strategies. Start-ups have opportunities in the area of security and performance engineering as more people are working from home creating gaps in the data security. We are learning many lessons from this pandemic. They are reinforcing and validating our current model of getting out of the global crisis. There is a scope of growth as long as we keep building innovative solutions. 

In a nutshell

In this session, Mr. Deepa Singhvi shared his insights on technology innovations needed in the time of this crisis for start-ups and how supply chains can be improved through a new set of technologies like AI, AR, VR, and automation in the post-pandemic world. 

Podcasts in this series:

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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