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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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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

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  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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