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Do You Have The Right Data Foundation To Enable Generative AI-Powered Control Towers or Digital Twins?

August 8, 2023 | Manju Devadas

Blog / Do You Have The Right Data Foundation To Enable Generative AI-Powered Control Towers or Digital Twins?

Key Takeaways

  • Market-Tested Solution for Tackling Disruptions:  Supply Chain Control Tower (SCCT) leverages advanced technologies to provide businesses with real-time insights across their entire supply chain. This helps them navigate through volatility and disruptions more effectively.
  • Requires the right Data Foundation to provide value: SCCT is a build solution, meaning it must be customized according to specific needs. Without the right data foundation, customization and effective implementation are not possible.
  • Blend of Control Tower Capabilities with Generative AI: Modern businesses are looking for solutions that blend control tower capabilities with the decision-making power of Generative AI, offering a new level of supply chain control and intelligence. Pluto7’s Generative AI-enabled control tower solutions seamlessly blend into existing customer capabilities and workflows
  • Diverse Industry Applications: Pluto7’s Data Platform Planning in a Box can optimize inventory management, supplier performance, and customer experience across various industries. It’s particularly effective in Manufacturing, Retail, and CPG sectors.
  • Use-Case Driven Approach: By implementing real-time analytics, strategic planning and execution become more closely connected across marketing, sales, supply chain, and finance. This connection is achieved one use case at a time, such as inventory positioning, and extends to other connected use cases like demand sensing and demand forecasting.
  • Quick Returns: Customer see signs of immediate value in 4 weeks and then observe significant ROI within six months or less by reducing planning and execution gaps, improving shipping efficiency, optimizing inventory management, and enhancing supplier performance.
  • Hands-On Workshop: Pluto7 is offering workshops that provide a hands-on experience of using a Generative AI-powered SCCT, with live use-cases demonstrated on Google Cloud, focusing on practical considerations to guide successful implementation and avoid common pitfalls. Sign up to participate and witness the potential firsthand.

Unpredictable global events continue to rewrite the rules of the game. Navigating a pandemic’s aftermath, grappling with volatile trade scenarios, adapting to abrupt regulatory changes, or mitigating climate-induced supply risks are daily realities. 

In this whirlwind of change, one crucial question arises – How can businesses retain their agility, swiftly pivot strategies, and continuously meet their consumers’ evolving expectations?

Supply Chain Leaders today are grappling with a set of pressing concerns:

  • Swift Response to Market Shifts: How can we anticipate market changes and respond in real-time to protect our bottom line?
  • Enhancing Customer Service: How can we deliver on customer expectations consistently amidst volatility and disruption?
  • Achieving Greater Visibility: How can we obtain a clear, end-to-end view of our supply chains in real-time to make informed decisions?
  • Breaking Decision Paralysis & Data Silos: How can we overcome data fragmentation and indecision to accelerate our decision-making process?
  • Harnessing Emerging Tech: How can we leverage advanced technology to drive quick returns on investments and drive competitive advantage?

These challenges have one common solution – a  Supply Chain Control Tower (SCCT).

A Supply Chain Control Tower is a centralized hub, powered by advanced technologies such as AI and Machine Learning. It offers end-to-end visibility into your supply chain, thus providing an accurate, real-time snapshot of your entire operations. More importantly, an SCCT can offer predictive insights, enabling proactive and informed decision-making. 

Transform Your Supply Chain Management

Build seamless and intelligent supply chain with Control Tower

Meet Planning in a Box: The Generative AI-Powered Control Tower on Google Cloud

  • Merge isolated data streams into a single, powerful data platform.
  • Position inventory with marketing, sales, and finance data.
  • Apply the right security with data management on Google Cloud.
  • Leverage Generative AI to get answers to your mission-critical challenges.
  • Combine internal data with 250+ external demand signals for accurate demand forecasts. 

Key Features of Planning in a Box:

  • Flexible Data Platform: Utilizing Google Cloud’s robust components, it creates a data platform that is both secure and scalable.
  • Glass-Box Methodology: Offering tweakable transparency that’s ready to adapt to your business needs.
  • Decision Intelligence: A Generative AI-powered decision layer that answers your complex queries without the need for SQL.
  • Multi-source Support: Designed to smoothly integrate with a wide range of data sources, including SAP, Salesforce, Oracle, and thousands of others.
  • Industry Recognition: Trusted by industry giants like Gartner and Google Cloud, offering you a solution that’s recognized for its efficiency and effectiveness.

Read: Harnessing the power of Decision Intelligence Platforms

Creating an effective Supply Chain Control Tower isn’t always straightforward, especially when the underlying architecture lacks robustness. Planning in a Box helps businesses overcome these challenges. 

It taps into the tried-and-tested components of Google Cloud to construct a flexible data platform. This platform is complemented by a glass-box methodology, providing a level of transparency that allows for clear insights and adjustments to meet specific needs.

What truly sets it apart is the decision layer powered by Generative AI, enabling you to ask questions and receive insights without delving into complex SQL queries.

Planning in a Box Isn’t an Off-the-Shelf Solution

Unlike standardized solutions, Planning in a Box is a data platform that can be shaped to fit your unique business needs. For example, a manufacturing company facing challenges in inventory positioning can customize Planning in a Box to integrate specific data sources and apply tailored analytical models. 

Think of it as the play-dough of supply chain solutions. You can mold and shape it into whatever form best fits your business. Need to optimize inventory in a unique retail environment? Planning in a Box can be shaped to do that. Want to enhance supplier performance in a complex manufacturing process? It’s up for the challenge.

In the hands of your team, Planning in a Box becomes a living, breathing extension of your business strategy, capable of shifting, adapting, and evolving as you do.

The Right Data Foundation Makes All the Difference

Many advanced technological solutions fall short of their promise, primarily because they lack a solid data foundation. Planning in a Box isn’t confined by these limitations. 

Leveraging the robust architecture of Google Cloud Cortex, it can integrate a wide range of datasets from Salesforce, Google Ad Tech, SAP, Oracle EBS, NetSuite, Infor, and 250+ external sources. This isn’t just about collecting data; it’s about organizing and making it accessible for precise analytics. 

Imagine having a disparate inventory system spread across multiple locations. 

With Planning in a Box, you can consolidate all this information, analyze trends, and derive actionable insights to optimize inventory positioning. No more guesswork or manual analysis. The right data foundation transforms the way you make decisions, leading to more effective strategies and better results. That’s power you can’t afford to ignore.

A Use-Case Driven Approach: Understanding Your Challenges from Day One

Planning in a Box’s use-case driven approach means it’s built from the ground up to understand specific problems that companies commonly face. 

You don’t need to spend countless hours training the system; it already knows the challenges, and all you have to do is provide your unique context. Try it yourself. 

For example: Imagine a large retailer struggling with inventory overstock in certain locations while facing shortages in others. A generic system might require extensive customization and months of training. 

Planning in a Box comes pre-equipped with detailed use-cases for inventory management. It recognizes the retailer’s problem and, with minimal input, aligns inventory across locations by comprehending regional sales trends, supplier lead times, and seasonal demands.

This use-case driven approach minimizes the time to implement, maximizes relevance to your specific challenges, and brings about quicker returns

Intelligent Decisions in Weeks, Not Months

With Planning in a Box, you don’t have to wait months to see value. Its synergy with Google Cloud and a robust data foundation allows for immediate integration into your existing workflows.

Example: A manufacturing company needed a solution for predictive maintenance of its machinery. With Planning in a Box, the necessary data foundation was laid, and predictive algorithms were up and running in just 4 weeks. This quick implementation led to minimized downtime and a significant reduction in maintenance costs.

Read the Case Study here. 

Whether it’s inventory management or predictive maintenance, Planning in a Box is designed to deliver actionable insights quickly, transforming your decision-making process without the traditional delays.

Industry Use Cases for Supply Chain Control Tower 

Manufacturing: Redefining Efficiency and Agility

Use Cases:

  • Predicting and preventing equipment downtime for better factory efficiency.
  • Monitoring supplier performance to secure raw material supply.
  • Aligning production plans with demand forecasts to avoid surplus inventory.
  • Finding the best shipping routes and methods for cost-effective logistics.
  • Real-time monitoring of production for quality and compliance.

Insights You Can Extract From Data:

  • Which machine is most likely to require maintenance in the next month?
  • Which supplier is consistently failing to meet delivery timelines?
  • Are we producing more than what is forecasted for the next quarter?
  • What is the most cost-effective and fastest shipping route for destination X?
  • How are we performing on quality metrics across different production lines?

Impact on the Bottom Line

  • Implementation Time: 3 months
  • Time to Value: 6 months
  • Workforce Efficiency Boost: Up to 20%
  • Equipment Downtime Reduction: A 20% decrease, leading to an approximate savings of $1.5M annually, based on an average downtime cost of $7,500 per hour.
  • Shipping Efficiency: A 15% improvement, translating to annual logistics cost savings of around $2M for a company with average shipping costs of $15M.
  • Time Saved: Approximately 500 hours annually
  • Approximate ROI: 1.5-2x in the first year

Read Case Study: Lixil, a market leader in the plumbing fixtures industry reduced manufacturing waste by enabling Predictive Maintenance for defect detection.


Retail: Empowering Customer-Centric Supply Chains

Use Cases:

  • Optimizing inventory levels across all store locations to meet customer demand.
  • Predicting seasonal trends for effective merchandise planning.
  • Enhancing omnichannel operations for seamless shopping experiences.
  • Analyzing customer behavior for personalized marketing and promotions.
  • Mitigating supply chain disruptions to ensure continuous product availability.

Insights You Can Extract From Data:

  • What products are likely to be out of stock in Store X in the next week?
  • When should we initiate the next inventory replenishment cycle for Store Y?
  • Should we adjust the price for Product Z based on current market trends?
  • What products should we recommend to a customer who frequently buys Product A?
  • Are there any discrepancies in stock levels across our online and offline channels?

Impact on the Bottom Line

  • Implementation Time: 2-4 months
  • Time to Value: 6-8 months
  • Workforce Efficiency Boost: 15-20%
  • Inventory Management: A 10% decrease in excess inventory, freeing up around $500K in tied-up capital for a company with an average of $5M in excess stock.
  • Reduction in Stock-outs: 25%
  • Time Saved: Approximately 300 hours annually
  • Approximate ROI: 1.8-2.2x in the first year

Read Case Study:  A Global Beauty Brand transformed its inventory and supply chain management with data-driven innovations.


CPG: Navigating Volatile Demand and Supply

Use Cases:

  • Streamlining SKU portfolio to avoid stockouts and overstocks.
  • Tracking promotional effectiveness in real-time for optimized marketing efforts.
  • Aligning production, logistics, and demand planning to minimize out-of-stock scenarios.
  • Enhancing supplier reliability and mitigating procurement risks.
  • Offering personalized customer experiences through targeted product offerings.

Insights You Can Extract From Data:

  • How is the upcoming local football championship in Region B likely to impact the demand for our line of sports drinks?
  • Are all our raw material suppliers adhering to the agreed-upon quality and safety standards for our organic cereals?
  • Should we redistribute stock from Warehouse D to E to prevent potential stock-outs of our popular haircare range?
  • What kind of promotions should we run for Retailer F based on their customer demographics for our urban fashion line?
  • How is our new skincare product performing in the market compared to our initial forecasts?

Impact on the Bottom Line

  • Implementation Time: 3-4 months
  • Time to Value: 6 months
  • Workforce Efficiency Boost: Up to 25%
  • Supplier Performance: Improved supplier performance tracking has reduced supply disruptions by 30%, saving potential lost sales worth around $3M annually.
  • Improvement in Forecast Accuracy: 30%
  • Time Saved: Approximately 400 hours annually
  • Approximate ROI: 1.6-2.1x in the first year

Read Case Study: A Leading American Department Store Retail Chain gained more than 85% of forecast accuracy


Note: The figures are based on our experience and general industry standards. The actual performance may vary depending on the specific context and needs of each organization.

Planning in a Box is aimed at accelerating your journey towards a resilient, efficient, and agile supply chain.

Best Practices for Implementing a Generative AI-Driven Supply Chain Control Tower

In your journey to implement a Generative AI-driven Supply Chain Control Tower, consider these best practices to unlock the true potential of your supply chain:

Embrace the Use-Case Approach: Begin with identifying specific business challenges where immediate gains can be achieved. This approach allows for quick wins and provides a pathway to broader enterprise-wide adoption.

Customizability over Off-the-Shelf Products: A one-size-fits-all model seldom works in supply chain scenarios with their unique dynamics. Opt for flexible, customizable solutions like Planning in a Box that can adapt to your specific requirements and scale as you grow.

Establish a Strong Data Foundation: Cutting-edge technologies like Generative AI need a robust data infrastructure to function effectively. Ensure you have a data foundation that is capable of supporting these advanced solutions.

Never Overlook Data Quality: Poor data quality can severely impact the accuracy of AI-generated insights. Leverage frameworks like Google Cloud’s Cortex Framework, which Planning in a Box uses, to ensure your data models are high-quality and accurate. This framework enables seamless replication of SAP, Salesforce, and other data sets on Google Cloud, ensuring data integrity and facilitating data-driven decision-making.

Remember, the aim is not just to have a Supply Chain Control Tower but to create an intelligent, dynamic, and resilient control tower powered by Generative AI. This paves the way for a future-ready supply chain capable of thriving amidst uncertainties.

Accelerate Global Impact: From Workshop to Wide-Scale Deployment

Real transformation begins with small, focused initiatives, before amplifying their impact on a global scale. Our workshops are designed as an immersive experience, diving deep into live use cases and best practices of implementing a Generative AI-driven Supply Chain Control Tower. You’ll see the potential of the technology firsthand and understand how it can be integrated into your specific context. Reserve your spot.

The truly transformative part of this journey comes after the workshop. The pilots developed during these sessions are not stand-alone projects, but blueprints for wide-scale deployment. Using a highly scalable data platform like Planning in a Box, these pilot projects can be replicated swiftly across multiple geographies, facilitating a consistent, data-driven approach across your entire supply chain.

The goal? Achieving a breakthrough in just six months or less. With a strong foundation, customizable solutions, and a data-driven approach, you can roll out an intelligent, dynamic, and resilient control tower that is not just ready to serve your current needs but is also future-ready.

For a more strategic conversation about your specific supply chain challenges and how data-driven solutions can be the key to overcoming them, feel free to connect with me on LinkedIn.


Manju Devadas is the Founder and CEO of Pluto7, bringing 20+ years of experience in predictive analytics for Supply Chain, Retail and Manufacturing. With expertise in AI, Deep Learning, and Machine Learning, he has been instrumental in improving efficiency and strategic growth across industries.

Connect with Manju on LinkedIn