What does the future hold for supply chain operators, and how can they prepare for what’s ahead? How do legacy apps for supply chain planning fall short, and what gaps must be filled? And what’s the role of artificial intelligence (AI) in helping supply chain leaders and their organizations to overcome critical challenges and seize new opportunities in a rapidly changing landscape?

These are significant questions, and to get substantive answers, we turned to three experts who could offer their unique perspectives as a supply chain operator, a systems integrator, and a professional dedicated to helping customers succeed in bringing AI capabilities into their supply chain operations.


We asked each of these experts to address one of these key questions. Here’s what they had to say:

The supply chain operator’s perspective:

Jay Koganti,
VP of Supply Chain COE,
Estée Lauder

Q: What does the future hold for supply chain operators, and how can they prepare for what’s ahead?

Koganti: What’s ahead? An array of significant challenges, unfortunately. Volatility and risk are on the rise for all supply chain operators — and that’s beyond the disruption we continue to experience due to COVID-19. The pandemic delivered a body blow to supply chain practices that had stood for decades.

Now, as they work to rethink how they operate their supply chains, operators must also navigate increased geopolitical risks, climate-related risks, raw material shortages and other resource constraints. Of course, customers’ heightened expectations about being able to get what they want, when they want it, despite any potential obstacles for suppliers, only amplifies the pressure.

An organization’s ability to create and maintain a resilient supply chain in this turbulent landscape hinges on whether the business can anticipate emerging risks — including financial, operational, and quality risks — and prepare options for executing plans successfully in light of those risks.

That’s why it’s becoming only more critical for supply chain operators to embrace supply chain risk intelligence. It’s a new paradigm for sustained growth — and for capturing enterprise value. It’s becoming only more essential to have a layer of intelligence on top of your planning systems, helping you manage your inventory and service in the near-term execution window. Your planning tools can’t do it alone, nor can your planners. They can’t see all the risks, and they can’t capture all the value.

Leveraging a mix of solutions, including AI and machine learning (ML), for planning orchestration is a must for driving supply chain planning transformation and innovation. That said, it’s imperative for organizations to keep a stable core for their planning so they can innovate rapidly on the outer edges while also maintaining stability in their processes.


The systems integrator’s perspective:

Tushar Bhalla,

Q: How do legacy planning apps fall short, and what critical gaps must be filled?

Bhalla: The adoption of cloud-based planning applications, like SAP Integrated Business Planning (IBP), has helped many organizations to advance key supply chain processes like demand planning and forecasting and sales and operations planning. These apps have propelled the scalability, design, and implementation associated with traditional on-premises applications, providing faster deployment options and integration access to multiple data sources. And with an integrated model, all stakeholders in sales, operations and finance have a single source of truth for planning — plus, more flexibility.

However, despite moving to the cloud, many organizations still rely on offline data and processes to address complex business rules, master data, and “planner knowledge” to execute detailed supply and production planning that’s needed to achieve end-end supply chain planning. Also, most planning systems still require the human touch — planners who can maintain the math-based master data and create the rules and strategies for decision-making.

But those rules and strategies quickly become irrelevant when unknown situations arise — like COVID-19 demand spikes, global component shortages or container availability issues. What’s needed is a way to alert supply chains well in advance that emerging risks will create impacts to customer lead times and production plan stability. And that brings us to a critical gap in supply chain planning: How can organizations ensure the plan they generate with their planning tools can be executed in real life and with the most accuracy with regard to the when, how and where of delivering their products?

Over the long term, demand and supply planning will likely become more of a “touchless” operation, with AI-based solutions enhancing the rules and data based on prediction. Advancements in AI and ML technologies and computing power provide a significant opportunity to advance demand and supply plan execution and additive capabilities such as process automation (upstream and downstream) using advanced prediction models and correlation analysis to better understand causes and effects.


The customer success expert’s perspective:

Gaurav Palta,

Q: How can AI help supply chain leaders and their organizations overcome critical challenges and seize new opportunities in a rapidly changing landscape?

Palta: Today’s turbulent environment helps to underscore the need for AI in supply chain planning. If an organization is only using deterministic, fixed rules-based math for planning for a network that’s experiencing high systemwide volatility across both demand and supply, the result is high levels of noise in their forward-looking execution planning signals. That leads to lost value in terms of sales, inventory and costs — not to mention a chaotic experience for both planners and executives.

Planning tools simply aren’t designed for the near-term planning horizon of 0-12 weeks, where planners must take a granular approach to planning to keep supply and demand in balance. Leading tools, like SAP APO/IBP, are meant for monthly operational planning. To create weekly plans, they use fixed rules to disaggregate from monthly to weekly, leading to significant errors. Daily tools do nothing to correct these errors.

So, there’s a gap in the supply chain planning stack that only AI can fill. Or, more specifically, that only a purpose-built AI application like Noodle.ai’s Inventory Flow can fill. And in the execution horizon, AI acts like the world’s best supply chain planner.

Inventory Flow helps supply chain operators and planners to eliminate blind spots in their weekly plan, providing a far more accurate view of supply and demand using a series of AI technologies that act in concert:

  • Data runners for parsing basic planning data into millions of relevant pieces, and for bringing relevant data into the planning process that planning systems can’t see otherwise.
  • Probabilistic risk visibility for uncovering risk blind spots and creating highly accurate supply and demand forecasts.
  • Risk prioritization based on the cost to the business — or Value-at-Risk (VAR).
  • Network re-balancing for evaluating millions of scenarios across the network, and for recommending actions to mitigate each risk.

Additionally, Inventory Flow solves the long tail capturing value that’s well beyond the reach of planners — by automatically course-correcting weekly plans with actions that solve millions of small risks

Once Inventory Flow has found, validated, and prioritized real risks within your network, it then helps your planners to solve all risks, no matter how large or small.  

That’s a key point: To be the world’s best planner, AI must augment human planners. Not all risks in the supply chain can be solved by humans or AI. By working alongside AI, planners can focus on the high-value risks that require human judgment — without ignoring the long tail of smaller risks that could conspire to derail the best-laid plans and result in leaving a tremendous amount of value on the table.


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