Our daily lives are inundated with data. Alerts and notifications from email, social channels, home devices, shopping apps and other platforms compete for our attention, creating an overwhelming stream of information. This deluge makes it challenging to discern what truly matters, where and when we should apply our focus. Supply chain teams face a similar dilemma – companies are overloaded with vast amounts of data, and the ability to sift through the noise and focus on relevant insights has become a critical capability. The real-time nature of this information makes this process more difficult, creating a backlog of data that appears insurmountable.
Unprecedented levels of uncertainty and disruptions, market volatility, and rapidly evolving customer demands has exacerbated the challenge of data overload for global supply chains. Decision-makers must operate with agility and speed, often orchestrating complex scenarios across vast supply chain networks. But these efforts are frequently hampered by fragmented visibility, overwhelming data and poor data quality.
Companies must harness a wide variety of data structures and formats, spanning internal and external sources. While the abundance of data is seen as an asset, the real question is: What do you do with it? Without the ability to distinguish actionable insights from irrelevant noise, decision-makers risk inefficiency, confusion, and misallocation of resources. To break through the noise requires context.
Seeing Signals Through the Noise
In supply chain planning, separating the signal from the noise is paramount. Planners need the right information at the right time, presented with the proper context, to make meaningful decisions. Contextual intelligence creates the ability to establish relevance by integrating internal and external data, empowering teams to assess the “so what?” of events and respond accordingly.
For instance, detecting a delay in a shipment is not enough. Planners need to know: Is the delay minor or business-critical? What is the estimated duration of the delay? Who needs to act, and when? Is there an impact on service levels or product line? What are the trade-offs, and how urgent is the decision?
Without this clarity, supply chain teams may overreact to minor issues or miss opportunities to proactively address critical disruptions.
Why Context Matters
Context transforms data into actionable insights. By answering the “who, what, when, and why” questions, context enriches the decision-making process in three key ways:
1. Clarity of Impact: Context helps planners understand the significance of an event. For example, a warehouse inventory discrepancy may only matter if it affects high-priority orders or strategic customers.
2. Prioritization of Resources: Contextual intelligence differentiates between urgent and non-urgent issues, allowing teams to effectively allocate resources.
3. Stakeholder Alignment: Understanding who needs to be involved, for instance local teams versus global stakeholders, ensures timely and accurate responses.
Using AI to Enhance Context of Data
Data fuels advanced analytics, artificial intelligence (AI), and machine learning (ML) in supply chain planning. These technologies and techniques generate insights that help organizations move from reactive to proactive decision-making. Yet, without context, even the most sophisticated algorithms fall short.
The rapid evolution of generative AI (GenAI), AI agents and multiagent systems are amplifying the role of context. These agents operate collaboratively, learning from shared experiences, integrating contextual information to refine their recommendations. For example, an AI agent can detect an issue in a regional distribution center and evaluate its impact across the global network, providing planners tailored recommendations to address the disruption. This context-aware approach increases trust in AI systems, reducing reliance on manual processes and enabling faster data-driven decisions.
Context-driven decisions enable a shift toward proactive, agile planning to better navigate fast-paced environments. By integrating contextual intelligence, companies can:
– Uncover new insights, patterns of behavior and relationships
– Identify root causes or underlying issues
– Personalize and tailor the decision support to different user roles and their skill sets
– Anticipate potential disruptions to mitigate risk in advance
This also assists teams in bridging the gap between silos, ensuring collaboration with a shared understanding of priorities and trade-offs.
Reshaping Your Approach to Bring in Context
Advancements in AI, digital twins, and knowledge graphs, as an example, are reshaping traditional notions of decision making in supply chain planning; with emerging concepts and approaches such as decision-centric planning (DCP). Unlike traditional static and siloed decision-making approaches, DCP emphasis is on dynamic, decision-making that adapts to changing contexts. This approach shifts the focus from rigid schedules to the possibility of near-real-time decision making by making more connected, contextual, and continuous decisions.
The Road Ahead: Context as a Strategic Enabler
In today’s landscape, high-quality decision-making is more critical than ever and a company’s ability to apply context can be a significant competitive differentiator. To create even more business value, go beyond adopting new technologies and planning approaches; look at how you can reengineer your decisions with an emphasis on context. With this focus, you can enable broader orchestration of decision choices across the end-to-end supply chain network.
Make sure you can see the signal through the noise and start turning challenges into opportunities.
Alex Pradhan is the Global Product Strategy Leader and Member of the Executive Leadership team at John Galt Solutions. In this position, Alex is responsible for leading the strategic development and product vision of John Galt Solutions’ end-to-end supply chain planning software solution. Alex has extensive expertise at the intersection of digital, supply chain and technology and is passionate about the role that technology plays in creating resilient, high performing supply chains.
In her prior role as a Research Analyst, she advised over 1000 global companies on a range of supply chain strategic and operational topics at the intersection of digital and technology. Before this experience, Alex spent several years at Subway where she was responsible for managing demand planning for promotional, limited time offers, and R&D test products. Alex received her MBA from the University of Miami and her postgraduate degree in Data Science from the University of California, Irvine. She lives in the Miami-Ft Lauderdale area with her family.
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