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The (misplaced) need for speed. Why faster supply chain planning will not get you to your destination.

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The (misplaced) Need For Speed. Why Faster Supply Chain Planning Will Not Get You To Your Destination.

Over the last two decades, there has been a significant focus on increasing the speed of supply chain planning solutions. Technology advancements in hardware, cloud, and in-memory computing fueled this increase, followed by the boost from automation, machine learning, and AI. Additionally, software vendors continuously invest in tuning the performance of their algorithms and models.

However, you may have noticed that the increase in the speed of supply chain planning solutions is not resulting in better decisions, improved performance, or much-needed supply chain agility. Instead, your planning team continues to be overworked and stressed. There is limited value to running an outdated process faster, and that value drops considerably when significant portions of the process run outside the enterprise tools. For example, running a batch process that now takes 8 hours instead of 12 does not translate into supply chain agility.

Supply chain leaders considering new supply chain planning solutions or upgrades must consider several aspects beyond speed when making investment decisions. Here are the top 4 capabilities to look for:

A better approach to scenario planning
Productized and explainable artificial intelligence (AI) and machine learning (ML)
Enhanced planner experience
End-to-end supply chain planning capabilities

A Better Approach to Scenario Planning

Scenario planning has always been a valuable tool in supply chain planning, and with the continuous rise in volatility and uncertainty, it has become critical. Scenario planning relies on the planner to identify, manually create, run, evaluate multiple scenarios to understand the trade-offs, and execute an action plan. Planners spend considerable time preparing scenario planning and not the actual analysis. Scenario plans are either too granular to offer useful insight or too wide to be effective, and the complexity increases non-linearly with the number of variables. Executing the scenario fast provides diminishing returns when non-value-added tasks take up most of the planner’s day.

For impactful scenario planning, planners must spend time on analysis rather than collating data and manually creating scenarios. When evaluating new solutions, look for tools that provide intelligent automation of activities like manually creating individual scenarios and eliminating the need to run individual scenarios sequentially. Look for capabilities that simulate a range of values for multiple variables and provide recommended scenarios based on performance against business objectives.

Productized and Explainable AI and ML

Artificial intelligence and machine learning are integral capabilities for supply chain planning. However, as in the recent case of ChatGPT incorrectly counting the number of r’s in strawberry, AI is not a panacea. Supply chain leaders must be mindful when deploying AI and not get swayed by small proofs of concept working on limited data.

When evaluating new solutions, look for scalability – can the models perform when deployed in global operations with millions of SKUs moving through complex global supply chain networks? Assess for security – can the new solutions withstand increasing cyberattacks on supply chains and continue to perform correctly? Look for explainability – can the planning solution show your planners which factors were included and the effects? Without explainability, planners and decision-makers will have difficulty trusting the AI recommendations. The lack of trust will hamper adoption and may never lead to the level of automation that AI promised. Look for solutions that provide productized AI and ML built into the product through a rigorous development process and testing, including performance, scalability, and security.

Enhanced Planner Experience

Supply chain planners are under immense pressure at work, not only do they need to navigate existing complex business environments, but they are also inundated with daily market events and disruptions that require immediate resolution. Supply chain management talent continues to be in short supply and attrition due to burn out is still high. Several factors impact the day in the life of a planner; however, one controllable factor is the amount of time spent outside the enterprise planning tool performing planning-related tasks. Planners resort to exporting plans and data to spreadsheets and BI applications for plan analysis, editing plans for downstream use, comparison, and performance analysis. Working outside the enterprise solution is time-consuming and adds several non-value-adding tasks to the planner’s day.

When evaluating new solutions, look for comprehensive inline or embedded analytics within the planning solution. Can the planning tool support analysis of the plan’s performance and variance while assessing different contingency plans to drive business outcomes? Can the solution natively support the creation of plans at varying aggregation levels and provide multiple views to support the requirements of downstream teams and enable them to move fast? Lastly, rigorously evaluate the generative AI solution for domain-specific understanding, source attribution, and enterprise security.

End-to-End Supply Chain Planning Capabilities

Supply chain planning impacts performance from the board room to the factory shop floor. The traditional siloed and sequential planning approach can no longer handle the complexity, volatility, and scale of modern supply chains. Building supply chain resilience means moving away from fragmented planning driven by mixed tools and systems and prioritizing local optimization over overall performance. A resilient supply chain requires interoperable solutions that provide end-to-end visibility and collaborative planning from IBP to factory planning.

When evaluating new solutions, look for a unified data model that combines all the supply chain planning and manages real-time event data internally and externally. Assess solutions on interoperability and not just integration – can different stakeholders across functions and regions work without friction on decision workflows? Lastly, look beyond planning for a comprehensive set of offerings that optimize the management of your complete supply chain.

Supply chain planning leaders and teams continue to deliver performance for their companies through once-in-a-lifetime events and disruptions. However, the continuous stress leads to burnout, and the general supply chain management talent shortage means you cannot just add more planners to fix the issues. Rethinking the process is an important first step, which needs to be supported by deploying technology to realize the process changes. New supply chain planning solutions must provide intelligent automation, a strong AI and ML backbone, end-to-end supply chain planning capabilities, and positively influence the planner experience by reducing tedium.

Running a flawed process faster only gets you so far. To understand the holistic capabilities to look for in a supply chain planning solution, download our eBook and assessment checklist.

BIO

Robin Bhatnagar is a seasoned supply chain and technology professional with over 14 years of experience in product marketing, field marketing, pre-sales, and consulting. As a Product Marketing Director at Blue Yonder, Robin specializes in Supply Chain Planning for Discrete Manufacturing industries. Before joining Blue Yonder, Robin held several leadership positions at Bristlecone (A Mahindra Group Company), including Head of Product Marketing, pre-sales for AI & Advanced Analytics solutions, and managing strategic product partnerships.

The post The (misplaced) need for speed. Why faster supply chain planning will not get you to your destination. appeared first on Logistics Viewpoints.

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India–U.S. Trade Announcement Creates Strategic Options, Not Executable Change

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10 heures ago

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3 février 2026

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India–u.s. Trade Announcement Creates Strategic Options, Not Executable Change

The announcement by Donald Trump and Narendra Modi of an India–U.S. “trade deal” has drawn immediate attention from global markets. From a supply chain and logistics perspective, however, the more important observation is not the scale of the claims, but the lack of formal detail required for execution.

At this stage, what exists is a political statement rather than a completed trade agreement. For companies managing sourcing, manufacturing, transportation, and compliance across India–U.S. trade lanes, uncertainty remains the defining condition.

What Has Been Announced So Far

Based on public statements from the U.S. administration and reporting by CNBC and Al Jazeera, several points have been asserted:

U.S. tariffs on Indian goods would be reduced from an effective 50 percent to 18 percent

India would reduce tariffs and non tariff barriers on U.S. goods, potentially to zero

India would stop purchasing Russian oil and increase energy purchases from the United States

India would significantly increase purchases of U.S. goods across energy, agriculture, technology, and industrial sectors

Statements from the Indian government have been more limited. New Delhi confirmed that U.S. tariffs on Indian exports would be reduced to 18 percent, but it did not publicly confirm commitments related to Russian oil, agricultural market access, or large scale procurement from U.S. suppliers.

This divergence matters. In supply chain planning, commitments only become relevant when they are documented, scoped, and enforceable.

Why This Is Not Yet a Trade Agreement

From an operational standpoint, the announcement lacks several elements required to support planning and execution:

No published tariff schedules by HS code

No clarification on rules of origin

No definition of non tariff barrier reductions

No implementation timelines

No enforcement or dispute resolution mechanisms

Without these components, companies cannot reliably model landed cost, supplier risk, or network design changes.

By comparison, India’s recently announced trade agreement with the European Union includes detailed provisions covering market access, regulatory alignment, and investment protections. Those provisions are what allow supply chain leaders to translate trade policy into operational decisions. The U.S. announcement does not yet meet that threshold.

Implications for Supply Chains

Tariff Reduction Could Be Material if Formalized

An 18 percent tariff rate would improve India’s competitive position relative to regional peers such as Vietnam, Bangladesh, and Pakistan. If implemented and sustained, this could support incremental sourcing from India in sectors such as textiles, pharmaceuticals, and light manufacturing.

For now, however, this remains a scenario rather than a planning assumption.

Energy Commitments Are the Largest Unknown

The claim that India would halt purchases of Russian oil has significant implications across energy, chemical, and manufacturing supply chains. Russian crude has been a key input for Indian refineries and downstream industrial production.

A shift away from that supply would affect energy input costs, tanker routing, port utilization, and U.S.–India crude and LNG trade volumes. None of these impacts can be assessed with confidence without confirmation from Indian regulators and implementing agencies.

Agriculture Remains Politically and Operationally Sensitive

U.S. officials have suggested expanded access for American agricultural exports. Historically, agriculture has been one of the most protected and politically sensitive sectors in India.

Any meaningful liberalization would raise questions around cold chain capacity, port infrastructure, domestic political resistance, and regulatory compliance. These factors introduce execution risk that supply chain leaders should consider carefully.

Compliance and Digital Trade Issues Are Unresolved

Several areas remain undefined:

Whether India will adjust pharmaceutical patent protections

Whether U.S. technology firms will receive exemptions from digital services taxes

Whether labor and environmental standards will be linked to market access

Each of these issues influences sourcing strategies, contract terms, and long term cost structures.

Practical Guidance for Supply Chain Leaders

Until formal documentation is released, a measured approach is warranted:

Avoid making structural network changes based on political announcements

Model tariff exposure using multiple scenarios rather than a single assumed outcome

Monitor customs and regulatory guidance rather than headline statements

Assess exposure to potential energy cost changes in Indian operations

Track implementation of the India–EU agreement as a near term reference point

Bottom Line

This announcement suggests a potential shift in the direction of India–U.S. trade relations, but it does not yet provide the clarity required for operational decision making.

For now, it creates strategic optionality rather than executable change.

Until tariff schedules, regulatory commitments, and enforcement mechanisms are formally published, supply chain and logistics leaders should treat this development as informational rather than actionable. In trade, execution begins only when the documentation exists.

The post India–U.S. Trade Announcement Creates Strategic Options, Not Executable Change appeared first on Logistics Viewpoints.

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Winter weather challenges, trade deals and more tariff threats – February 3, 2026 Update

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15 heures ago

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3 février 2026

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Winter weather challenges, trade deals and more tariff threats – February 3, 2026 Update

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Published: February 3, 2026

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Weekly highlights

Ocean rates – Freightos Baltic Index

Asia-US West Coast prices (FBX01 Weekly) decreased 10% to $2,418/FEU.

Asia-US East Coast prices (FBX03 Weekly) decreased 2% to $3,859/FEU.

Asia-N. Europe prices (FBX11 Weekly) decreased 5% to $2,779/FEU.

Asia-Mediterranean prices(FBX13 Weekly) decreased 5% to $4,179/FEU.

Air rates – Freightos Air Index

China – N. America weekly prices increased 8% to $6.74/kg.

China – N. Europe weekly prices decreased 4% to $3.44/kg.

N. Europe – N. America weekly prices increased 10% to $2.53/kg.

Analysis

Winter weather is complicating logistics on both sides of the Atlantic. Affected areas in the US, especially the southeast and southern midwest are still recovering from last week’s major storm and cold.

Storms in the North Atlantic slowed vessel traffic and disrupted or shutdown operations at several container ports across Western Europe and into the Mediterranean late last week. Transits resumed and West Med ports restarted operations earlier this week, but the disruptions have already caused significant delays, and weather is expected to worsen again mid-week.

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The resulting delays and disruptions could increase congestion levels at N. Europe ports, but ocean rates from Asia to both N. Europe and the Mediterranean nonetheless dipped 5% last week as the pre-Lunar New Year rush comes to an end. Daily rates this week are sliding further with prices to N. Europe now down to about $2,600/FEU and $3,800/FEU to the Mediterranean – from respective highs of $3,000/FEU and $4,900/FEU in January.

Transpacific rates likewise slipped last week as LNY nears, with West Coast prices easing 10% to about $2,400/FEU and East Coast rates down 5% to $3,850/FEU. West Coast daily prices have continued to slide so far this week, with rates dropping to almost $1,900/FEU as of Monday, a level last seen in mid-December.

Prices across these lanes are significantly lower than this time last year due partly to fleet growth. ONE identified overcapacity as one driver of Q3 losses last year, with lower volumes due to trade war frontloading the other culprit.

And trade war uncertainty has persisted into 2026.

India – US container volumes have slumped since August when the US introduced 50% tariffs on many Indian exports. Just this week though, the US and India announced a breakthrough in negotiations that will lower tariffs to 18% in exchange for a reduction in India’s Russian oil purchases among other commitments. President Trump has yet to sign an executive order lowering tariffs, and the sides have not released details of the agreement, but once implemented, container demand is expected to rebound on this lane.

Recent steps in the other direction include Trump issuing an executive order that enables the US to impose tariffs on countries that sell oil to Cuba, and threatening tariffs and other punitive steps targeting Canada’s aviation manufacturing.

The recent volatility of and increasing barriers to trade with the US since Trump took office last year are major drivers of the warmer relations and increased and diversified trade developing between other major economies. The EU signed a major free trade agreement with India last week just after finalizing a deal with a group of South American countries, and other countries like the UK are exploring improved ties with China as well.

In a final recent geopolitical development, Panama’s Supreme Court nullified Hutchinson Port rights to operate its terminals at either end of the Panama Canal. The Hong Kong company was in stalled negotiations to sell those ports following Trump’s objection to a China-related presence in the canal. Maersk’s APMTP was appointed to take over operations in the interim.

In air cargo, pre-LNY demand may be one factor in China-US rates continuing to rebound to $6.74/kg last week from about $5.50/kg in early January. Post the new year slump, South East Asia – US prices are climbing as well, up to almost $5.00/kg last week from $4.00/kg just a few weeks ago.

China – Europe rates dipped 4% to $3.44/kg last week, with SEA – Europe prices up 7% to more than $3.20/kg, and transatlantic rates up 10% to more than $2.50/kg, a level 25% higher than early this year.

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Judah Levine

Head of Research, Freightos Group

Judah is an experienced market research manager, using data-driven analytics to deliver market-based insights. Judah produces the Freightos Group’s FBX Weekly Freight Update and other research on what’s happening in the industry from shipper behaviors to the latest in logistics technology and digitization.

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Microsoft and the Operationalization of AI: Why Platform Strategy Is Colliding with Execution Reality

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2 février 2026

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Microsoft And The Operationalization Of Ai: Why Platform Strategy Is Colliding With Execution Reality

Microsoft has positioned itself as one of the central platforms for enterprise AI. Through Azure, Copilot, Fabric, and a rapidly expanding ecosystem of AI services, the company is not merely offering tools, it is proposing an operating model for how intelligence should be embedded across enterprise workflows.

For supply chain and logistics leaders, the significance of Microsoft’s strategy is less about individual features and more about how platform decisions increasingly shape where AI lives, how it is governed, and which decisions it ultimately influences.

From Cloud Infrastructure to Operating Layer

Historically, Microsoft’s role in supply chain technology centered on infrastructure and productivity software. Azure provided scalable compute and storage, while Office and collaboration tools supported planning and coordination. That boundary has shifted.

Microsoft is now positioning AI as a horizontal operating layer that spans data management, analytics, decision support, and execution. Azure AI services, Microsoft Fabric, and Copilot are designed to work together, reducing friction between data ingestion, model development, and business consumption.

The implication for operations leaders is subtle but important: AI is no longer something added to systems; it is increasingly embedded into the platforms those systems rely on.

Copilot and the Question of Decision Proximity

Copilot has become a focal point of Microsoft’s AI narrative. Positioned as an assistive layer across applications, Copilot aims to surface insights, generate recommendations, and automate routine tasks.

For supply chain use cases, the key question is not whether Copilot can generate answers, but where those answers appear in the decision chain. Insights delivered inside productivity tools can improve awareness and coordination, but operational value depends on whether recommendations are connected to execution systems.

This highlights a broader pattern: AI that remains advisory improves efficiency; AI that is embedded into workflows influences outcomes. Microsoft’s challenge is bridging that gap consistently across heterogeneous enterprise environments.

Microsoft Fabric and the Data Foundation Problem

Microsoft Fabric represents an attempt to simplify and unify the enterprise data landscape. By combining data engineering, analytics, and governance into a single platform, Microsoft is addressing one of the most persistent barriers to AI adoption: fragmented and inconsistent data.

For supply chain organizations, Fabric’s value lies in its potential to standardize event data across planning, execution, and visibility systems. However, unification does not eliminate the need for data discipline. Event quality, latency, and ownership remain operational issues, not platform features.

Fabric reduces friction, but it does not resolve governance by itself.

Integration with Existing Enterprise Systems

Microsoft’s AI strategy assumes coexistence with existing ERP, WMS, TMS, and planning platforms. Integration, rather than replacement, is the dominant pattern.

This creates both opportunity and risk. On one hand, Microsoft can act as a connective tissue across systems that were never designed to work together. On the other, loosely coupled integration increases dependence on interface stability and data consistency.

In execution-heavy environments, even small integration failures can cascade quickly. As AI becomes more embedded, integration reliability becomes a strategic concern.

Where AI Is Delivering Value, and Where It Isn’t

AI deployments tend to deliver value fastest in areas such as demand sensing, scenario analysis, reporting automation, and exception identification. These use cases align well with Microsoft’s strengths in analytics, collaboration, and scalable infrastructure.

Where value is harder to realize is in autonomous execution. Closed-loop decision-making that directly triggers operational action requires tighter coupling with execution systems and clearer decision ownership.

This reinforces a recurring theme: platform AI accelerates insight, but execution still depends on operating model design.

Constraints That Still Apply

Despite the breadth of Microsoft’s AI portfolio, familiar constraints remain. Data quality, security, compliance, and organizational readiness continue to limit outcomes. AI platforms do not eliminate the need for process clarity or decision accountability.

In some cases, the ease of deploying AI services can outpace an organization’s ability to absorb them operationally. This creates a risk of insight saturation without action.

Why Microsoft Matters to Supply Chain Leaders

Microsoft’s relevance lies in its ability to shape the default environment in which enterprise AI operates. Platform decisions made today influence data architectures, governance models, and user expectations for years.

For supply chain leaders, the key takeaway is not to adopt Microsoft’s AI stack wholesale, but to understand how platform-level AI affects where intelligence sits, how it flows, and who ultimately acts on it.

The next phase of AI adoption will not be defined solely by model performance. It will be defined by how effectively platforms like Microsoft’s translate intelligence into operational decisions under real-world constraints.

The post Microsoft and the Operationalization of AI: Why Platform Strategy Is Colliding with Execution Reality appeared first on Logistics Viewpoints.

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