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The Role of Supply Chain Planning in Today’s Complex Business Environment

Published

9 mois ago

25 juin 2025

The Role Of Supply Chain Planning In Today’s Complex Business Environment

Over the past five years, supply chains have faced unprecedented challenges. E-commerce demands, trade pressures, and increasingly complex supplier networks have necessitated executives to raise concerns about their supply chain operations. Recently, tariffs have become a focal point in major headlines, and the looming fear of a trade war persists.

Supply chain planners are now frequently tasked with initiating transformations to address these various pressures. Planners are uniquely positioned with an end-to-end focus, from procurement of materials, through manufacturing and engineering, to the movement, storage, and delivery of finished products or services. Executives are now recognizing Supply Chain Planning as an essential piece of operations for leveraging their companies’ commercial and technical resources.

Supply Chain Planning (SCP) is a critical component of Supply Chain Management (SCM), starting with the accurate forecasting of customer demand. SCP involves meticulously planning the journey of a material or product from its raw material stage to its final consumer. It establishes the foundation for the entire Supply Chain Operation and drives day-to-day decision-making across multiple functions within a company.

Key Trends Accelerating SCP Importance in the Supply Chain and Broader Business Operations:

Rise in Supply Chain Complexity:

As supply chains grow more complex, business leaders are tasked with continually finding new efficiencies while increasing responsiveness. Investing in planning enables companies to quickly identify and address constraints while consistently realigning assets to maximize value.

Managing Multifunctional Business Processes:

Multifunctional business processes can improve both top and bottom-line results. However, these processes often span multiple functions within the organization and can be difficult to oversee. Managing them requires strong technical and interpersonal skills. Planning is one of the few areas that consistently develops skills in both, resulting in planners more often leading critical business processes.

Consumer Expectations Heightened for Customized Products and Services:

Retailers aim to leverage consumer expectations for competitive advantage, often increasing or requesting highly customized SKUs, which place significant pressure on demand fulfillment systems. Traditional supply chain infrastructures, built to serve brick-and-mortar markets, are being strained by e-commerce. By developing strategies for design, supply, production, distribution, and inventory, planning provides a foundation for product innovation and plays a key role in product simplification and SKU rationalization.

Who is responsible for Supply Chain Planning?

In most organizations, supply chain planning is a centralized function carried out by supply chain planners. Supply chain planners are responsible for developing and executing the supply chain strategy, ensuring that the supply chain can meet the business’s demands. Supply chain professionals use various tools, including supply chain modeling, inventory management, and forecasting.

Supply chain planning is moving away from traditional, siloed planning processes and towards a more collaborative approach, which is made possible with solutions hosted in the cloud.

Key Components of Supply Chain Planning:

Demand Forecasting

This process involves ingesting historical data, market trends, and customer insights to predict future demand. By forecasting demand accurately, companies can ensure that they have the right amount of inventory at the right time.

Supply Chain Management

The oversight and control of all the activities required for a company to convert raw materials into finished products that are then sold to end-users. Key responsibilities include supporting continuous improvement, increasing velocity, and always seeking new technologies that could improve processes.

Risk Management:

Identifying potential disruptions, such as natural disasters, supplier disruptions, such as bankruptcies, transportation delays, and developing contingency plans to mitigate their impacts. Some solution offerings can provide “what-if” case scenarios and map out solutions.

Inventory Management:

Aims to strike a balance between supply and demand, ensuring that there is enough inventory to meet customer demand without incurring excessive carrying costs.

Sales & Operations Planning

This is an opportunity to make better decisions that key strategic chain drivers, such as customer demand, production rates, inventory status, and marketing, inform key supply chain drivers. To improve S&OP data quality must be improved, performance metrics must be rigorously defined, and company goals and objectives must be aligned to ensure there are clear roles and expectations.

The ARC Advisory Group will soon begin updating the annual Supply Chain Planning Market Analysis. This analysis is conducted by analyst Gaven Simon. If you’re interested in providing the supply chain team at ARC with a briefing to discuss any developments in your solution or the market as a whole, please reach out. (gsimon@arcweb.com)

The post The Role of Supply Chain Planning in Today’s Complex Business Environment appeared first on Logistics Viewpoints.

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Amazon Tests Structured Delivery Windows as It Repositions Speed

Published

14 heures ago

26 mars 2026

Amazon is testing a delivery model that divides the day into ten delivery windows across a 24-hour period. This follows recent efforts around sub-hour delivery and a proposed one-hour “rush” pickup model using stores such as Whole Foods Market.

The direction is straightforward: delivery speed is being segmented and potentially priced, rather than treated as a single standard.

From Uniform Speed to Tiered Service

The delivery window model introduces structured choice:

Customers select defined delivery windows

Faster or narrower windows may carry higher cost

Broader windows allow for lower-cost fulfillment

This allows Amazon to shape demand instead of only responding to it.

Operational Impact

The focus is control over network flow rather than absolute speed. With defined windows, Amazon can:

Improve route density

Reduce peak congestion

Align delivery timing with available capacity

The proposed “rush” pickup model extends this into physical locations. By combining online inventory with store stock, stores function as local fulfillment nodes.

Competitive Context

Walmart continues to expand store-based fulfillment and drone delivery. The competitive focus remains:

Proximity to demand

Flexibility in fulfillment options

Cost to serve at different service levels

Amazon’s approach emphasizes range of options rather than a single fastest promise.

Economic Model

This structure creates a clearer link between service level and cost. As supply chains become more dynamic, companies are aligning service commitments with operational constraints and capacity . Delivery windows apply that logic to the last mile.

Implications

If this model scales:

Speed becomes a selectable service level

Customer choice influences network efficiency

Pricing can be used to balance demand and capacity

The change is practical. The objective is not simply faster delivery, but more controlled execution of it.

The post Amazon Tests Structured Delivery Windows as It Repositions Speed appeared first on Logistics Viewpoints.

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NVIDIA and the Role of AI Infrastructure in Supply Chains

Published

19 heures ago

26 mars 2026

NVIDIA is not a supply chain software provider. It is part of the infrastructure layer now supporting how supply chain decisions are made.

As AI moves from isolated use cases into core operations, compute and runtime environments become part of system design. NVIDIA’s role sits at that layer.

Infrastructure, not applications

NVIDIA provides the underlying components used to build and run AI systems:

GPU hardware for model training and inference

CUDA and supporting libraries

Enterprise AI deployment software

Simulation platforms such as Omniverse

These are used by software vendors and enterprises. They are not supply chain applications themselves.

From isolated models to concurrent workloads

Earlier AI deployments in supply chains were limited to specific functions. Forecasting, routing, and warehouse automation were typically deployed independently.

With access to scalable compute, multiple models can now run in parallel and update outputs more frequently. This supports:

Continuous forecast updates

Real-time routing adjustments

Computer vision in warehouse operations

Network-level scenario modeling

The change is not the use case. It is the ability to operate them together and at higher frequency.

Planning is no longer periodic

Traditional systems operate in cycles. Data is collected, plans are generated, and execution follows. AI systems supported by GPU infrastructure operate on shorter loops.

Forecasts are updated as new data arrives

Transportation decisions adjust during execution

Inventory positions shift as conditions change

Exceptions are identified earlier

This reduces the time between signal and response.

Simulation as a planning tool

Simulation has been used in supply chains for years, but often with limited scope. GPU-based environments allow more detailed models:

Warehouse layout and flow

Distribution network scenarios

Equipment and automation performance

Platforms such as Omniverse support these use cases. The objective is to evaluate decisions before deployment.

Multi-system coordination

As AI expands across functions, coordination becomes a constraint.

Running multiple models simultaneously requires:

Sufficient compute capacity

Low-latency processing

Integration across systems

NVIDIA’s platforms are commonly used in environments where these conditions are required.

Why this matters

Supply chains are operating with higher variability across demand, supply, and cost.

Systems designed for stable conditions are less effective in this environment.

AI-based approaches increase the frequency and scope of decision-making. That depends on infrastructure capable of supporting continuous model execution.

Implications

The primary question is not whether to adopt AI, but how it is supported. This includes:

Compute availability for training and inference

Data integration across systems

Ability to run models continuously

Use of simulation in planning

AI deployment in supply chains is increasingly tied to infrastructure decisions.

The shift underway is practical. Companies are working through how to run models more frequently, connect systems more effectively, and make decisions with less delay. The enabling technologies are becoming clearer, and the path forward is less about experimentation and more about execution.

The post NVIDIA and the Role of AI Infrastructure in Supply Chains appeared first on Logistics Viewpoints.

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Designing Supply Chain Networks for Energy Volatility

Published

19 heures ago

26 mars 2026

Designing Supply Chain Networks For Energy Volatility

Energy is no longer a background cost in supply chain operations. It is becoming a primary design constraint.

For years, network design focused on labor, transportation, and inventory positioning. Energy was assumed to be stable and largely interchangeable across regions. That assumption is breaking down.

Volatility in fuel and electricity prices, combined with regulatory pressure and increasing electrification, is reshaping cost structures and operational risk. As a result, supply chain leaders are being forced to rethink how networks are designed and managed.

Energy Is Now a Structural Variable

Three forces are driving this shift:

Price volatility across fuel and grid-based energy

Regulatory pressure tied to emissions and reporting

Increased dependency from automation and electrification

In many networks, energy is now one of the most dynamic and least controlled inputs.

A network optimized for transportation cost alone may now be exposed to regional energy spikes. A warehouse automation investment may reduce labor but increase sensitivity to energy pricing. These trade-offs were not historically modeled.

From Static Models to Adaptive Networks

Traditional network design assumes relatively stable inputs and periodic optimization.

That model no longer holds.

Modern supply chains require:

Dynamic cost modeling that incorporates real-time energy inputs

Scenario-based design that accounts for regional volatility

Adaptive routing and sourcing decisions

This reflects a broader shift toward adaptive, data-driven operations described in ARC research . Energy is now one of the variables forcing that transition.

Embedding Energy Into Network Design

Leading organizations are beginning to incorporate energy directly into network decisions:

Facility Placement
Evaluating locations based on grid stability, long-term pricing, and regulatory exposure

Consumption Optimization
Managing energy usage across warehousing, transportation, and fulfillment operations

Integrated Planning
Linking energy considerations into transportation, inventory, and sourcing decisions

This moves energy from a cost line item to a system-level design factor.

Building Resilience Against Volatility

Energy introduces a new layer of operational risk:

Regional grid instability

Fuel price shocks

Regulatory shifts affecting flows and sourcing

Resilience now requires diversified network structures, flexible transportation strategies, and scenario planning that includes energy as a core variable.

The Strategic Implication

Supply chains are becoming more context-aware, adaptive, and interconnected. Energy is not a side consideration. It is a driver of network design, cost performance, and long-term competitiveness.

Organizations that incorporate energy into their network models will operate with greater stability and control. Those that do not will face increasing exposure to volatility they cannot predict or manage.