Delivering your supply chain copilot: Prioritizing areas of ROI

Understanding AI transformation

AI transformation offers you a phenomenal chance to innovate and compete with new vigor—offering previously unimaginable opportunities. It is a term you are likely to hear more over the coming years, and Microsoft aims to place a copilot on every desk, every device and across every role in support of Microsoft’s mission to empower every person and every organization on the planet to achieve more.

As part of this, Microsoft has identified four areas of opportunity for organizations to drive their AI transformation¹:

Enrich employee experiences.
Reinvent customer engagement.
Reshape business processes.
Bend the curve on innovation.

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The value of AI transformation and copilots

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The supply chain context

In an era of rapid global change, macroeconomic shifts, and geopolitical disruptions, the global supply chain faces unprecedented challenges. Simultaneously, technology is undergoing a transformation fueled by data and AI. These powerful tools and capabilities empower organizations to enhance efficiency, mitigate risk, and discover hidden opportunities.

As the world becomes increasingly complex, leading organizations are gravitating towards technology to accelerate supply chain optimization with greater speed and precision to shift the paradigm from a reactive mode of operating to one that is proactively getting ahead.

It is a foundational concept that supply chain excellence is achieved by consistently and efficiently getting the right products to the right place, in the right quantities, at the right time and at the desired quality, the first time. Doing this while respecting constraints and balancing inventory, waste, and transportation costs is what makes the work of a supply chain practitioner so difficult.

Integral to this challenge is optimized data management, real-time visibility combined with integration and interoperation across supply chain elements—such as production, logistics, procurement, partners, and customer service.

Yet so often, organizations struggle with siloed business processes, communications challenges, disconnected systems, complex planning workflows, transportation disruption, warehouse capacity issues and multiple other challenges leading to high inventory, increased costs, waste, and a lack of overall business resilience.

For a supply chain practitioner there are simply too many information sources to assimilate and consider when making better-informed decisions in real time. The practitioner can get started with a copilot to overcome fragmented data and integrate it into usable insights. Read about how Altana began overcoming fragmented knowledge—establishing a uniform understanding of the data/knowledge gap combining enterprise resource planning (ERP) systems, factory data, enriched with market and external risk factors.

The application of AI across the supply chain

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With all the focus on generative AI, it can be easy to perceive that generative AI is the answer to all your problems. This would be incorrect—as ever there are no silver bullets. AI and generative AI are distinct, yet complementary technologies used for supply chain optimization that provide the analytical horsepower to process vast amounts of data that can deliver significant impact.

Non-generative AI techniques can be used for multiple different tasks in a supply chain context, for example:

Clustering: Route planning for customer shipments and Warehouse slotting optimization.
Classification: Inventory management approaches (for example, fresh, frozen) and resource allocation.
Rules and heuristics: Inventory planning and distribution planning.
Optimization: Inventory optimization, and route optimization and network design.
Regression: Demand forecasting and supplier performance analysis.

Likewise, generative AI offers some incredible opportunities across the supply chain, which can be broadly placed into three groups:

Content generation: For example, summarizing multiple contracts and agreements associated with a given supplier.
Insight generation: For example summarizing multiple sources of external data to provide a perspective of events that could influence your demand forecast.
User Interaction: Provision of a universal interface with which supply chain practitioners interact and spans multiple systems and allows for both understanding and interaction with systems that control the supply chain.

The control tower concept

You can think of your supply chain function as a central brain orchestrating data and physical movements across your organization. This is critical work, influencing all the key metrics that drive business performance.

The concept of a supply chain control tower appeared a few years ago as a centralized system providing real-time visibility and insights across the entire supply chain. It leverages a unified data platform to deliver next-generation supply chain capabilities, beginning with end-to-end visibility and performance management.

The concept looks to incorporate data from various sources to help you monitor, manage, and optimize your supply chain operations, enabling better decision-making and more rapid responses to disruptions.

Retail supply chain management

How to use Microsoft 365 Copilot

Adding AI into this mix offers tantalizing possibilities—the ability to dramatically reduce the quantity of direct decision-making that supply chain practitioners need to be directly engaged in.

Enrich employee experiences

Generative AI is fundamentally changing how we, as individuals, relate to, and benefit from technology. While both generative AI and traditional AI contribute to supply chain optimization, generative AI emphasizes employee productivity and can work with a broader set of data, revolutionizing the types of insights you can glean with better explainability. The gamechanger here is the ability to use a conversational “agent” or copilot to navigate any task and turn data into knowledge through a conversational user interface using natural language. A copilot can enhance supply chain teams by providing real-time insights, automating routine tasks and workflows, and facilitating collaboration. For instance, it can analyze data to identify bottlenecks, suggest optimal routes for shipments, and streamline inventory management. It provides the ability to move beyond static dashboard reporting by extracting actionable insights to empower users.

A copilot for supply chain can help empower teams during their workday by converting predictive insights into specific actions while powering collaboration within a connected ecosystem.

This means organizations are better able to manage the cascading impact of their supply chain with more transparent and collaborative data sharing. Visibility improves because, where once it was restricted by the network it is now enhanced through a wider global context.

Internal data is augmented with real-time connections to partners and external signals—like geopolitical tensions, logistics challenges, and commercial factors like promotional activity or weather events. Data is continuously available and interoperable across the supply chain, giving users simultaneous access to current information, with the ability to pass on insights into the wider organization. Microsoft Teams and Microsoft 365 become engines in the connected ecosystem for greater connectivity and collaboration—empowering team members who may not be using supply chain systems—like a store manager or sales representatives—to be consumers of supply chain insights and information. This improves access to insights that are actionable at the optimal point in the value chain.

Copilots can dramatically improve productivity while accelerating decision-making. For example, take this common scenario where Hillary—an inventory analyst—needs to understand why projected cost and freight (CFR) of a key product has dropped and determine what to do to reduce impact on customer service level agreement (SLA).

Instead of compiling spreadsheets from different data sources and spending hours doing manual analysis, Hillary uses a combination of copilots and a CFR prediction algorithm to quickly identify the root cause, assess alternatives, and share the recommended approach with her manager.

Next steps to apply generative AI across your supply chain

We’ve explored some strategies for applying AI and generative AI across your supply chain, and how a supply chain copilot can support supply chain practitioners. Stay tuned for part two, where we delve into data considerations and how to get started on AI ideation for your organization.