Agility helps retail and consumer goods supply chains:

• Quickly switch suppliers, develop more flexible sourcing strategies, and mitigate disruptions from potential tariffs¹
• Adapt product offerings and pricing strategies to combat the lingering effects of inflation
• Adopt more real-time demand forecasting tools and flexible warehousing solutions to keep up with shopping patterns
• Augment human labor with automation to improve productivity and address labor shortages

Retail and consumer goods organizations that develop greater agility will catapult themselves forward by using insights from their supply chains as a critical enabler.

Nonetheless, many retailers’ supply chains struggle with agility because warehouse data is often still on-premises—and that’s holding them back from the latest technologies. Because data is central to all business processes, it’s data that either fuels or inhibits supply chain growth. Reliance on on-premises data and legacy systems likely inhibits supply chain growth because it:

• Causes latency that slows decision-making since leaders lack access to real-time data and often rely on outdated snapshots of old data
• Prevents visibility and collaboration since data is often fragmented and siloed
• Limits scale because systems can’t efficiently process increased data volumes and fluctuating demand
• Impedes flexibility when systems can’t adapt quickly to shifting market conditions and demand
• Impairs adoption of new technologies and processes when existing platforms aren’t adaptable

The warehouse is the ideal starting place for increased digitalization because investments made at the warehouse create value that extends to other parts of the supply chain and enterprise.

Digitalizing the warehouse enables operational excellence and innovation through:

• Data-driven decision-making through real-time insights that help managers make more informed decisions and get teams unified around the same information so retailers can get ahead of demand.
• Reduced operating costs related to warehousing operations through enhanced efficiencies gained by automation and robotics—and improved warehouse throughput through layout optimization, labor efficiencies, and automation. This includes reduced time and labor required for tasks such as picking, packing, and shipping.
• Seamless integration throughout supply chain systems, such as enterprise resource planning (ERP) and warehouse management systems. It also sets the stage for other powerful capabilities, such as intelligent stores.
• More scalability, making it easier for retailers to handle seasonal demand fluctuations or rapid growth without disrupting operations.

Agility helps supply chain leaders drive operational excellence and innovation. Nothing enables that level of agility like the cloud. Here are three compelling reasons to start digitizing your warehouse today with Microsoft and its partner ecosystem.

1. Help warehouse managers drive operational excellence with agentic AI

The role of the warehouse manager is pivotal in the supply chain ecosystem, yet warehouse managers are overloaded with information from multiple sources, making it hard to parse what’s relevant and useful.

Blue Yonder’s warehouse manager AI agent offers an easy-to-digest, interactive report designed to help warehouse managers stay up to date with the most important data and information. The agent delivers those key insights when they’re needed, helping ensure operational excellence every day.

Instead of sifting through hundreds of charts and dashboards, pages and pages of report analysis, or piecing together fragments of information from their teams, warehouse managers get a simplified view of what’s happening, what caused the issue, and what to do about it.

It’s like having a personal analyst working alongside the warehouse manager who knows all about their role, their company, and warehouse. That partnership helps the manager move much more quickly from information overwhelm to clear, decisive action.

Blue Yonder expects more developments coming soon, including more data highlights, summaries, and suggested actions, as well as an expanding list of tasks the agents can perform with human guidance.

2. Optimize warehouse design, planning, and operations with simulation

Today’s customers expect retailers to have what they want and deliver it fast to their store or home. Warehouses are critical nodes in the supply chain where optimizations can improve growth and profitability. From receiving shipments to sorting, picking, and packaging, every step of warehouse operations is being modernized with AI that analyzes changes in the physical world.

Simulating facility designs and layouts, processes, and discrete events in fulfillment and distribution centers helps retail and consumer goods enterprises make more informed and faster decisions without the need to physically install systems to evaluate use cases. Simulation also lets enterprises create and use synthetic data to orchestrate between manual labor and automation systems applying AI, machine learning, robotics, sensor technology, management systems, cloud platforms, and data analytics. How can warehouses achieve operational excellence at every step of the orchestration?

NVIDIA Omniverse is a platform for developing and deploying physical AI and simulation applications for industrial digitalization. Developers use Universal Scene Description (OpenUSD) to build solutions on a platform that enables warehouse scale, digital twins, and simulations to optimize layouts and achieve operational efficiencies. These digital twins also serve as virtual training grounds for autonomous systems and robotic fleets that increasingly operate inside these facilities.

Today, leading retailers and consumer goods companies use applications and solutions built on NVIDIA Omniverse to design and simulate greenfield and brownfield warehouses from scratch, establishing an optimal layout and process flow all in a physically accurate digital space. They can evaluate technologies like robotic shelving systems, robotic grid-based storage, or vertical lift modules (VLM) for high-density storage.

Solutions built on Omniverse let retailers integrate data from different enterprise and industrial systems to create, test, and measure design, process, and operational twins before spending precious capital or stepping foot in the building. For greenfield sites, this means a fully optimized virtual version of the entire design before construction begins. For brownfield sites, retailers can seamlessly integrate new automation technologies with existing systems, ensuring the entire warehouse achieves its operational benchmarks and performs as one cohesive unit.

Applications developed with the Omniverse platform also allow supply chain leaders to understand the impact of discreet events that impact efficiency so they can make decisions that improve key performance metrics like warehouse throughput without the risk of costly physical trials.

In the fast-paced world of commerce, time to value is everything. But platform technologies are never the end-all, be-all. That’s why collaborating with the right partners and experts is crucial for retail and consumer goods enterprises. By bringing together integration partners like Accenture to simplify the development and implementation of end-to-end advanced automation and robotics solutions and services, Microsoft’s powerful cloud solutions, and NVIDIA’s cutting-edge accelerated computing, AI, and simulation platforms, retailers can accelerate warehouse transformation and realize value faster than ever.

3. Boost productivity and collaboration with robotics-enabled automation and intelligent orchestration

Warehouse managers have traditionally relied on manual processes and human labor to keep their operations running smoothly. But labor shortages and rising operational costs are making it increasingly difficult to maintain efficiency and productivity. Additionally, the complexity of managing inventory and ensuring timely order fulfillment often leads to bottlenecks and errors.

Advancements in robotics can help supply chains augment staffing, improve employee safety, and drive warehouse productivity. New capabilities are emerging every day and startups are the ones embracing these new capabilities.

Intelligent orchestration and sortation with Unbox Robotics

The last mile can be a significant chunk of the cost in getting the supply chain right. Unbox Robotics is one of hundreds of startups Microsoft works with to deliver retail supply chain solutions. Unbox Robotics can help automate the last mile process by using robots and swarm intelligence that mimics what a swarm of bees or ants do by carrying goods from one place to another. These robots pick items, sort them, and put them in one lot lightning fast so they can easily be picked up and delivered. And because robots can work around the clock, Unbox Robotics can help retailers offset labor challenges with “always on” reliability.

Smart redistributions with YDISTRI—a new era in inventory optimization

Even the best demand forecasting systems can’t fully prevent real-time overstock and understock issues. YDISTRI doesn’t compete with these systems—it complements them by providing an AI-based reactive inventory redistribution solution. For example, in a supermarket chain, YDISTRI analyzes sales patterns, local demand, and product turnover to identify overstocked items—such as specialty foods or seasonal goods—and moves them to stores where they will sell faster at full price, reducing markdowns and waste.

By weighing transfer costs against the risk of discounts or write-offs, YDISTRI helps retailers maximize revenue from existing stock, improving inventory efficiency without relying on heavy markdowns.

Bend the curve on innovation by digitalizing your warehouse in 2025

Improving agility gives retailers the ability to future-proof their business, flex and scale their operations, and be more responsive and adaptive to consumer demands. Supply chain leaders can achieve operational excellence and catapult themselves forward with generative AI, digital twins, and robotics.

Microsoft partners with Blue Yonder, an organization that provides complete solutions across the entire supply chain, and with hundreds of today’s most innovative startups to complement a retailer’s existing technologies. Start using your supply chain as a business enabler by digitalizing your warehouse in 2025 and gain more agility for years to come.

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Learn more

• Microsoft’s responsible AI principles

• Enhancing supply chain efficiency in the retail and consumer goods industry with agentic systems

• Delivering your supply chain copilot: Prioritizing areas of ROI

• Delivering your supply chain copilot: Getting started on ideation

¹ “Tariffs: What Retailers Need to Know,” Bain & Company, January 2025.

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Retailers and consumer goods companies have faced constant change, particularly in supply chains. New sales and distribution models, such as online sales, omnichannel approaches, direct-to-consumer sales, and complex ecosystems, have evolved. External disruptions are frequent, with 90% of leaders reporting supply chain challenges in 2024¹. 

Supply chain agility and resiliency rely on fast and accurate decision making. Poor decisions or slow responses lead to missed promises, negatively impacting revenue and customer satisfaction, and increasing costs due to inefficient shipments and higher inventory levels. 

To address these challenges, there is an urgent need to improve both the quality and speed of decision making in supply chain management. 

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Enter agents and agentic systems

Agentic systems offer a revolutionary opportunity to enhance decision making quality and speed. Triggered by business events, agents collect and analyze relevant data to either act directly or recommend actions. 

Microsoft announced the ability to build autonomous agents using Microsoft Copilot Studio during Microsoft Ignite in October 2024. In a supply chain context, this capability could, for example, allow for the identification and action upon alternative supply sources in the event of a delayed shipment, with minimal human intervention. 

Overview of agentic systems 

In the context of agentic systems, an agent refers to a system capable of autonomous decision making and action. These systems can pursue goals independently without direct human intervention. Agentic systems have the following characteristics: 

• Autonomy. They operate independently, making decisions and executing tasks without human oversight, escalating to a human when necessary. 
• Context aware. They interpret data and adjust actions accordingly. 
• Goal orientation. They can aim to achieve specific objectives. 
• Learning. They enhance their performance by using new data and past outcomes. 
• Reasoning and decision making. Agents use reasoning to process information, infer relationships, and make decisions. 
• Perception and sensing. Agents perceive their environment through sensors or other means, which allows them to be triggered by changes in the process.  
• Skills and capabilities. Agents possess specific skills or capabilities to perform tasks. These skills can be learned or programmed.   
• Memory. An agent’s memory stores relevant information for decision making and future actions. 

Agents can be programmed to pursue specific objectives once activated. For instance, when searching for an alternative supply source, they can prioritize cost minimization rather than selecting the first available option. 

Agents are already delivering value for customers—for example, one customer has autonomous agents reviewing shipping invoices with more use cases planned. Over time, agents can be developed for various tasks across the organization, with Microsoft Copilot serving as the ‘UI for AI’.  

Have we heard this before? 

This may sound like RPA (Robotic Process Automation). You might also question how an agent differs from a copilot. 

RPA employs rules-based automation, while agents enhance this capability by reasoning over data and using large language models (LLMs) to extract relevant information from extensive datasets. Whereas an RPA-based solution is rigid in terms of the scenarios that it can address and requires programming to make changes, an agent-based process automation solution can learn and improve over time, resulting in more effective outcomes. 

Agents operate autonomously, unlike copilots who assist users in real-time. An agent can work within Copilot, aligning with the Microsoft vision of Copilot as the UI for AI. In the future, users will have one copilot but multiple agents including many working autonomously behind the scenes. 

How agents can operate in the retail and consumer goods (RCG) supply chain 

Agents can be widely applied across the RCG supply chain to automate repetitive tasks, analyze vast amounts of data for insights, and improve supply chain management. An ideal use case involves tasks that are human-intensive, repetitive, and require real-time decision making, where AI can significantly boost efficiency and accuracy. The criteria for an ideal use case includes high data availability, clearly defined achievable outcomes, and the potential for measurable improvements in revenue and cost savings. 

AI agents can play a crucial role in retail store performance and inventory management practices. An agent can autonomously monitor performance data to alert the store manager when store performance metrics fall below a defined threshold. By comparing performance across similar stores, the agent can identify areas for improvement and recommend actions to improve store performance.  

Agents can help to avoid stockout and overstock situations at retail locations. By analyzing data from various sources (such as sales, inventory, promotions, and external events), an agent can identify when a sales spike is misaligned with the forecast, leading to a potential shortage, and alert the supply chain team. The agent recommends a replenishment order which it can automatically generate to help ensure optimal stock levels, lower carrying costs, and reduce the likelihood of stockouts or surplus inventory. 

Mitigating challenges with agentic AI

Disruptions across the supply chain often lead to product shortages and low case fill rate (CFR), leading to the complex daily task of allocating inventory across your customers. An agent can analyze customer orders, current inventory levels, and product substitution options to identify potential CFR situations. The agent allocates inventory by prioritizing orders based on predefined criteria such as customer loyalty, customer segmentation, order value, SLA fines, and urgency. 

One of the biggest challenges facing RCG companies in 2025 is assessing the impact of tariffs. AI agents can evaluate and recommend alternative suppliers from different regions to mitigate the risk of high tariffs. This diversification strategy helps in maintaining a steady supply of materials while minimizing costs. By continuously monitoring tariff regulations and market conditions, an AI agent can suggest cost-saving measures such as bulk purchasing before tariff hikes or shifting production to countries with lower tariffs. An agent can assist in negotiating better terms with suppliers by analyzing market conditions and historical pricing data. This helps to ensure that companies get the best possible deals despite tariff fluctuations.  

What’s next? 

Consider the significant amount of time and effort that it takes today to answer the question: “How can I optimize my supply chain to boost sales by 10%?”. 

Although this might feel like a supply chain question, it involves finance, sales, marketing, and possibly manufacturing. It’s such a complex question that answering it is likely to need days or weeks of analysis. 

Today, agents integrated into Copilot enable users to ask specific questions in defined areas. This capability will expand in scope and complexity over time, eventually leading to a comprehensive redesign of business applications. 

Project Sophia envisions agents, copilot, and business applications converging into an infinite research canvas.   

Designed with an AI first approach, Project Sophia lets you ask business questions by analyzing data from various disparate systems and inputs. The AI guides you to view different perspectives, helping you understand and act on insights holistically. 

Project Sophia reimagines the user experience, supporting each job function to address questions from their perspective while integrating strategic and tactical approaches. 

Getting started with agentic systems 

Increasing AI’s potential to scale value chain optimization in retail, consumer goods 

Agentic AI lends itself well to navigating the complexity of routes to market—integrating manufacturing and sales strategies, selling through multiple channels or direct to consumer, managing multiple product lines and businesses, and integrating marketing and sales efforts globally. 

Agentic AI is an integral tool that gives LLMs agency, with the ability to act autonomously. Whereas LLMs have previously been used to perform tasks including generating text and summarizing documents, they have not been able to act on their recommendations. Agentic AI on the other hand, is designed to drive goal-based optimizations and can dynamically adapt and execute goals with high predictability and minimal human oversight. Together, advancements in generative AI and agentic AI will redefine strategic value and productivity derived from technology, incorporating more advanced decision making processes with greater accuracy and speed. 

Identify business problems and scenarios for more strategic engagement 

As you consider how to use AI agents in a strategic manner, it is vital to frame applications of agentic AI in the larger context of identifying line of business processes that lend themselves to automation: optimizing time-consuming and mundane tasks/scenarios; establishing user trust in the agent’s capabilities and establishing clear operational guardrails for agentic AI including data governance, privacy, security; and instilling confidence in the agent’s value delivery, extending collaborative work management beyond task tracking to planning and execution functions.  

The integration of agentic AI and generative AI into business applications signifies a monumental shift in how organizations can approach problem solving, strategic planning, and operational efficiency. By using advanced AI capabilities, businesses can anticipate a future where decision making is not only faster and more accurate, but also more insightful and holistic. This convergence of technology paves the way for innovative solutions and unprecedented levels of productivity, firmly with AI at the core of tomorrow’s business landscape. 

Learn more about agentic systems

• Delivering your supply chain copilot: Prioritizing areas of ROI 

• Delivering your supply chain copilot: Getting started on ideation 

• Empowering responsible AI practices 

•  Microsoft Cloud for Retail website  

Sources

¹ https://www.mckinsey.com/capabilities/operations/our-insights/supply-chain-risk-survey  

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Now that we’ve explored priority areas of return on investment (ROI) that a supply chain copilot can provide in the context of our supply chain, let’s delve into data considerations and how to get started on ideation for your supply chain copilot. The complex real-time decisions and agility required of today’s supply chains can seem daunting when thinking about transformation, but actually is well-suited to application of AI to selected use cases as you get started. 

Technology requirements  

Irrespective of the target areas for a supply chain copilot, the foundational technology capability requirements are the same. These can be thought of three distinct layers that ensure scalability and maximum extensibility into the future. The three layers are laid out below:  

1. Data platform:
   Data is the fuel that powers AI, consequently it must be the foundation of any application of AI such as a supply chain copilot. Data is likely to exist in multiple places, leveraging different storage approaches. Some could be in distributed databases such as Azure Cosmos DB. Other sources might include SQL databases such as Azure SQL. Irrespective of such sources, the need to unify across multiple locations is essential to make sense of the data. This need to govern, model, and consolidate multiple data sources creates a strong case for a single data platform like Microsoft Fabric—offering unified storage, experience, and governance—Fabric was designed to be the data platform for the era of AI.
2. Analytics and AI:
   Some might say that ‘this is where the magic happens’, and when it comes to making sense of the data they would be correct. The analytics and AI layer is where predictions and detailed—yet actionable—insights are generated. As an example, Azure AI Studio empowers you to simplify the creation of AI-powered solutions, giving access to multiple AI techniques and approaches—including generative AI with Azure Open AI, but also incorporating tools such as AI vision or AI Speech.
3. Application layer:
   While it is easy to think of the application layer as the layer with which users interact with your solution, this is only part of the story. Process orchestration and automation is something that can add significant value to a copilot. For example, when a supply chain copilot recommends actions to a supply chain planner—for example requesting expedition of a shipment—it would add so much more value if the copilot were able to execute the recommendation on behalf of the planner.  This is where Azure Functions, which are event driven, can play their part. Typically, this would require interaction with other execution systems through an API—resulting in the need for careful API management over the lifecycle of the different solutions.  

Your own supply chain copilot: Design and delivery  

Experienced supply chain and technology practitioners know very well that what may look amazing in a conceptual demonstration or video can be very difficult to deliver in the real world.  

Issues such as data sources, system integration, technology choices, and overall architecture can make the prospect of delivering your own copilots feel overwhelming.   

Microsoft teams have seen this and pioneered approaches to help you establish a way forward and deliver results quickly. This is broken down into three steps:  

Rapidly ideating on value  

This is primarily a workshop-driven approach to identify and validate specific challenges or opportunities where AI can deliver value for the customer. From a supply chain perspective, this would focus on the elements that you feel offer the largest potential benefit or cause the largest business pain. It is broken into three parts:   

• Inspiration: Here, a high-level education regarding the capabilities of AI, generative AI, and copilots are covered alongside inspirational examples of where customers are using this technology to deliver value.
• Opportunity generation: In this stage, opportunities within your supply chain organization are examined.
• Prioritization: This is the evaluation stage where opportunity areas are prioritized using a simple four-quadrant matrix with ‘Effort’ on the x-axis and ‘Impact’ on the y-axis. This provides a simple yet effective visualization approach to defining priority focus areas.  

The output of this is to be clear on the ”solution route,” or most appropriate combination of technologies to be applied, to deliver what is required. As an example, Microsoft Copilot Studio, Microsoft Azure AI Studio, and Microsoft Power Automate, when combined together, form a very powerful combination to support copilot delivery especially where there are requirements around information extraction, knowledge mining, and process orchestration.   

There is no “one-size-fits-all” so other approaches may be recommended. This may include leveraging partner offerings—Blue Yonder for example have mature capabilities in the form of its control tower solution which may be appropriate.   

Another key outcome of this stage is to understand which of four AI opportunities is being targeted: enrich employee experiences, reinvent customer engagement, reshape business process, or bend the curve on innovation. This aids identification of the key value drivers that you seek to influence, key performance indicators (KPIs) you seek to influence, and provides a segue into the next stage.  

Envisioning  

Again, this is a workshop driven approach—sometimes spanning several sessions—including all relevant customer stakeholders, to determine details of the solution to be delivered.   

This will include establishing a detailed view of architectural elements, interaction and integration points, value potential, and data requirements. A perspective on the business process impact will further enhance the detail behind the value case. This would leverage benchmarks from existing research and established copilots to create a view tailored to your business.  

An additional key output to complement the value case is a view of the investment required and the way in which the delivery can be structured to maximize return on investment and deliver using an Agile delivery approach.  

Proof of concept, minimum viable product, and beyond  

Following envisioning, there is a choice between moving to deliver a proof of concept (POC) or a minimum viable product (MVP).  

A POC demonstrates that an idea or use case is feasible through the delivery of a specific set of capabilities. It is used to illustrate and prove a concept and is usually self-contained in that it does not connect to live data or other systems. Consequently, it is of limited use beyond demonstrative purposes.  

By contrast, an MVP is deployed into production and integrated to existing systems so offers immediate value to the business and end-user while requiring limited effort to deliver. It can therefore become a foundation for further development and enhancement by adding capabilities based on prioritization using Agile development principles.

Integrate AI across your supply chain  

AI transformation, and specifically copilots, present a remarkable opportunity for you to innovate and compete with renewed vigor. By leveraging AI, businesses can enhance efficiency, mitigate risks, and uncover hidden opportunities.   

The integration of AI into supply chain management through a supply chain copilot, for instance, allows for real-time visibility, optimized data management, and seamless interoperation across multiple elements. This shift from reactive to proactive operations enables organizations to consistently deliver the right products at the right time, while balancing inventory, waste, and transportation costs. Moreover, the use of generative AI offers new possibilities for content and insight generation, further empowering supply chain practitioners. As technology continues to evolve, embracing AI transformation will be crucial for organizations to stay ahead in an increasingly complex and dynamic world.  

Learn more  

•  Microsoft Copilot Studio  
• Azure AI Studio   
• Microsoft Cloud for Retail website  
• Microsoft for Consumer Goods website  
• Supply chain AI for the new era of value realization  
• Unlock the full potential of your next-generation supply chain with Microsoft and Blue Yonder  
• Empowering responsible AI practices  
• Copilot in Microsoft Dynamics 365 Supply Chain Management   

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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.

The value of AI transformation and copilots

While it may feel instinctive that the value of AI transformation lies in its ability to save time, this is only part of the story. Early studies are already showing significant value from AI transformation being derived from not only reducing costs, but also increasing revenue and reducing risk through improved quality of decision making.

Highlights from key studies include benefits of:

• Delivered 25% increase revenue through enhanced efficiency.²
• Increased customer satisfaction by 12%.³
• Increased revenue growth by 4% through improved strategy and engagement.⁴
• Reduced costs of 10%.⁵
• Completed tasks 25% faster.⁶
• Reduced total expenditure by 0.7%.⁷
• Reduced risk through a 40% improvement in quality of decisions.⁸

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

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.

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.

Learn more

• Microsoft Copilot Studio
• Azure AI Studio
• Microsoft Cloud for Retail website
• Microsoft for Consumer Goods website
• Supply chain AI for the new era of value realization
• Unlock the full potential of your next-generation supply chain with Microsoft and Blue Yonder
• Empowering responsible AI practices

¹Embracing AI Transformation: How customers and partners are driving pragmatic innovation to achieve business outcomes with the Microsoft Cloud, Official Microsoft Blog.

²How Netlogic Computer Consulting is Boosting its Sales Performance with Microsoft Copilot for Sales, Tech Community.

³Microsoft: Copilot for Service Boosts Customer Satisfaction by 12 Percent, CX Today.

⁴What Can Copilot’s Earliest Users Teach Us About Generative AI at Work?, WorkLab.

⁵Is Microsoft Copilot Worth the Investment?, Varonis.

⁶Navigating the Jagged Technological Frontier.

⁷Is Microsoft Copilot Worth the Investment?, Varonis.

⁸Navigating the Jagged Technological Frontier.

The post Delivering your supply chain copilot: Prioritizing areas of ROI appeared first on Microsoft Industry Blogs.

Maximizing AI: Category management and more

Buying habits shift quickly in today’s consumer-driven world. For retailers, especially grocers, providing customers with affordable, fresh, and convenient options while navigating the impacts of inflation and supply chain disruption is critical. Meeting these expectations requires creating and maintaining a supply chain centered around customer demand—no easy task when supply chain functions are siloed, data is disparate, and needs change from day to day.

Together, Blue Yonder and Microsoft are unlocking a new era of value for retailers with AI. With AI-powered solutions, retailers can empower their teams to make decisions based on access to real-time data and intelligent insights. AI has allowed us to reimagine planning, making it possible for retailers to operate more effectively by transforming category management into an agile, responsive, and ongoing process that is tightly synchronized with the broader supply chain.

Microsoft Cloud for Retail

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AI-powered category management makes it simple to keep the end consumer the focal point of your supply chain functions, helping retailers quickly achieve several critical capabilities:

• Address demand across every channel
• Plan at the hyperlocal level
• Optimize for demand in real time
• Factor in space and labor parameters
• Monitor and adjust instantly
• Identify and respond to opportunities and concerns quickly
• Enable continuous learning with constant space and assortment performance feedback
• Share updated demand forecasts across the supply chain

Enabling AI in this way facilitates a constantly improving demand forecast as the AI model builds iteratively on the data provided, allowing planners across the entire value chain to make better decisions for the business. It’s clear that, properly integrated, AI is not just a technological advancement but rather a strategic tool that can lead to improved customer experiences, operational efficiencies, and ultimately, financial growth and scale for retailers.

Blue Yonder and Microsoft teams recently collaborated to present a webinar titled “Supercharge Your Category Management Process with AI Assistance.” In this presentation, we introduced category managers to the many ways AI-powered assortment can help streamline category management and empower faster, smarter decision-making.

But category management is just one piece of the modern supply chain puzzle. In this blog post, we’ll discuss some of the major connecting points between category management and the overarching supply chain and how understanding the interplay between components can help you begin to realize the art of the possible with supply chain AI.

To that end, we’re looking at three major considerations for making the most of category management within a broader, AI-powered supply chain.

1. Synchronizing with the overall supply chain

One crucial thing to consider is the extent to which your category management process must be synchronized with the broader supply chain to enable an agile, responsive, iterative process. This requires thinking about how you get the initial data, and then how you operationalize it — how you put the data to work. Everything should be framed in terms of the end consumer as the focal point, making sure that you address demand across all channels. Doing so normalizes the physical and the digital channels, enabling hyperlocal planning at the individual store level.

It used to be that whatever the practice was, you would cluster stores and talk about stores that had similar formats, planning similarly for all store locations based on one generalized model. Now, with the integration of AI-powered insights and analytics, we’re getting into hyperlocal store planning, where you can really reflect not only the local community shoppers who are making the trip into brick-and-mortar locations, but also support the way that buyers want to shop online, normalizing those two experiences.

But this also requires acute awareness around demand planning, as you have to essentially make sure that demand planning is optimized in real time. This is why the correlation with the supply chain is so important: because you’re reflecting the latest trends, but you’re also working around the space and labor parameters in the store and optimizing in real time to make sure that demand planning is updated accordingly. This ability to execute on constantly changing data across workstreams—to monitor and adjust on the fly—is key to achieving the agility piece that’s so necessary for responding with flexibility to market demands and driving better margins for the business.

2. Enabling collaborative data sharing

Data sharing sits squarely at the intersection between retail consumer goods and category management. In an AI-supported category management process, you have category captains managing entire shelves of a category and gleaning invaluable insights in the process about the performance of products on the shelves, both physical and digital. These insights inform and support their retail partnerships in ways that weren’t possible until very recently.

Cross-capability data sharing allows you to identify the problems and root causes, understand them quickly, take action, and then implement that continuous learning. With interoperability, you can leverage that AI-powered continuous learning component around space and assortment performance, feeding that data back into the forecasting engine to generate an updated view of demand that can be shared across the supply chain so that the demand forecast is constantly improving, allowing planners across the entire value chain to make better decisions.

But a plan is only as good as the ability to execute it, so we move on to thinking about the execution piece and how to optimize that with store-level compliance.

3. Pulling in the store as a node in the supply chain

Syncing this concept of category management with the supply chain is critical for high-impact results because this is where operationalizing your data becomes real. It’s important to understand that integrated architecture is not an orchestrated ecosystem. In order to have a holistic view of the business, synchronization has to take place. You’re reducing the latency to have better data synchronization across various supply chain functions; you’re enabling the collaboration both with store associates but also with brands and retailers, empowering adaptive decision-making by connecting the planning and execution functions.

What’s pivotal to realize here is a theme that we’ll see become more prominent over time: the store is now a huge data source that needs to be integrated with the rest of the supply chain. As we see customer experience playing an increasingly pivotal role in the supply chain, we see a greater need to incorporate store-specific data. It’s no longer that we’re just optimizing store operations off to the side—the store and its operations are now part of the supply chain itself.

Many organizations seek to address concerns around siloed technology, and yet, the retail store often continues to be an overlooked component. Many retailers have warehouse management systems that are connected to their transportation management solutions (TMS), but very rarely do they also connect the stores as being a node in the supply chain for real inventory visibility. So, when we think about optimizing across the different channels with e-commerce and fulfillment, structuring warehouses and the fulfillment network, it becomes more relevant to connect the data across these functions.

Powering a connected supply chain with Microsoft and Blue Yonder

Integrated AI across the supply chain has incredible potential to enhance business performance and reduce volatility with predictive intelligence. Together, Microsoft and Blue Yonder are making it easier for retailers to get ahead with technologies that empower agility, transformation, and innovative operations at scale.

Bringing together the best of supply chain technology and cloud platform capabilities, Blue Yonder and Microsoft are at the forefront of a cognitive revolution of supply chain innovation. Blue Yonder’s Luminate® Cognitive Platform lays the foundation for a truly intelligent autonomous supply chain with predictive and generative AI capabilities that are industry-specific. It’s built on Microsoft Azure, which is a game changer in the cloud platform space, ensuring data is unified for centralized and accessible insights. Our partnership enables supply chain innovation by connecting information across the value chain for better collaboration, scalability, security, and compliance.

Sainsbury’s: Results that speak for themselves

Sainsbury’s is a trusted UK brand, loved by millions of consumers and operating more than 2,000 store locations across its Sainsbury’s and Argos brands. A longtime user of Blue Yonder’s warehouse management, Sainsbury’s sought to implement new AI-powered solutions in 2023 to improve forecasting and replenishment capabilities and increase sustainability.

Blue Yonder has helped Sainsbury’s to tackle several significant goals:

• Realizing improvements in inventory stockholding and availability key performance indicators (KPIs) with machine learning (ML) forecasting and multi-echelon replenishment
• Transforming Sainsbury’s architecture and business processes to become easier to understand, scalable, resilient, and nimble, as well as able to support any future business changes quickly
• Reducing the current number of key systems to eliminate redundant functionality, reduce technology risk, and improve the user experience for colleagues, suppliers, and business-to-business (B2B) customers
• Offering a more automated, simplified user experience and standardized workflows to increase user productivity

Our partnership with Sainsbury’s has already resulted in significant savings for the organization as part of its ongoing plan to future-proof the business. Sainsbury’s leadership confirmed in April 2024 that the company is unlocking significant savings and have already improved ambient availability, using real-time forecasting to optimize sales, waste, and stock equation.

Implementing Blue Yonder’s solutions built on the resilient, scalable Microsoft Azure cloud platform, Sainsbury’s has elevated its ability to monitor and respond to changing customer needs with new capabilities allowing prediction and prevention of potential supply chain disruptions. Blue Yonder has helped Sainsbury’s take advantage of ML-based forecasting and ordering capabilities to help stores better manage fresh and perishable products, while also achieving visibility, orchestration, and collaboration across the end-to-end supply chain, using automation to make better business decisions.

Explore solutions from Microsoft and Blue Yonder

• Learn more about Blue Yonder category management and Microsoft Azure
• Learn more about Blue Yonder Luminate Cognitive Platform
• Learn more about Blue Yonder Orchestrator
• Explore Microsoft Cloud for Retail

The post Supply chain AI for the new era of value realization appeared first on Microsoft Industry Blogs.

In a world where market complexity and disruptions are common, retail organizations must learn to navigate and be ready to adapt to new challenges. Retailers seek to get ahead of supply chain disruptions, embrace workforce transformation, and address economic uncertainties—all in the context of a “new era of AI” that has emerged with generative AI, where technology serves a pivotal role in future-proofing businesses.

Microsoft Cloud for Retail accelerates business growth by providing retail-specific capabilities across the Microsoft Cloud portfolio to seamlessly connect your customers, people, and data. Together with Blue Yonder, Microsoft’s generative AI-powered scenarios are enabling retail organizations to create agile, resilient, and sustainable supply chains by connecting data across their ecosystems to identify issues and optimize performance. Microsoft and Blue Yonder have been at the forefront of a cognitive revolution of supply chain innovation, laying the foundation for a truly intelligent, autonomous supply chain, with a predictive and generative AI copilot, delivering faster and better decision making.

Retail and consumer goods companies are turning to AI, including generative AI, predictive AI, machine learning, and automation, to respond faster and in an agile and scalable way to a myriad of problems. These emerging technologies reimagine the user experience, unlock productivity, and drive greater efficiencies in a way that was previously not possible. Harnessing AI, machine learning, and generative AI in this way affords greater visibility and insights into the next best actions. When integrated into retail planning and execution workflows, generative AI can transform the way teams respond to evolving market dynamics, continuously adjusting decision-making based on demand and supply signals, while considering exponentially more scenarios in a fraction of the time it would take the team to compile.

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In the evolving landscape of supply chain management, the integration of AI is becoming a popular strategy for enhancing efficiency and innovation. While the journey of implementing AI and machine learning technologies can be challenging, with some initiatives possibly not fully achieving their expected outcomes, this doesn’t detract from the potential value AI brings to the table. The effectiveness of AI doesn’t solely rely on its application to existing processes but rather on a transformative approach towards how these technologies are embedded within the organizational fabric. Embracing AI is about more than just technological adoption; it’s about reshaping the foundational elements of workflows and processes to truly leverage the power of predictive insights, automation, and data-driven recommendations.

Traditional, linear supply chains, business units and even data currently exist in silos, perpetuated by the inherently disjointed nature of how supply chains were originally constructed: every function contained within its respective walls. These silos can lead to slower decisions and hinder collaboration throughout the organization. Each functional team lacks visibility beyond their sphere of influence, and often doesn’t understand the impact of their actions on other business areas. When AI and machine learning are applied in this environment, quick decisions can conflict, and optimized key performance indicators (KPIs) can cancel each other out. It is therefore imperative that organizations centralize their data, standardized into a single data model that is ready for AI and machine learning consumption, and connect their workflows to reap the full benefits of AI and machine learning. Once connected, businesses can leverage AI to orchestrate the entire supply chain, allowing visibility and collaboration between all functional teams, all driving toward common goals: to fuel profitable growth and delight the end customer.

Unlocking the power of AI

There’s a lot of hype around generative AI right now, and for good reason. Its inherent creativity, speed, and automation have been viewed as a potential way to replace labor, but we would caution that view is an incomplete view of what this emerging technology can do and how it can be applied. Rather than seeing the technology as competing with human workers, generative AI offers an unprecedented opportunity to enhance industry expertise and experience, unlocking levels of productivity.  

Imagine: instead of having to search multiple systems to find the answer to your question, you have a dedicated assistant that can access the far reaches of your company’s system-wide knowledge, and in a moment return any answer to any question—in simple, everyday terms. Think about the impact this would have on your team’s productivity, quality of performance, and overall satisfaction. Or how this could dramatically accelerate the onboarding of new team members. The opportunities are endless, and as more use cases are discovered, generative AI can and should be used to supplement or redeploy the efforts of human workers in ways that empower and enable teams, not supplant the workforce. While these technologies cut down on manual processes, saving time and effort while providing robust recommendations and thorough data, the human touch will always be necessary to some degree. 

Adapting to new information and updates is vital to controlling your supply chain and making the best choices, and AI holds the key to greatly improved shopper experiences. By using automation and AI, stores can stay ahead of customer demand and keep their shelves stocked with the right product at the right time, and at the right price. Applying predictive analytics to internal and external data can help identify potential disruptions before they happen, empowering teams to proactively respond before it impacts end customers. And bringing holistic data from your warehouse, logistics network, staffing plans and more together with market conditions, seasonality, weather, and traffic patterns can provide a 360-degree view of how shoppers think, act, and what they will experience as consumers.  

By partnering with Blue Yonder and their strategic services team, an iconic fashion retailer will be able to align sales demand forecasting and replenishment by using Cognitive Merchandise Financial Planning throughout their supply chain to improve agility and efficiency. After introducing Cognitive Merchandise Financial Planning, the retailer will reduce time spent on set up and maintenance tasks like adjusting to trends, seasonality, and more. Because the solution dynamically adjusts to the latest marketing needs, there will be improved decision-making speed and automated accuracy, resulting in better performance and greater collaboration across teams while using fewer resources and improving global inventory control.  

Microsoft and Blue Yonder taking retail planning to new heights with AI and machine learning

Blue Yonder and Microsoft are transforming the way supply chains are run. The Blue Yonder Luminate Cognitive Platform, which runs on Microsoft Azure, is embedded with AI and machine learning and serves as the foundation for all systems and applications. Retailers can spin up unconstrained computing power to run hundreds of simulations in a matter of minutes, versus in a few hours—or days. Blue Yonder’s solutions also run on a single source of truth, eliminating batch so teams don’t have to sacrifice accuracy for speed. As a result, retailers are collapsing the time horizon between planning and execution to nearly zero, while working synchronously across their supply chain. Integrated generative AI serves as a force multiplier for productivity so teams can do more important things more frequently and drive continuous optimization. To enable this transformation, Blue Yonder recently announced the launch of two next-generation planning solutions for retail, Cognitive Demand Planning and Cognitive Merchandise Financial Planning, as well as its generative AI solution, Blue Yonder Orchestrator.

The Blue Yonder Cognitive Demand Planning solution utilizes patented Blue Yonder algorithms and machine learning models to forecast, shape, and sense demand while collecting inputs from all key stakeholders to produce an optimized plan. These capabilities reduce the effort required to simulate drivers in real-time while managing more complex scenarios, resulting in faster and more accurate results. By seamlessly bringing together AI and machine learning driven capabilities, Cognitive Demand Planning empowers teams to respond faster to problems while building supply chain resilience and managing more complex scenarios, providing a leg up for demand planners to deliver higher plan accuracy and more relevant AI-driven insights.

Traditional merchandise financial planning can be a manual and reactive process. Cognitive Merchandise Financial Planning from Blue Yonder solves these problems and more. Blue Yonder can take your manual process and transform them into long-range planning and workflows that span across stores, e-commerce, wholesale, and more. This planning solution adjusts to fit your needs, with planning processes that can be configured based on your priorities and business objectives. To more accurately predict upcoming challenges, AI-enabled ‘what if’ scenarios allow a multitude of roles in the company to make quality decisions based on all available data, enabling a true omnichannel planning process. Cognitive Merchandise Financial Planning also makes it easier to analyze data after the fact—from large aggregations to slicing down to granular data—or continuous learning and optimization.

Integrated within the Luminate Cognitive Platform, Blue Yonder Orchestrator brings together the power of generative AI, the natural language capabilities of large language models (LLMs) and the depth of Blue Yonder’s supply chain expertise to unlock the full value of data. This unique solution delivers dynamic decision-making and orchestration by allowing users to query in everyday language then providing recommendations and insights in an easy-to-use format. This approach accelerates the learning curve for new employees while increasing overall productivity by serving up insights and guided recommendations without needing to navigate multiple software applications. Because Blue Yonder Orchestrator is embedded within the Luminate Cognitive Platform, which runs on Azure, it inherently benefits from robust security measures, auditing, reliability, and cost control. With Blue Yonder Orchestrator, companies can also establish guardrails based on user permissions, protecting data access without inhibiting performance. No matter what your focus is, using generative AI can bring you closer to your goals.

Getting started with AI and machine learning

At the end of the day, these emerging technologies have been proven to help accelerate better business outcomes when the right foundation is in place. Supply chains are an excellent place to incorporate predictive AI, generative AI, and automation due to the number of moving parts and the massive amounts of data generated. AI can unlock the complete value of data in near real-time for better insights to increase efficiency of decision-making. For example, while a person can only create so many scenarios in a day, AI’s unconstrained computing powers enables it to process hundreds of scenarios in minutes. These data-driven insights allow teams to focus their time on making value-added, high-impact decisions rather than on manual data entry and analysis.

AI, machine learning, and automation are great tools that can act as a force multiplier for supply chain efficiency and profitability. While new solutions are cropping up every day, it’s imperative to look for business applications that run on a centralized, cloud-based platform, a single database, have connected workflows, and have AI and machine learning embedded throughout. With this foundation, retailers will be empowered to break down existing silos, foster both inter-and intra-enterprise collaboration, and drive their businesses towards a future of greater resilience and sustainability.

Explore solutions from Microsoft and Blue Yonder

• Learn more about Cognitive Demand Planning from Blue Yonder.
• Learn more about Cognitive Financial Merchandise Planning from Blue Yonder.
• Learn more about Blue Yonder Orchestrator.
• Build your own copilot and generative AI applications with Azure OpenAI Service.
• Microsoft introduces new capabilities that help retailers bring AI to the shopper journey and enhance store associate experiences.
• New era of value realization is here—put your data to work with AI.

The post Unlock the full potential of your next-generation supply chain with Microsoft and Blue Yonder appeared first on Microsoft Industry Blogs.

Consumer goods decision-making has become so complex that human talent alone is not sufficient. In order to scale, every data point must be digitized, analyzed, and put to work through AI and machine learning, to identify trends and patterns, and tell us what to do. Predictive analytics is at the foundation of proactive productivity, business agility, and market share growth.  

We are at the cusp of a huge shift in what it truly means to be a digital business. A digital business where you use technology to change how and why you operate, not merely leveraging it to optimize your existing processes. In the consumer goods industry, we spent more than a decade getting our data estate in order and hired data scientists en masse to help us make sense of it. Now we truly know why we were doing all the arduous work.  

AI turns data into shareholder value 

The volatility of macro events in recent years—and their accompanying challenges and disruptions—have had a serious impact on the retail industry and consumer behavior. Today’s consumers are driving the revolution of retail with new expectations in terms of experience and service. Consumer goods organizations are closely monitoring and predicting customer behavior to ensure their offerings are aligned today and tomorrow. There is so much data and it is changing so quickly that finding patterns and insights and putting them to work in a timely fashion is not possible without AI. While quick access to actionable data insights is key to understanding fast-changing consumer needs that enable better demand prediction and forecasting, the ability to insert those insights into your business processes in a timely manner is what will drive business and shareholder value.  

Digital ecosystems for greater transparency, traceability, and agility 

Consumer purchasing experiences are only as good as the retailer or brand’s ability to deliver the right product, at the right place, and at the right time so consumers can happily discover, fall in love, and purchase it repeatedly whenever and however they desire. The expectation of a seamless purchasing experience across multiple channels and shortened delivery times at little or no cost creates enormous supply chain challenges. We are not saying anything earth-shattering when we highlight once again that relying on historical models is not, and certainly will not, be enough to build necessary resilience and agility into supply chains. We must leverage AI to be predictive to proactively detect opportunities and risks across the entire value chain all the way from idea to design production, to the point of sale, and finally to the experience of the product itself. Retailers and consumer goods organizations must adopt a digital-first mindset, shifting the paradigm from a reactive way of doing business to one of long-term planning to sense, predict, and adapt to disruptions—preventing stockouts, missed sales, and avoiding overstocking. 

The complexity of forecasting demand amid market fluctuations has highlighted the need to shift from a traditional cost-driven supply chain based on siloed networks to a customer-centric supply chain of services, which allows synergies between channels and collaborative data sharing. An interconnected digital ecosystem across an end-to-end supply chain network is critical to bringing data together in one place with a holistic planning and logistic system for improved collaboration. Connected end-to-end visibility and collaboration across the supply chain network can prepare for and mitigate potential disruptions. Optimizing stock levels across all selling channels, tracking inventory from manufacturers to warehouses to transit route to point of sale, calculating shipping time for that inventory, and promising accurate delivery time to customers requires multi-tier visibility and collaboration. Compiling data in one place with updates in real-time enables the insight, control, and management needed for greater flexibility, transparency, and traceability.  

Data sharing between retailers and consumer goods vendors has not been optimal. Everyone protects their gold mine of data, and they should—data monetization is a business strategy, not a data strategy. However, retailers and consumer goods brands must find a way to work better together to both share the data and protect it so all parties can benefit. It is the ability to share information in real-time and orchestrate responses to risks and changes, in demand to ensure they are placing the inventory in the supply network at the right place and time. End-to-end visibility is a business imperative for better collaboration with suppliers for on-time fulfillment and the ability to anticipate fluctuations in consumer demand as well as bottlenecks in supply in terms of inventory and freight. Consumer goods companies and retail organizations need to find the correct balance of sharing data to improve demand planning and growth management. 

Generative AI to predict and remediate risks with actionable insights 

Supply chains have mostly been assiduously designed to be as lean as possible. That is no longer imperative. You must optimize supply chain through enabling true collaboration and using generative AI to mitigate disruptions, produce actionable insights, and orchestrate business processes to act on those insights in an automated way. Applied throughout the supply chain to improve inventory positioning, on-time delivery, accurate order fulfillment, convenient returns, and to reduce stock-outs, this orchestration will improve consumers’ experiences and help to ensure their brand loyalty.

Microsoft Dynamics 365 AI Copilot proactively alerts supply planners to risks and mitigation strategies and the best course of action: inventory restocking, inventory placement, demand shaping, and improving lead-time estimates. Predictive insights identify impacted orders, while Dynamics 365 Copilot helps act on these insights with contextualized email drafts. Now supply chain personnel can collaborate with impacted suppliers in real-time to quickly identify new estimated times of arrival and reroute purchase orders based on weather disruptions or geopolitical tensions. Dynamics 365 Copilot helps to identify reliance on suppliers in shock-prone regions leveraging external signals to predict and remediate external risks, to feed back into planning systems and improve demand forecasting accuracy. 

Know your customer  

The volatility of consumer demand, and the increasingly complex path to purchase, combined with the continuous wave of disruptions affecting supply chain logistics (commodity and component pricing) make demand forecasting incredibly challenging. With our Smart Store Analytics solution, we’re providing retailers with e-commerce-level shopper analytics for the physical space. Microsoft’s partnership with AiFi—the world’s most broadly developed computer vision-powered store operator—provides check-out free solutions and also delivers actionable insights on AiFi smart store data with predictive models that optimize store layout and product recommendations—shelf placement and inventory—but also informs marketing and trade promotions to move inventory more efficiently through the stores. AiFi powers autonomous stores at stadiums, convenience, and grocery stores using AI to enable shoppers to check out without waiting in line to pay. 

The multiple ways customers and consumers interact with brands and retailers—gathering data at each of those touchpoints, and gaining insights to improve their experience—allows brands and retailers to strengthen their relationship with consumers through collaborative data sharing using AI to provide accurate suggestions and recommendations enhancing the customer experience and deepening brand loyalty.  

Sustainability 

Consumers—more environmentally conscious than ever before—are the driving force behind a “greener” future. They want to shop from retail and consumer goods organizations that are transparent and sustainable.  

There is a growing role of data and AI in operationalizing sustainability efforts in terms of reducing costs while gaining greater resilience and efficiency in reducing environmental impacts. Using data to operationalize sustainability will reduce costs and drive efficiencies. Businesses are also choosing to extend their mission beyond shareholder value to encompass broader ecological and societal issues.¹  Investing in next-generation demand planning that leverages AI insights and machine learning capabilities helps improve forecasting accuracy. Gaining analytic agility in planning ensures that supply more precisely matches demand and increases in-store availability by reducing overall inventory levels.  

Digital is business  

AI is a game changer. At every level of your business, investing in data and AI should be the highest priority to improve net margin, free up working capital, improve customer satisfaction, anticipate changing demand to maximize revenue, manage costs and improve efficiencies to protect margins, and optimize end-to-end networks to balance inventory and service.  

So how do you decide where to start? The first step is to identify the type of data you want to collect. Remember—data monetization is not a tech strategy, it is a business strategy. The next step is to assess what technology and tools you have in place to gather that data. From there you can investigate the technology and AI options that will get the results you need. 

Learn more 

• Read our blog “Digital Imperatives for Market Tectonics in the world of Consumer Goods” and how leading with customer centricity can drive your engine for change. 
• Consumer Goods Technology’s recent article highlights how consumer goods companies are using OpenAI’s GPT-4 language model for consumer goods business intelligence discovery.
• Visit Microsoft for the consumer goods industry for more solutions. 

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¹ “Perspectives, The future of the consumer industry, Buying into Better^TM,” Deloitte, 2023.

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COVID-19 significantly altered consumer and user behavior—heightening online shopping and social media brand engagement. Consumer trends that may have once shifted over months or years are now happening more rapidly, faster than organizations can keep up with.

Technology is the engine for change

The need to leverage digital technologies to build agility into the core of a brand’s business has never been more apparent. Technology-driving business adaptability is the fine line separating brands that are surviving and thriving from those that are losing ground. Powerful and innovative leaps forward in areas such as data management, analytics, modeling, personalization, collaboration, and intelligent automation are giving organizations the tools and resources to not just survive, but to thrive in this new landscape.

Cloud-based solutions are the perfect enabler for engaging consumers in new ways—creating and delivering highly personalized contextual offers and uncovering customer insights via advanced analytics across multiple channels. By utilizing new data-driven models and offerings, companies can unlock new sources of value among customers, suppliers, retailers, and other third parties to create new value propositions.

PepsiCo has faced new market demands head-on, utilizing machine learning and analytics to adjust how they operate across their 23 billion-dollar brands—PepsiCo’s machine learning journey:

“Data is the lifeblood of the company, we have 23 billion-dollar brands across multiple product segments. We rely on insights from machine learning to bring together our knowledge of the industry, the market, and our in-depth understanding of the shopping habits and preferences of consumers. It enables us to make informed decisions that ensure consumers get the products they want, helping us consistently meet consumer demand and drive growth for PepsiCo.”—Michael Cleavinger, Senior Director of Shopper Insights Data Science and Advanced Analytics at PepsiCo.

Lead with customer centricity

Implementing integrated customer management practices, such as customized marketing and sales strategies founded on shopper behaviors and driving tailored customer experiences, are now even more critical for organizations to compete and succeed.

Even with heightened efficiency and collaboration, many consumer goods organizations struggle to keep up with the increased pace and expanding breadth of retail demands that morph and evolve daily. With outdated practices and outmoded models, some companies have begun to view their customer management resources as being on the brink of collapse as they attempt to engage leading retailers. Restricted and limited by their current system, they are overwhelmed and under-resourced.

Manufacturers today need to adopt different approaches that consider retailer and shopper perspectives about products, mix, volumes, and other factors. It is no longer enough to work off historical performance to aid in forecasting. Leveraging machine learning and AI, revenue growth management teams can easily simulate and model alternative growth opportunities and better balance revenue and profits against volume, penetration, and market share.  

Proctor & Gamble took this lesson to heart when they began to approach their digital transformation, moving their manufacturing processes to the Microsoft Cloud—P&G’s incredible digital manufacturing journey:

“Together with Microsoft, P&G intends to make manufacturing smarter by enabling scalable predictive quality, predictive maintenance, controlled release, touchless operations, and manufacturing sustainability optimization—which has not been done at this scale in the manufacturing space to date. At P&G, data and technology are at the heart of our business strategy and are helping create superior consumer experiences. This first-of-its-kind co-innovation agreement will digitize and integrate data to increase quality, efficiency, and sustainable use of resources to help deliver those superior experiences.”—Vittorio Cretella, Chief Information Officer, P&G.

Navigating to a better bottom line

Like P&G, all manufacturers can use greater forecast quality and agility through digital. They need to create a proactive inventory strategy—driven by real-time monitoring at the stock keeping unit (SKU) level, creating standardization across the organization, and including a central operational process library, redesigned workforce compositions, and more.  

Brands need to identify, measure, and monitor both channel and customer “cost-to-serve” metrics, critical to enabling fact-based decision-making and decide how best to carry out customer management initiatives. Additionally, advanced analytics allows companies to produce results that can be implemented within the customer’s operating model constraints.

Leaders in consumer goods manufacturing must recognize that due to the shifts in consumer behaviors, some of their retailer partners are thriving and that better-aligned metrics and intelligent processes, along with closer collaboration with retail partners, can be a powerful tool to address challenges that the “new normal” has created.

Get the solutions you need

By bringing together data, insights, inputs, engagements, and other metrics—manufacturers can harness powerful, customized tools to consolidate their data pipeline, implement automation plans, and manage their data flow to drive improved performance and more.

Learn more

To learn more, visit Microsoft solutions for the consumer goods industry.

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