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

Connect your customers, your people, and your data

Accelerate retail growth

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.

Microsoft Cloud for Retail

Connect your customers, your people, and your data

Discover capabilities

Gain efficiency and profitable growth with the power of AI and machine learning for retailers

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. 

Enabling intelligent brands

Evolve with the ever-changing consumer preferences.

Start innovating today

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

The post New era of value realization is here—put your data to work with AI appeared first on Microsoft Industry Blogs.

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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