New era of value realization is here—put your data to work with AI
I need to improve how my field workforce supports sales to brick-and-mortar and online retailers. I want to cut waste from operations, improve financial performance—market share, revenue, and margin, and make smart decisions across my products, placement, price, and promotions. I want to maximize product sell-through, minimize stockouts, and reduce expired or obsolesced products, at every retail endpoint.
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.
1 “Perspectives, The future of the consumer industry, Buying into BetterTM,” Deloitte, 2023.
