Decoding AI to Apply Lucrative Learnings

AI Across the Value Chain

CGs across the industry are putting an emphasis on their AI efforts, investing in technology that’s going to improve visibility into consumer demand; inform product innovation in new, more efficient ways; and improve their customer service levels. While AI calls for hardy tech stacks and management infrastructures that can handle the large influx of data, and the fragile task of disseminating it to various departments across an organization, CGs are progressing in its implementation in a range of functions, from product innovation to manufacturing to marketing. For Procter & Gamble, this means leaning into AI for quality control on the production line. Sensors and imaging capture data can replace manual off-line quality testing, improve equipment efficiency, and manage power and water consumption, according to Vittorio Cretella, P&G CIO. The company is also applying AI to granular data collected through digital interactions with consumers, enabling them to better personalize messaging and offers.

"As we plan for the future, AI is central to our holistic digital transformation strategy for how to disrupt our business constructively and to better meet the needs of our consumers and customers, creating value for all stakeholders."

VITTORIO CRETELLA CIO, Procter & Gamble

And it’s not alone in recognizing the advantages AI holds in personalization: L’Oreal has introduced immersive experiences that incorporate AI, AR, and machine learning to more accurately provide personalized products and services. It recently engaged in a two-step collaboration, exploring new AI technologies that merge clinical beauty and skin research with sensor-run tele-diagnosis solutions to identify individual needs, provide customized recommendations, and flow that data into future product generation. Mondelēz International took the consumer engagement capabilities one step further into social media with its Kinh Do mooncake brand in Vietnam. The company married AI with computer vision to create “live portrait” filters that animate still images, and then used this tech to promote the Mid-Autumn Moon Festival. It invited consumers to upload a photo from a previous year’s moon festival, which then animated with the AI filter, along with a holiday message, to be shared across social media.

28%

24%

27%

Top 3 AI/ML Use Cases in CG Industry

Supply Chain Planning and Execution

Marketing/Promotion Campaign Planning and Execution

Demand Planning and Forecasting

Source: CGT & RIS, 2021 Retail and Consumer Goods Analytics Study

Click the Levi's logo above to see how they are leveraging technology in demand forecasting.

Levi’s has targeted the supply chain for some of its AI investments, leveraging the technology in demand forecasting to improve accuracy. “Scaling it should enable more precise inventory investment, lead to less markdowns and clearance, prevent waste, and enhance sustainability — all of which will improve our margins,” said Chip Bergh, Levi’s president and CEO. “This will be powerful in combination with the ongoing work AI has been contributing to pricing and promotion.”

Clearing Up Cloudy ROI

What is the appropriate use case? Is it going to be an inventory use case? Safety solutions? Quality grading? As Tyson heavily focuses on determining this answer, it’s implemented a vetting process via a steering committee that’s made up of key business stakeholders to help the company prioritize projects, taking into consideration cost, the return, and how it aligns to the company’s overall strategy. From there, the company takes on projects it believes will grow and provide ROI — specifically the ones the company knows it can scale with its existing infrastructure.

Source: IDC FutureScape: Worldwide Manufacturing 2022 Predictions

Supply chain forecasts automated using AI by 2023, improving accuracy by 5 percentage points

50%

As more CGs embrace AI, they must consider how they will fulfill key goals — whether across the supply chain, field sales, or marketing — and provide increased visibility into the comprehensive data being collected throughout the business lifecycle. “Many organizations are drowning in data they are collecting but not using, focusing on the immediacy, not the importance, of data,” reports IDC. And while AI is playing a key role in interpreting and harnessing this wealth of data to create insight, value, and learning across the enterprise, “organizations need to maintain a balance between the potential of AI and the realization that people are still needed in the loop.”

Source: Insider Intelligence

Consumer retail spend via chatbots worldwide by 2024, up from just $2.8 billion in 2019

$142

billion

"They can't measure the impact. While you are making the AI system learn, you must monitor the impact on your business."

AI is relatively new for CGs, with only 15% of companies quantifying the return on investment from AI.

MARIA JESUS SAENZ Director of the Digital Supply Chain Transformation Lab, MIT Center for Transportation and Logistics

Establishing IT Readiness

CGs ready to adopt AI solutions must first determine whether their IT architecture can support their needs and scale to meet the capacities they hope to achieve. When used within a function like last-mile delivery, it can be a long journey that requires a lot of stakeholders to agree on how a product is going to be delivered, says MIT’s Saenz. They must also decide how to house their data. PepsiCo recently pivoted, previously relying on hard drives and individual cloud locations when sharing learnings — which led to missed opportunities and lost insights. The company now leverages an AI-infused platform that provides greater visibility and allows it to make more informed decisions. Levi’s has a livestreaming repository of data that incorporates several forms of data, including transactions, consumers’ browsing behavior, weather, economic outlooks, epidemiological models, social media trends, fashion trends, competitive intelligence, and more. It’s then able to apply machine learning to predict product demand. While some AI readiness investment comes in the form of vendor partnerships, others will go toward bolstering in-house efforts. Tyson, for example, considers partnerships on a project-by-project basis. If the company wants to tackle a forecasting project, it may look at vendors who have done this type of work before. If it’s a task specific to its business, however, the company leans on its own expertise, says Lee Slezak, Tyson Foods VP of IT architecture emerging technologies in analytics.

"The best decision that we could have made was to dedicate resources in that space so that we could grow the approach and be successful, and then look over time to expand that out to others."

LEE SLEZAK VP of IT Architecture Emerging Technologies in Analytics, Tyson Foods

Source: CGT, AI-Powered Fulfillment and Distribution Study

26%

22%

11%

CGs interested in bolstering their AI-powered efforts

Working with Solution Provider Partners

Looking to Acquire Systems and Solutions

Using a Hybrid Approach of Internal and External Sources

Building Internal Capabilities

Click below to see how Adidas predicts seasonal demand

Adidas recently partnered to migrate its software solutions to a more modern platform, which will provide advanced analytics capabilities, data science, and enterprise reporting. By aligning with technology solutions that support the company’s growth plans, Adidas said its data scientists can more accurately predict seasonal demand for products and ensure product availability from warehouse to retail.

An AI-powered consumer-intelligence engine can provide enhanced decision making, improving operational efficiency to fuel further innovation. Rich Products and Vera Bradley are both examples of CGs leveraging partnerships to derive deeper analytics for these purposes. Rich Products is using AI- and ML-based data analysis and a digital twin foundation to autonomously predict issues, risks, and opportunities, while Vera Bradley is committed to a scalable system that leans on AI and data-driven insights to automate and simplify its processes.

Accountability Beyond Silos

Key to ensuring consistent practices across AI adoption is identifying who will be accountable for the systems in place and the data development and distribution. Research from CGT that focused specifically on AI in fulfillment and distribution found that the departments responsible for developing, implementing, or acquiring AI/ML supply chain solutions were most commonly dedicated AI/ML data science units (at 71%). What’s more, linking initiatives to a particular challenge is best addressed from the beginning. It’s a strategy Kellogg’s employs to ensure the brand is focusing its resources on “something that will matter to our actual business performance.” From there, CGs can formulate an IT plan, have conversations surrounding data accessibility, and establish a single uniform set of systems and data that provide cohesiveness and transparency. “Anyone who has ever worked for a large organization knows that information silos are a challenging fact of doing business,’’ says Lesley Salmon, senior vice president and CIO of Kellogg's. “The left hand doesn’t always know what the right hand is doing, and employees who are supposed to be working in concert are out of sync.” Saenz agrees that siloed efforts are rarely fruitful, whether implementing AI in pricing, marketing, or forecasting. And what really holds companies back from progress is a lack of enforcement in monitoring AI success.

"To get started, companies should launch self-governing pods of workers from marketing, operations, analytics, technology, and the commercial functions and invest them with clear goals, budgets, and decision rights."

DAVID C. EDELMAN AND MARK ABRAHAM "Customer Experience in the Age of AI," Harvard Business Review

Key Takeaways

Fill any gaps before committing to AI-based systems and processes. CGs must determine who will own the data and how they will disseminate the information to the company’s various departments to eliminate siloed efforts. Build an ecosystem that uses AI to solve existing challenges across the value chain, leveraging live data to inform business decisions related to consumers preferences, inventory needs, delivery optimization, and more. Ensure existing or new infrastructures can support the AI capabilities that are aligned with your key business strategies. Consider whether the organization has the appropriate system in place or if third-party vendors need to step in. Create a culture of education so key company members know how to leverage the technologies and can communicate findings and recommendations across the organization. Consider developing a dedicated data science team to manage the technology in order to support the business through continued growth.

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