The past several years have thrown numerous challenges at consumer packaged goods (CPG) companies. The pandemic has led to shifting consumer channel preferences, a supply chain crunch, and cost pressure, to name just a few. CPG titan Unilever has been answering the challenge with analytics and artificial intelligence (AI).

The 93-year-old, London-based CPG company is the world’s largest soap producer. Its products include food and condiments, toothpaste, beauty products and much more, including brands like Dove, Hellmann’s, and Ben & Jerry’s ice cream.

Alessandro Ventura, CIO and vice president of analytics and business services for North America at Unilever, has been at the forefront of helping the company apply AI to its businesses for years. While originally in the role of IT director, he has since added analytics and people services to his portfolio.

“That’s everything from facility management, fleet management, employee and facilities services, and people data, and that kind of stuff,” Ventura explains.

Unilever believes AI is not a technology of tomorrow. It’s already being widely used, and Ventura feels all industries will need to adapt to it.

In recent months, Unilever has developed a number of new technology applications to help its lines of business in the markets of tomorrow. One of the most important is “Alex,” short for Alexander the Great. Alex, powered by ChatGPT, filters emails in Unilever’s Consumer Engagement Center, sorting spam from real consumer messages. For the legitimate messages, it then recommends responses to Unilever’s human agents.

“Although Alex is good at what it does, it may lack a bit of a personal touch that instead our consumer engagement center agents have in big quantities,” Ventura says. “So, we let them decide whether they want to respond to our consumer as Alex suggested, or they want to add some personal recommendation; if the answer suggested by Alex is wrong or doesn’t have an answer, they can flag it so Alex can learn it the following time.” 

Generative AI in action

Alex was created using a system of neural networks, with ChatGPT for content generation. Ventura says the tool can understand what a consumer is asking and even capture the tone. It can then store the answer and sentiment in Salesforce. Importantly, he says, the tool does the heavy lifting on those tasks, giving the human agents more time to dedicate to what they do best. To date, Ventura says Alex has helped Unilever reduce the amount of time agents spend drafting an answer by more than 90%.

Another Unilever tool, called Homer, leverages ChatGPT to generate content. It’s a neural network that takes a few details about a product and generates an Amazon product listing, with a short description and long description that matches the brand tone.

“We want to ensure we captured the voice of the brand so, for example, that we differentiate between a TRESemmé and a Dove shampoo, and the system got it absolutely nailed,” Ventura says. 

Another AI-based tool that Unilever launched on the week of US Thanksgiving supports the Hellmann’s mayonnaise brand. Its purpose is to reduce food waste.

“It links up with the recipe management system that we have at Hellmann’s, so somebody can go in and select two or three ingredients that they have in the fridge and get in exchange recipes for what they can do with those ingredients,” Ventura says.

In the first week, the tool got 80,000 users who reported loving it.

For Ventura, that’s the magic of analytics and AI in the CPG space: It enables personalization at scale.

“In CPG, we rely more and more on analytics and AI for different things,” he says. “Consumers are more and more specific about what they want. It’s a bit of a cliché, but they really do want personalized products and experiences. Analytics helps CPG to understand the context they’re navigating through and what the consumer wants, and then, with AI, we can scale that one-to-one relationship across all the multitude of consumers that we have.”

Co-creation key to AI success

Beyond the consumer relationship, analytics and AI are also key to making CPG companies more sustainable. Ventura points to examples like ingredient traceability and using machine learning (ML) to automate forecasting, which in turn helps the company minimize waste. Unilever is also applying analytics and AI to logistics, including tracking inventory and optimizing routes.

“The old interpretation of elasticity, we threw it out the window,” Ventura says of operations in the wake of the inflation crisis. “We had to come up with new calculations because the traditional ones were giving us very different scenarios from what we were seeing happening at the shelves. Going forward, we will continue to see that pressure from all the different challenges coming from the geopolitical situation around the world.” 

To support its innovation around analytics and AI, Unilever has adopted a hybrid model. It has a global center of excellence, but also keeps some data scientists embedded with business units.

“It’s basically a two-gear system,” Ventura says. “The local team can be activated very quickly, ingest the data very quickly, and then create a statistical model and analytics model together with the business, sitting next to each other. Then, if that model can be leveraged across and scaled, we pass it on to the global team so they can move data sets in the global data lake that we have and can start creating and maintaining that model at a global level.” 

Ventura believes co-creation and co-ownership of analytics and AI capabilities with the business function is essential to success.

“Whether it is machine learning for automating the forecast or Alex with the Consumer Engagement Center, if we show up with a black box and say, ‘Hey, follow whatever the machine tells you,’ it will take a long time and probably will never get to 100% trust in the machine,” Ventura says. “With co-creation and co-ownership, I feel like we get to start with the right foot, with the human and the machine working alongside each other in partnership, almost as colleagues. Also, you get a much less biased system in the end because you’re able to introduce a much more diverse angle in your algorithms, both from a business perspective and a technology perspective.” 

Artificial Intelligence, Digital Transformation

From highways to parking lots to tennis courts and more, asphalt is ubiquitous in modern life. It can also be highly dangerous under high temperatures such as those used in processing the petroleum-based substance. According to the US Occupational Safety and Health Administration (OSHA), more than a dozen heated storage tanks for asphalt or No. 6 fuel oil have exploded in the past decade.

To improve the safety of its asphalt operations, US-based Owens Corning has put data analytics to work, leveraging low-code tools to develop a digital platform that incorporates multiple data flows and enables previously plant-specific information to be shared and coordinated across the company’s operations.

“This project was driven by a need to create real-time visibility to data with actionable insights to prevent hazards and enhance safety in operating asphalt processing tanks across our manufacturing network,” says Malavika Melkote, director of IT and the Analytics Center of Excellence (COE) at Owens Corning.

The project, which has earned Owens Corning a CIO 100 Award in IT Excellence, leverages digitizing sensors to extract data from asphalt tanks. This data is integrated with a range of other data points, providing easy-to-use visuals for plant operators to analyze, Melkote says. “They can quickly assess potential hazards and risks in real-time and proactively take preventive actions.”

The increased visibility into potential hazards enabled by the loss prevention platform, combined with preventive maintenance, has minimized unplanned production outages and created sizable cost savings via reduced equipment losses. Melkote says the time required to make a decision and take action at its plants has gone from days to minutes, thanks to the platform.

The MVP approach

Monitoring and managing an asphalt tank’s vapor space is critical to safety and compliance with Title V of the federal Clean Air Act. Prior to developing the loss prevention platform, Owens Corning and the rest of the asphalt industry collected vapor space data in offline databases. Those databases were plant-specific and the information they contained was difficult to share and coordinate across the company.

“Through our Loss Prevention discovery process, it became evident that the monitoring and managing of our asphalt tanks’ vapor space was a critical component to safety and regulatory compliance,” says Frank Burg, asphalt manufacturing support leader at Owens Corning. “While there was a rigorous process to manage safety and compliance, there was a big opportunity to make it more efficient and scalable across plants through automation and analytics. The opportunity was to fully digitize the data collected from tanks, automate integration of multiple data flows, and provide tools to plant personnel to analyze data, to highlight and assess risks and hazards for quick actions.”

In August 2021, Owens Corning set developing the loss prevention platform. A cross-functional team across Environment, Health, Safety (EHS), engineering, controllers, and plant leaders worked together with the IT analytics COE to lay out the vision and roadmap. To address the biggest pain points of the current process, the team identified three key requirements for the platform: It needed to provide timely access to data and proactive analytics that highlight risks; give operators the ability to share insights, actions, and learnings across plants; and be easy enough to be used by a diverse group of tank operators with varying degrees of technology experience. The team felt the best approach would be to leverage low-code tools.

“A key consideration was to enable the business team, with a citizen developer, to enhance, operate, and manage the solution long-term,” Melkote says. “Giving more control to business users to enhance the solution was a driver to choose a low-code technology platform.”

The resulting system is a single source of truth for loss prevention data, with proactive monitoring and analytics that alert plant leaders with real-time insights for hazard prevention. It uses a combination of advanced analytics and machine learning to perform relational data analysis beyond the raw data.

The development team, led by Muhammad Shoib, enterprise information architect at Owens Corning, took a minimum viable product (MVP) development approach, creating a proof-of-concept that was deployed at one plant within three weeks. That provided valuable feedback for fine-tuning the process and provided excitement among users and the business team. With their support, Shoib’s team scaled the solution and fully deployed it in 17 plants within three months.

“The MVP took two weeks to build and let users experience the solution to give feedback,” Shoib says. “We then moved to a pilot for one plant, which took eight weeks of iterative development. Deployment at the first plant took two weeks plus hyper care to fully operationalize the solution — to get end users, the product owner, and the citizen developer comfortable.”

Melkote says the pace of adoption and the minimal training required for end users was a pleasant surprise. “The iterative approach to developing an MVP, pilot, and the first plant implementation minimized any hiccups,” Melkote says.

Empowering business users

The approach undertaken for the project represents a significant shift for Owens Corning, which previously relied on a traditional approach to solution delivery, with the full scope solution delivered to the business for testing and validation. Melkote says getting the business tuned into an iterative method of co-developing the solution was a big change, but ultimately a worthwhile one.

“The MVP set the tone and the speed for what was possible that the users could touch and feel,” Melkote says. “They got onboard after the MVP to support the monthly release schedule of functionality.”

Since the platform has been deployed, Owens Corning has replaced personal tools and siloed information with a digital platform accessible across the enterprise, Melkote says. That, in turn, has enabled more efficient data flows and analytics that have increased the speed of decision-making such that the business can now solve more use cases with minimal IT involvement.

“Our business team now manages the solution,” Melkote says. “They felt empowered to enhance the solution at their pace. Dependence on IT to prioritize their enhancements is no longer an issue.”

Melkote now heartily recommends the MVP and low-code approach to her peers.

“Start small with an MVP, let your business partners experience the solution, see the value quickly,” Melkote says. “Put your business in the driver’s seat with the right roles; they manage the speed, functionality. Continue to nurture citizen developers, business product owners; be the best business partner. Let your business partners share stories and experiences and market the success stories.”

CIO 100, Digital Transformation, Manufacturing Industry