by Martha Heller

Predictive analytics unlocks factory capacity at Reynolds

Apr 29, 2020
AnalyticsManufacturing IndustryRobotics

Rita Fisher, CIO and SVP of Supply Chain, discusses her approach to digitizing plant operations at the global consumer products company.

Rita Fisher, CIO and SVP of Supply Chain, Reynolds Consumer Products
Credit: Reynolds Consumer Products

When customers demand faster delivery and lower costs, consumer packaged goods companies need to optimize their manufacturing and supply chain operations. That’s where intelligent factories come in. But how do you drive change in factories that have been run in the same way for decades? According to Rita Fisher, CIO and SVP of Supply Chain at Reynolds Consumer Products, it is about showing a plant early value, and then being with them every step of the way.

Martha Heller: How are digital technologies impacting Reynolds Consumer Products?

Rita Fisher: Even as recently as a year ago, the expectations of our retail customers have multiplied in the branded side of the business. Our customers used to give us orders weeks in advance of delivery, and now it’s a matter of days. The idea that it will take years to introduce a new product is gone. Now, the timeline to introduce products is typically six months or less, and that’s from the idea for a new product to that product being on the shelf available to consumers.

Reynolds Consumer Products (RCP) also has a private label business, and for those customers, it is all about price and quality.

The question we our asking ourselves is: what kind of supply chain and manufacturing capabilities do we need to deliver on these new customer expectations in speed-to-market and lowest cost-to-serve?

How are you creating the cultural change necessary for digital transformation?

Our vision is to digitize our company with full support from the RCP leadership team. We started with the knowledge that customer and consumer expectations are changing, and that the only way we can meet those expectations is by rethinking and reimagining how we run our business.

What is the vision for digitizing the company?

Our vision for the digital enterprise is built on five major pillars: 

Digitizing the customer experience. Our work here centers around learning how to plan and execute better with our customers, which are Walmart, Sam’s, Costco, Kroger, Amazon, and others.

The digital supply chain. This includes enabling e-commerce solutions for our customers, and digitizing our planning, distribution, and transportation processes. It means using analytics to make better decisions early, rather than being reactive.

The intelligent factory. This program is changing how we think about delivering a product in the factory. It’s much more than automation; it is teaching ourselves how to work differently.

The digital workforce. This pillar involves robotics and creating a data-driven and analytical organization where employee time and skills can be used for more strategic efforts rather than repetitive tasks easily automated by technology.

Digital products and services. This is really about introducing new business models and thinking of new ways to create revenue.

Let’s focus in on the intelligent factory. What does that program entail?

The intelligent factory program has three major components.

The first is operational transformation, which instills robust, Lean principles into our manufacturing processes, and delivers better data to drive decisions. It’s not only about the data, it’s about showing people how to turn the data into insights and the insights into action to achieve business benefits.

The second component is the digital manufacturing group, which we built to work directly with the plants, to help them with technology expertise, implementation, and change management.

And the third is actually building the intelligent factories, which use IoT and data science to provide predictive insights that improve overall throughput and reduce our costs. Here, we’ve deployed new data collection tools and software to enable production tracking and monitoring.  We are also using data science for predictive capabilities.

What kind of results is the intelligent factory demonstrating?

Now that the program is in place, we are seeing real results. For example, in reviewing six months of plant data, we were able to determine the most optimal settings for every line, to decrease downtime and scrap to achieve maximum capacity. That information has allowed us significantly to increase our output without additional investments.

But it is in the area of predictive analytics that we are seeing the best bang for the buck. Sensors on the line can indicate that the equipment is in need of service before the line goes down completely. When the production managers know ahead of time that a line could go down, they can do preventative maintenance. The software that we are using even prescribes what maintenance steps to take based on the condition of the machine.  All of this means they can produce more with the same equipment. 

That was a huge eye-opener for the manufacturing teams, where they truly saw the value in the program. Through predictive maintenance, we are unlocking a lot of capacity.

What have been some challenges in rolling out the intelligent factory?

The first challenge was purely technical. We had made assumptions about what capabilities the plants had and how everything worked. But we had to adjust our plan once we looked further into implementation.

The second challenge was getting people in the plant to buy into the intelligent factory program. Jobs were changing, and we wanted to be sure all team members joined us in this effort.

We learned that we had to help people in the plants see what is possible before we could get them fully on board. We are bringing in new tools, but also new training to show people how to get the most value.

How did you bring the people along?

It is so critical to the success of the program to make sure your early pilots are successful, because our new investments change what have historically been viewed as manufacturing best practices.

We didn’t build the entire platform right away, because that would be complex, so we prioritized in an effort to make thoughtful and deliberate steps in the process.

Day by day, we were there with the team members in the plants every step of the way. We learned that you cannot run a program like this without having resources on-site. You have to be there physically. You have to become part of their team and a trusted partner.