IoT in ingredient handling and mixing: questions, answers and next steps

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IoT in ingredient handling and mixing: questions, answers and next steps

26 Feb 2021

Liz Goodwin

Liz Goodwin is a Texas-born journalist and photographer. Although a Southerner at heart, she fell in love with the Midwest at the University of Missouri where she earned her bachelor's of journalism. When she’s not writing or behind a camera, she loves to cook, find new restaurants, and spend time outside. Her favorite baked goods include croissants and crackers, especially when topped with fig jam and brie.

As technology revolutionizes every industry, commercial baking is no exception. Automation is key in performance and efficiency, and bakers are looking for ways to track automated machine performance to improve the process. That’s where new technologies like “the Internet of things,” or IoT, come into play — taking automation to the next level by introducing cloud data storage and other internet programs. These programs learn as they go and are constantly monitoring and tracking ingredient flow, equipment performance and more. And it helps bakers stay one step ahead in the manufacturing process.

Several veterans of the commercial baking industry came together at the American Society of Baking’s BakingTECH conference, held Feb. 16-18, to discuss how IoT could help bakers with ingredient handling and mixing. They broke down how this technology could present data in an accessible format to avoid run-to-failure scenarios among other benefits the program has to offer.

Here’s a snippet of the questions and answers discussed by panelists Luis Vargas of Horsham, PA-based Bimbo Bakeries USA, Jeff Teasdale of Camden, NJ-based Campbell Snacks, Jason Stricker of Shick Esteve and Andrew McGhie of Shaffer, a Bundy Baking Solution. The panel was led by Dave Watson with the Austin Company.

These responses have been edited for length and clarity. Attendees can view the full session here.

The advent of IoT has provided many tools for managing, reporting and tracking downtime, maintenance operations and activities. What tools are available to bakers as it relates to ingredient handling and mixing? How affordable are these technologies?

“The way a platform displays information can go a long way to allowing bakers to make informed decisions.”

Stricker: The shift in both effectiveness and affordability of IoT in the baking industry has a lot to do with lower-cost sensors, but most importantly with the platform used to aggregate the data and provide that to operators and decision makers in an easy-to-understand format. In the early days, there was a challenge with the enormity of data, and it was difficult to understand — you were kind of paralyzed by how much was available. The way a platform displays information can go a long way to allowing bakers to make informed decisions. Where we sit today, both the devices and the platform itself are very affordable. There shouldn’t be a prohibitive cost to inhibit entry into this technology.

McGhie: There’s always been a lot of information available, but getting access to it and understanding it has been difficult. Another thing is to understand what you are trying to achieve. Just because you can do something doesn’t mean it’s always the smart thing to do, so understand what is available and how it can help bakers make better product and run more efficiently. And it’s also important to make the programs user-friendly and easy to understand. Having it be in the cloud and accessible to many players will be helpful.

From a baker’s standpoint, what are your companies doing today to leverage this technology?

Vargas: At Bimbo Bakeries, we are embarking on a project to visualize operations and line performance and are aiming to have at least half of our bakeries hop to these kinds of platforms. We want to collect that data and track performance over time, and we want that information to be available to frontline associates and supervisors as well as management. We are excited to use this technology to improve performance in our bakeries.

From a predictive maintenance standpoint, how are bakers and equipment suppliers using these smart technologies to improve plant performance and reduce unplanned downtime? Are we getting to a place where we can truly predict and plan for downtime for major repairs?

Stricker: When it comes to IoT, the preventative and predictive maintenance measures are one of the easier parts to understand. It can monitor performance critical parameters such as filter life in the system. That, in particular, can be hard to catch on your own because it is quiet and doesn’t make noise. But this technology can actually predict the end of life of those filters and allow you to schedule your downtime to make those repairs days or weeks out. It also learns as it goes and can predict the amount of time you have left in your current operation before you need to perform maintenance. This constant monitoring allows a windshield view into what is coming.

Teasdale: I’ll also note that these predictive features can be for avoiding breakdowns and under-performance, but it can also go the other way. Some sites can get into the habit of, let’s say, filter changes, to Stricker’s example, every certain period of time – but maybe that’s overkill. Maybe those predictive features can tell you when you don’t need to change something. Data is power, so it can help you both ways.

Stricker: You know, it’s all about highest efficiency possible. We want to prevent run-to-failure conditions, but that’s a great point. There are plenty of times where people are doing things unnecessarily. And this can really help optimize operational expenses and maintenance periods.

McGhie: On a simple thing like mixer reducers and the sensors on those, if there is a variation of more than two decibels, bakers can put that on a maintenance schedule to check on it. That way they can be proactive and not do something just on route. Let’s only do it when we really need it, and it can be flagged. It doesn’t become an urgent thing. It can be a really prescriptive tool.

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