20 Aug
2021

3 Questions Food Processing Companies Should Ask to Always Identify the Best Improvement Opportunities

Here are three areas food processing companies should focus on to find improvement opportunities.

Continuous Improvement
Downtime
OEE
OEE in Manufacturing Industry
Smart Factory
3 Questions Food Processing Companies Should Ask to Always Identify the Best Improvement Opportunities

Food processing companies face factory problems every day, including unplanned downtime, reduced throughput, hidden waste, and overproduction giveaway. Additionally, beyond the typical issues on the factory floor, many food processing companies are facing the same labor shortage other industries face.

With fewer people to perform manual tasks, digitization and automation have become even more important. Manufacturers need to ensure their factories are operating as efficiently as possible to keep up with consumer demand while keeping down costs. However, many food manufacturing companies are not aware of improvement opportunities because they’re not asking the right questions.

Though there is a long list of questions to ask when considering improvement opportunities, three of the most important questions revolve around downtime causes, worker performance, and using the right data.

1. Do you know all the causes of your unplanned downtime?

Though planned downtime is part of life in manufacturing, unplanned downtime is unexpected and usually the result of some sort of failure on the production line, from machine failure to excessive tool changeover to supply chain issues that hold everything up, causing obvious product waste. Unplanned downtime can also be the result of manual processes, where hard-to-catch human error can cause hidden waste that eats away at costs. 

Downtime can destroy production efficiency and severely impact throughput. Tracking and monitoring downtime is difficult if the process is manual or if your automated process doesn’t provide the necessary visibility. 

Implementing technology such as Worximity’s Downtime Monitor, which can track your data and provide clear KPIs, can help you maximize machine operations and reduce unplanned downtime. First, determine your production line machinery’s overall equipment effectiveness (OEE) so you have benchmarks by which you can measure improvement. Make determining OEE simpler by using Worximity’s OEE Calculator.

2. Have you taken measures to attract new workers or increase the skills of current floor employees?

Overcoming the current workforce shortage requires a combination of using digital factory solutions, attracting new workers, and cross-training current workers.

Implementing automation and robotics on factory floors can have a significant impact. There are many tasks that can be completed by automation or robots, including repetitive things workers don’t enjoy, such as picking, filling, and some packaging processes. Automating such processes can also cut down on hidden waste, which many times can be attributed to human error due to poor communication or lack of proper training. 

Plus, automating repetitive tasks creates factory roles that are more attractive to the right workers. 

However, finding workers skilled enough to work on the factory floor is getting more and more difficult. That’s why investing in your current staff is smart. 

Focusing on cross-training and diversifying your current workforce can help fill skills gaps, which will help bring more efficiencies. It’s not a matter of piling on new responsibilities, it’s about training up and truly engaging with your workers. When you invest in workers on such a level, it’s more likely that you’ll keep the skilled workers you need.

3. Do you have the best data for continuous improvement?

Making decisions based on the best data is the only road to successful continuous improvement. If any of your data is still collected manually, you’re likely not working with the best possible data. Or, if your current automation solution only gives you visibility into the past—even if it’s just a day—the data is simply not current and won’t give managers the whole picture of what’s happening on the floor. Looking at old data won’t help eliminate production mistakes.

The right automation tools provide managers with real-time data about what’s happening on the factory floor, from line feed speed to microstops to bottlenecks. For example, if you’re having consistent overproduction giveaway issues that impact product quality, that impacts sales and your ability to meet customer demand. If all machine settings look right, and you determine it’s not a worker-related problem, it could be difficult to identify why the problem persists. 

Comprehensive systems such as Worximity's Smart Factory analytics give managers the real-time visibility they need to identify issues such as machine operation inconsistencies, broken processes, and poor-quality products before they have a chance to make a major impact on the company’s bottom line. This could mean seeing that a machine is producing more than what its settings say it’s supposed to, identifying a crucial error immediately. 

Managers can also use smart factory analytics to measure worker performance, identifying issues as they occur instead of after they’ve become a problem.

Food Processing Companies Should Start with OEE Benchmarks

Worximity’s solution is comprehensive and provides the automatic data collection, monitoring software, and analytics that food processing companies need to enact real change and gain efficiencies. This more accurate data makes continuous improvement plans more meaningful, effective, and accurate.

Start with our OEE trial. If your plant is a fit for our OEE solution, you’ll get everything you need to collect with production line OEE data. It’s valuable information that highlights areas of improvement, from equipment health to labor efficiency. Your trial will reveal and confirm your real-time OEE to benchmark your efficiency against your industry, revealing efficiency opportunities on your shop floor and showing you how much money you should be saving.

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