Throughout a typical workday in a manufacturing plant, employees who oversee production processes spend much of their time problem-solving. They continuously look for more efficient ways to mitigate unexpected challenges, such as power outages, equipment breakdowns, or production capacity constraints.
In many instances, plant workers tackle problems with an ongoing, underlying cause—fixing the symptoms without solving the true issue. Manufacturing companies don’t drill down into the details and uncover the root cause of the issue, which may save operators time at the moment but will also cost more money in the long run.
This MacGyver-style “fixing”—using bubblegum and rubber bands to patch things up temporarily—does not ensure sustainable success or efficiency. An ad-hoc approach to plant production processes will not do and contributes to increased process variability and wasted materials.
Ongoing disturbances to supply and demand cast light on manufacturing operations that are ineffective. In a classic bullwhip effect, even the tiniest errors at the beginning of the supply chain will compound and create more significant problems down the line.
Manufacturers must find ways to double down on their supply chains to maximize their processes.
Leveraging digital tools, like machine learning for anomaly detection, makes plants more responsive and resilient. Data-backed centerline manufacturing approaches are crucial for a plant’s success.
What is Centerlining?
Centerlining is an approach to optimize machine efficiency and reduce process variability in manufacturing. This allows plant operators and managers to find their secret sauce—their golden batch—for production.
Manufacturing processes must continually meet a demanded outcome. This can be a product’s physical framework, such as its stiffness or length. Plants must also meet output parameters, including timing, temperatures, and other trackable KPIs.
These output parameters are constantly fluctuating. Centerline supply strategies ensure consistent manufacturing processes while allowing flexibility to deal with fluctuations and external influences. The goal is to keep all parameters within the determined tolerance range. Thus, the plant operates at full, efficient capacity.
What is Process Capability?
Manufacturers use a KPI called process capability to measure how well their processes are centerlined. Process capability is the consistency and repeatability of a manufacturer’s processes relative to consumer requirements in terms of specification limitations of product parameters. Process capability measurements hold plant operators accountable and objectively determine if production goals are being met or not.
Understanding Centerlining Processes
While processes differ from plant to plant, traditional centerlining processes involve four key steps:
1. Identifying the essential process variables
2. Determining the best ranges and settings for these variables
3. Determining how these variables will affect production processes
4. Ensuring the implementation of centerlining settings
To help you better understand the centerlining process, let’s look at the process of centerlining an item you may be more familiar with, such as the goal of making a cup of tea every morning that consistently tastes the same. The first step you’d take is to determine what variables affect the tea’s taste, such as the brand of tea and the amount of water in which you steep the bag.
Once you make your list of variables, you’ll need to understand the effect each one has. You might try different brands, add various amounts of sugar, and conduct taste tests.
Variables will interact differently, even during a process as simple as making the perfect cup of tea. For instance, Brand A might make the best cup with 1.5 cups of water, while Brand B will taste great with just one cup of water. To ensure you’re consistently making great-tasting tea, you should use a statistical method to analyze the results most effectively. This ensures you’re centerlining the optimal combination of process settings to reduce the amount of time and taste tests.
After creating the best processes to make the best-tasting cup, you’ll need to develop a detailed “recipe.” This recipe would include all the measurements and processes used to ensure you’ll easily make flavorful tea every morning.
Everyone else in your family should be able to follow your recipe to make the same decadent drink.
As manufacturing plant operators, you only wish true centerlining was as simple. Centerlining in manufacturing has to consider several dozen factors, if not more, such as raw material makeup, energy input and output, manual process efficiency, conveyor belt backups, storage tank integrity, and more.
The use of traditional methods to improve centerlining allows your competitors and their processes streamlined by digital tools to outpace you.
The Problems with Using Traditional Centerlining Tools and Methodologies
Taking a manual approach to centerlining your tea-making process could take hours. This would make you late to work for days on end and cost you your job.
In the same sense, manufacturers don’t have absorbent amounts of time to ensure they are meeting supply demands. Manual sampling throughout the production process to measure overall efficiency and human error allows for large variations in production “recipes”—making it nearly impossible to achieve optimal centerlined processes.
With traditional centerlining methods, machine setup information and adjustments are typically documented and printed on paper. This bottlenecks the improvement process as most employees won’t spend their days sifting through piles of paperwork, calculating data, and drawing conclusions on what this means. Thus, data goes unused, and inefficiencies continue.
Inconsistent Centerlining and Its Impact on the Bottom Line
While many manufacturing companies strive to achieve perfect centerlining, there are a few factors that come into play that can create an environment for inconsistent centerlining—traditional and manual centerlining methods being the biggest culprit. Incorrectly identifying the key centerline variables, establishing too large or too little of a variable range, and restricted access to clearly presented data can also halt the centerlining process.
Without consistent and accurate centerlining efforts, the following may occur:
- Decreased product quality
- Slowed production
- Downtime upticks
- Increased waste
- Higher costs
- Downtrends in revenue
Improving Manufacturing Centerlining with Digital Tools and Data Analytics
Manufacturers can optimize their centerlining processes by leveraging digital tools and data analytics.
Instead of manually recording data and then storing it away in boxes, never to be seen again, manufacturers can collect operational data directly from their equipment with no manual work needed. This data is accurate and free of human errors.
Additionally, the data is readily accessible and compiled into visual representations, making it easier for plant operators, data analysts, and floor managers to use the data and implement immediate improvements.
Digital tools don’t only automate and improve slow and ineffective measure processes, but they also use historical and real-time data along with smart algorithms to predict future occurrences, capacities, forecasts, and more.
Centerlining in your manufacturing facility has never been more effortless than it will be with data analytics tools, making your golden batch well within reach.
Say “Goodbye” to variability outside your set tolerances and get ready to decrease wasted materials significantly.
Easily Centerline Manufacturing Processes with Sightline EDM
Sightline EDM leverages machine learning, historical analytics, and predictive analytics to help manufacturers get to the root cause of problems while mitigating potential issues that might happen down the road.
Manufacturers implementing data analytics strategies ensure they’re optimizing processes—or brewing the perfect cup of tea—every day.
With Sightline EDM’s powerful data visualization and analytics tool, plant operators and floor managers access real-time data from machines. They then use this data to not only identify the key variable, create accurate variable limits, and quickly evaluate the effects of the variable, but they also use the EDM platform to gather historical, current, and predicted data that allows continuous centerlining improvement to occur.
Client Results from Sightline EDM Use in Centerlining Processes
Sightline EDM allows manufacturers to truly harness the power of their data. With simple integration into existing systems, your operations will experience endless benefits such as:
- Access to one easy-to-use dashboard that allows all employees to drill down into machine operation details
- Customizable alerts to issues and anomalies so plant operators can react quickly
- Real-time predictive analytics to determine machine downtime and productivity caps before they occur
Our powerful, out-of-the-box technology has already saved clients millions of dollars by eliminating wasted resources, increasing labor productivity, and streamlining machine inefficiencies.
Food manufacturer Renwood Mills used digital data analytics tools on one of their lines to test the efficiency advanced data analytics had centerlining their manufacturing processes and realized savings of $100,000.
An automotive manufacturer used digital data analytics tools to identify process improvement for a paint line resulting in $2 million of savings in a year.
Using digital advanced analytics tools to create effective and efficient centerlining practices in manufacturing is no longer an option. It’s vital to save money and time and to find the perfect “recipe” for the ultimate golden batch.Discover how Sightline EDM helps your plant employees with centerlining manufacturing. Schedule your free consultation with our process manufacturing experts today.
