{"id":226958,"date":"2023-02-21T17:15:14","date_gmt":"2023-02-21T22:15:14","guid":{"rendered":"https:\/\/www.sightline.com\/?p=226958"},"modified":"2025-11-21T11:29:46","modified_gmt":"2025-11-21T16:29:46","slug":"reducing-variability-and-waste-with-centerlining","status":"publish","type":"post","link":"https:\/\/www.sightline.com\/reducing-variability-and-waste-with-centerlining\/blogs\/","title":{"rendered":"Reduce Variability & Waste: Improving Manufacturing Processes with Centerlining Tools"},"content":{"rendered":"\n
\"Manufacturers-can-use-digital-tools-to-improve-centerlining-and-optimize-efficiency.\"<\/figure>\n\n\n\n

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.\u00a0In many instances, plant workers tackle problems with an ongoing, underlying cause\u2014fixing the symptoms without solving the true issue. Manufacturing companies don\u2019t 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. There are other ways to improve manufacturing such as centerlining. <\/p>\n\n\n

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\"Leverage<\/a><\/figure>\n<\/div>\n\n\n

This MacGyver-style \u201cfixing\u201d\u2014using bubblegum and rubber bands to patch things up temporarily\u2014does 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.<\/p>\n\n\n\n

Leveraging digital tools<\/a>, like machine learning for anomaly detection, makes plants more responsive and resilient. Data-backed centerline manufacturing approaches are crucial for a plant\u2019s success.<\/p>\n\n\n\n

What is Centerlining?<\/strong><\/h2>\n\n\n\n

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\u2014their golden batch\u2014for production.<\/p>\n\n\n\n

Manufacturing processes must continually meet a demanded outcome. This can be a product\u2019s physical framework, such as its stiffness or length. Plants must also meet output parameters, including timing, temperatures, and other trackable KPIs. <\/p>\n\n\n\n

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.<\/p>\n\n\n\n

What is Process Capability?<\/strong><\/h2>\n\n\n\n

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\u2019s 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.<\/p>\n\n\n\n

Understanding Centerlining Processes<\/strong><\/h2>\n\n\n\n

While processes differ from plant to plant, traditional centerlining processes involve four key steps: <\/p>\n\n\n\n

1. Identifying the essential process variables<\/p>\n\n\n\n

2. Determining the best ranges and settings for these variables<\/p>\n\n\n\n

3. Determining how these variables will affect production processes<\/p>\n\n\n\n

4. Ensuring the implementation of centerlining settings <\/p>\n\n\n\n

To help you better understand the centerlining process, let\u2019s 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\u2019d take is to determine what variables affect the tea\u2019s taste, such as the brand of tea and the amount of water in which you steep the bag. <\/p>\n\n\n\n

Once you make your list of variables, you\u2019ll need to understand the effect each one has. You might try different brands, add various amounts of sugar, and conduct taste tests. <\/p>\n\n\n\n

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\u2019re consistently making great-tasting tea, you should use a statistical method to analyze the results most effectively. This ensures you\u2019re centerlining the optimal combination of process settings to reduce the amount of time and taste tests. <\/p>\n\n\n\n

After creating the best processes to make the best-tasting cup, you\u2019ll need to develop a detailed \u201crecipe.\u201d This recipe would include all the measurements and processes used to ensure you\u2019ll easily make flavorful tea every morning.<\/p>\n\n\n\n

Everyone else in your family should be able to follow your recipe to make the same decadent drink.<\/p>\n\n\n\n

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. <\/p>\n\n\n\n

The use of traditional methods to improve centerlining allows your competitors and their processes streamlined by digital tools to outpace you.<\/p>\n\n\n\n

\"Check<\/a><\/figure>\n\n\n\n

The Problems with Using Traditional Centerlining Tools and Methodologies<\/strong><\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

In the same sense, manufacturers don\u2019t 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 \u201crecipes\u201d\u2014making it nearly impossible to achieve optimal centerlined processes.<\/p>\n\n\n\n

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\u2019t spend their days sifting through piles of paperwork, calculating data, and drawing conclusions on what this means. Thus, data goes unused, and inefficiencies continue.<\/p>\n\n\n\n

Inconsistent Centerlining and Its Impact on the Bottom Line<\/strong><\/h2>\n\n\n\n

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\u2014traditional 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.<\/p>\n\n\n\n

Without consistent and accurate centerlining efforts, the following may occur:<\/p>\n\n\n\n