How to Use Data to Streamline Your Manufacturing Process

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How to Use Data to Streamline Your Manufacturing Process

Get connected and take control of your processes from production to delivery

[/vc_column_text][vc_single_image image=”37077″ img_size=”full” alignment=”center”][/vc_column][vc_column width=”1/6″][/vc_column][/vc_row][vc_row css=”.vc_custom_1624970177671{padding-top: 3% !important;}”][vc_column width=”1/4″][/vc_column][vc_column width=”1/2″][vc_column_text]It certainly seems as if we’ve been hearing “big data” talk continually for years now. While it’s true that in some respects we’re well into the so-called big data revolution though, it is also fair to say we’ve only just begun to see the impact of proper data usage. Current projections of worldwide data at Network World suggest that there will be an astounding 175 zettabytes in the world by 2025 — and much of it will be gathered in clouds for analysis by businesses.[/vc_column_text][vc_column_text]That number pertains to all data, but it still serves to emphasize that if anything it’s as important as ever for big businesses to take their data operations seriously moving forward. That brings us to our core question of how you can use data to streamline and improve your manufacturing process.[/vc_column_text][vc_column_text]To some extent this will always depend on the specific nature of your company and its size, performance, and goals. However, we do have some general tips that will help you to use data more effectively.[/vc_column_text][vc_empty_space][vc_column_text]

Identify Desired Metrics

[/vc_column_text][vc_column_text]One of the mistakes a lot of companies make today — in manufacturing or elsewhere — is to start one version or another of data collection and analytics without first identifying what exactly they’re looking for. And yet, as was stated nicely in a more general overview of business leaders and big data at Entrepreneur recently, exceptional companies “identify specific metrics that set them apart from competitors, align with specific goal sets or reveal what’s happening in their industry.” This is another way of saying that the companies that use data most effectively do so by focusing on what is truly relevant and helpful for their industries.

Practically speaking, you should identify the desired metrics that you believe will help you excel in streamlining manufacturing, and build your collection and analytical effort to revolve around them.[/vc_column_text][vc_empty_space][vc_column_text]

Explore Expert Input

[/vc_column_text][vc_column_text]Another common mistake business leaders make is to take on data efforts on their own without necessarily having the relevant expertise. This can be understandable if resources are limited, but the proper way to implement data is to explore expert input. And you may have more options than you expect. Consulting with qualified data analysts will help you to ensure you’re going about your own data operation correctly.[/vc_column_text][vc_empty_space][vc_column_text]

Use the Power of IoT & Predictive Algorithms

[/vc_column_text][vc_column_text]In terms of more practical steps, you (and any data expert(s) you may employ to help) should make a point of using IoT systems and edge analytics for initial gathering and classification of information. If you’re wholly unfamiliar with data operations in manufacturing it’s important to recognize that you aren’t merely meant to deal with manual observation and results. Rather, there are all sorts of IoT-connected sensors and machines that can be used in conjunction with your manufacturing equipment, and which automatically track everything from production time, to maintenance needs, to inventory management — and ultimately help with fleet management and product tracking as well. Altogether, these perks help you to oversee processes from production to delivery.[/vc_column_text][vc_column_text]Edge analytics, meanwhile, is almost like a more in-depth version of that same kind of information tracking. A regular data analytics operation typically funnels information from various IoT points to a central hub where it can be sorted and analyzed. Edge analytics refers to a similar system in which some degree of analysis (as opposed to raw data gathering) is performed at sensors and nodes. This can save time during the back-end analytical process for companies that may have limited resources, or cannot employ an expert.[/vc_column_text][vc_column_text]Beyond the IoT and edge analytics, you may also be able to put predictive algorithms to use — almost as a means of preempting data findings and getting ahead of the curve. This is something ei3 deals with specifically, as was explained in an overview of predictive machine operations put to use by Milacron. This example shows how past machine performance can be used to produce algorithms that will in turn analyze and assess other machines to predict issues in advance. It’s not what one typically thinks of when considering data analytics, but it is one more means of heading off issues to streamline manufacturing operations.[/vc_column_text][vc_empty_space][vc_column_text]

Prepare to Adjust Processes

[/vc_column_text][vc_column_text]Technical methods aside, another crucial aspect of using data in manufacturing is being prepared to adjust processes as needed. Once you’ve collected a meaningful amount of data and made sense of it, you may learn that there are clear inefficiencies in how you use resources. You may find that a given machine is performing more slowly than others even if it doesn’t seem to be in real-time; you may find that you’re utilizing the same resources on the production of two products when one performs much better than the other. These are only a few examples, but they speak to the kinds of adjustments you need to be prepared to make. Analysis of manufacturing data is ultimately only as valuable as your response to it.[/vc_column_text][vc_empty_space][vc_column_text]

Assess the Market & Customer Needs

[/vc_column_text][vc_column_text]Another important thing to recognize as you undertake this kind of operation is that you aren’t only meant to focus on internal data. Yes, technology like IoT in manufacturing or predictive algorithms in your machine usage will help you to gain insights and improve your own efficiency. But the other side of all this is that you also need to analyze the market you’re in, and the needs of your customers (or partners). Data collection on this end of things can entail anything from customer surveys to social listening. But in the end, having a clear picture of which products are most wanted and why will help you to streamline manufacturing all the more effectively.[/vc_column_text][vc_column_text]As stated previously, the specifics of your data operation — from the metrics you highlight, to the methods you use, to your capacity to use findings — will always depend on your specific business. No two operations of this kind look exactly alike. Broadly speaking though, the tips and strategies above outline how you can put a comprehensive data plan into place in order to streamline your manufacturing process. Done correctly, this can work wonders for your business.[/vc_column_text][vc_column_text]For ei3 by Allie Cooper[/vc_column_text][/vc_column][vc_column width=”1/4″][/vc_column][/vc_row]