How Big Data in Manufacturing Leads to Perfect Production
For every person on the planet today roughly 1.7 MB data is created per second. In manufacturing, data is created by machines and sensors, operator inputs on the factory floor, quality systems, ERP and MES systems and more. The term “big data” was coined by Roger Mougalas in 2005 and primarily refers to the large volumes of data that require more processing power than the average business intelligence tool can handle. Big data in manufacturing can be defined by the tremendous amount of data factories are collecting and the analytical tools used to turn data into actionable insights.
Find the following in the read below: The Three vs Of Big Data, How Is Big Data Used, Why Is Big Data Essential, Apply Big Data To Your Industry, Big Data Applications
The History Of Data In Manufacturing Machine learning in manufacturing
The earliest evidence of effective use of data to regulate business activities dates back 7000 years ago to the banks of Tigris and Euphrates. Although confined to simple tracking and controlling, this marked the birth and beginning of conscious accounting. Across the expanse of history, from mortality data logs during the Great Plague to the implementation of the Social Security Act of 1937, we’ve seen data consistently being harnessed to improve the efficiency and accuracy of decision making.
The beginning of large data sets can be traced to the1960s and ’70s with creation of the first data centers and development of the relational database. Businesses today fully realize the hidden potential that lies dormant within these data. Organizations are constantly on the lookout for ways to harvest data with the power of analytics for improved decision making.
Over the past 20 years, manufacturers started using data to reduce variability in the production process, as well as improve production and yield.
Advancements in cloud computing have made storing big data in manufacturing easier which has opened endless avenues to turn routine production runs into actionable insights that can make a big difference on the bottom line.
What Is Big Data: Meaning And DefinitionMachine learning in manufacturing
Gartner defines big data as high-volume, high-velocity, and/or high-variety information assets that demand cost effective, innovative forms of information processing that enable enhanced insight, decision making and process automation. In other words, big data includes complex and diverse streams of data obtained from various sources and requires advanced processing methods such as machine learning and cloud-computing. Once the data is processed, these tools provide insights for improved decision making and process automation.
The Three Vs Of Big Data Big data use cases
Volume: Big Data involves processing large volumes of data which are composed of strings of structured, semi-structured and unstructured data collected by manufacturers.
Velocity: Velocity refers to the unprecedented rate at which data is received. Typically, these data streams are stored in memory, but real-time processing mechanisms are also feasible.
Variety: Variety simply refers to the different types of data available. Traditional data types are structured, and hence seamlessly fit into relational databases. The advent of big data brought with it a wealth of structured, semi-structured and unstructured data types. These data types require additional processing for meaningful interpretation.
How Big Data Is Used In ManufacturingMachine learning in manufacturing
Big data is a buzzword that has been making the rounds across almost every industry. Its implications in the world of manufacturing are tremendous. Efficiently and effectively gathering big data can transform a company from reactive decision making to proactive and predictive decision making.
The advent of sensors has contributed to the significant increase in data. It goes without saying that all of this data being collected means very little unless interpreted with effective tools. Data visualization is an extremely helpful tool for determining insights from your data.
The role of big data in manufacturing is rapidly growing and can largely be attributed to a variety of reasons like the availability of skilled personnel, competitive advantage, and sustainable manufacturing. Today, manufacturers are aggressively on a pursuit to identify their ideal production process using the power of data.
Big Data Use Cases
Big data finds purpose in an array of applications such as improving manufacturing, driving quality assurance and managing supply chain efficiency.
Numerous scenarios around the production environment require intervention from personnel on the factory floor. Actions that might need to be taken include adjustments to prevent imminent failures, optimize production, or maximize throughput. Other actions that might need to be taken can involve improving equipment efficiency and minimizing energy consumptions. The combination of big data, artificial intelligence and machine learning can effectively empower factory personnel in improved decision making.
Decision Support Systems (DSS): Decision support systems gather, process and analyze data to draw valuable insights from them. They can either be fully computerized or powered by factory personnel. Ideally, it is a combination of both. DSS analyze information and enable timely and efficient decision making. In other words, DSS are tools that help the intervening personnel decide when and what course of action should be initiated.
Recommendation Engine: Recommendation engines can be viewed as the subtler version of the Decision support system. They promptly notify the floor staff when an intervention is required. It also offers a list of possible actions from which the staff can choose the most appropriate.
While there may be over a dozen different ways to make a product, identifying the most profitable one will make all the difference. Oden’s Golden Run™ is a proprietary recommendation engine specifically designed to address manufacturers’ need for efficiency and to identify the most profitable way to make a product. It enables manufacturers to solve problems faster than ever before and unlock cost-saving efficiencies from data that would have remained hidden otherwise. Harnessing the power of machine learning, Golden Run constantly seeks to identify avenues for improvement where changes can be implemented to continually realize cost-savings.
Agility In Response To Market Demand Fluctuation
The ability to forecast the future would certainly be regarded as a valuable skill in any business. The ability to do it in real-time would be even better. The good news is, with the right system in place, this is possible. Scrutiny of data from CRM can reflect the difference in consumption and order patterns. This can be used accordingly to drive production adjustments. If the system can garner sufficient intelligence from the CRM to forecast interest before the completion of the sales cycle, the data can be leveraged to populate the production planning for the manufacturing unit, then it will minimize lead time to respond.
Cost Of Quality
Big data can be effectively used to reduce in-process deviations to reduce wastage. Leveraging the data available from sensors on turning shafts and tool jaws, deviations in specifications can be easily predicted. For instance, let’s say the tolerance for deviation in a shaft is pegged at 2mm. When the deviation is about 1.5mm or 1.4mm, the available data can be used to prompt action. This will help minimize rejects and thereby reduce rejects and hence ultimately the cost of quality.
Predictive And Preventive Maintenance
Sensor technology has almost completely changed the way we perceive the world today. Sensors diligently collect and send data for real-time processing to respective systems. These processed datasets are analyzed with predefined pattern recognition methods which help predict upcoming failures. This allows for the efficient prevention of downtimes due to maintenance. Preventive maintenance also plays an important role in prolonging the lifespan of equipment by averting irreversible failures.
While preventive maintenance largely depends on data patterns, predictive maintenance also factors the usage of the equipment. Data is used to fashion periodic routines to ensure consistency and optimal efficiency. Preventive maintenance also helps reduce machine downtimes and warranty costs.
Why Is Big Data Essential To Achieve Perfect Production
Every unit of data has a story to tell. Comprehensive integration of data collected from various sources portrays the bigger picture. Efforts to combine data from sensors, quality aspects, maintenance logs, and design efficiency can reveal patterns that would help make well-informed decisions.
The goal of every manufacturer is to achieve perfect production. Many manufacturing lines are rocked with inefficiencies. Even OEE (Overall Equipment Effectiveness) the measure of success is fixed at 85% for world-class manufacturing, which reveals how far manufacturing processes are from perfect production. Every incremental change in percentage will translate into profitability and strengthen the competitive advantage. Hence, production perfection is essentially the holy grail for manufacturers
The past 30 years have seen manufacturers relentlessly implement lean and continuous methodologies to consistently improve factory performances. Today, we find data at the core of such improvement efforts. The importance of data in this regard can hardly be overstated. Research shows that the effective use of data can easily bring a 10 to 15% improvement in productivity.
Let’s take a closer look as to how big data helps manufacturers achieve perfect production.
Predictive Testing Manufacturing data analysis
Let’s assume there’s a slight change in the specification of the product that is manufactured due to a glitch in the equipment. If unnoticed, chances are this could lead to continued production of these units of incorrect specifications, creating waste. Predictive testing can constantly monitor for any deviance from conforming standards. It is the platform for real-time data analysis that can offer rapid feedback to the appropriate personnel.
Similarly, predictive testing can also help avoid unnecessary downtime by preventing asset failure by analyzing production data to identify patterns and predict issues before they happen. Factory and plant personnel generally schedule periodic maintenance and regular replacement of machine parts to avoid downtime. This may be effective in averting downtime, but it certainly isn’t the best way forward. Statistics show that in addition to consuming unnecessary resources and driving productivity losses, more than 50% of all predictive activities are ineffective.
In the world of manufacturing, time is always of the essence. Unpreparedness and unanticipated downtimes can prove to be very costly. Predictive testing goes a long way in helping manufacturers accelerate the journey between the shop floor to the store shelves.
Finding Unexpected Insights
Despite stringent adherence to best practices followed by top manufacturing firms, there will always be room for improvement. Big data analytics can be used to reveal new innovative ways to improve productivity. Insights not only answer questions relevant to pressing issues that manufacturers seek to fix, but also reveal useful patterns that help make accurate inferences.
Big data analysis provides the necessary vantage point into complex issues that may otherwise go unnoticed. Many of the patterns translated out of this collected data can be truly eye-opening.
Shell, the British-Dutch oil and gas company concluded that, “engaged employees have fewer accidents.” The company found (among its oil refinery employees) that a one percentage-point increase in team employee engagement is associated with a 4 percent decrease in the number of safety incidents per employee. This connection would not have been revealed without the power of big data insights.
Maintaining Product Quality
Maintenance of product quality is of utmost priority to manufacturers. They are constantly on the lookout for areas of improvement. While most of them may be equipped with the data they need to improve quality standards, they may lack the ability to interpret the data into actionable insights. The use of predictive analytics software in testing can save huge sums of money. These insights can drastically reduce the number of unnecessary tests required. It can also be used to determine the number and type of tests needed instead of running all the tests.
Apply Big Data To Your IndustryManufacturing data analysis
Big data offers a slew of benefits across industries. One of the major benefits most manufacturers attribute to Big Data is the ability to detect product defects. However, the benefits can be quite diverse for different industries depending whether the process is a discrete manufacturing or process manufacturing. It depends on the industries they cater to. It could also depend on the throughput or the value of medium and material. What is certainly clear is that big data has a significant impact on manufacturing across industries in different ways. For instance, in oil and gas industries, the application of big data would be focused around uptime and continuity of operations, whereas in the field of precious metals the key factor would be throughput.
The Perfect Big Data Platform Industrial data solutions
Organizations today are proactively looking for data management strategies rather than being reactive to data problems. Most companies are gunning for ways to make effective use of the data that they already possess. Simply having large volumes of data won’t amount to anything, much like having a car without tires.
Selecting a big data platform is the first step toward turning data into actionable insights. There are many factors that influence including:
Robustness: The platform must be robust and should be able to cope with certain inconsistencies without compromising stability.
Consistency: Proven ability to deliver business results while minimizing the margins of errors in the translation of large amounts of data, consistently.
Reliability: Ability to handle information in a secure manner supported by robust analytics and machine learning modules.
Bespoke Or Agnostic: Standardized platforms can be designed to fit a budget while highly customized platforms can be tuned to deliver business specific outcomes. The relevance of either of these will depend on the nature and priorities of the business.
Ease Of Use And Availability Of Manpower: Skilled manpower will be crucial to harness the power of data. The use of data and AI is to support human effort, and efficiency is born from this interpretation.
Big Data Manufacturing ApplicationsIndustrial data solutions
Big data manufacturing applications are manifold. They are extensively diverse with respect to the industry they cater to. Some of the widely used applications in manufacturing industries are mentioned below:
Predictive Production Analysis: Predictive Production Analytics enables manufacturers to proactively implement mitigating solutions to avert efficiency loss in manufacturing operations. Besides predicting equipment performance and estimating the time to failure, they also help curtail the impact of these uncertainties.
Predictive Performance: Improve productivity, optimize throughput and OEE metrics.
Predictive Quality: Predict and prevent quality failures to optimize first-pass yield and reduce waste.
Predictive Maintenance: Optimize machine availability and prevent unplanned downtime.
Benefits of big data are seen in operational visibility.
Asset Navigation: Quickly assess how every line in your factory is doing – based on live state, work order, and production information, as well as richer high-level data over a recent time period.
KPI Optimization: Discover and understand trends, identify constraints and areas of opportunity across the factory.
Live Process And Factory View: View real-time data across the whole factory at once or detailed metrics for each line.
Big data benefits show up in predictive alerting.
Action Alerts: Email-based notifications for when conditions go outside of target ranges for longer than specified allowance.
Investigative Reporting: Email-based report targeted at Process Engineers and Quality. Lists the previous day’s completed runs by Cpk, with additional metrics as configured. Raises quality issues for further investigation for each line.
If you’re interested in taking a peek behind the curtains to witness where the magic happens, visit us today!
Get Oden’s Help
Oden offers the best in class combination of data analytics and automation. It has helped numerous manufacturers derive value through optimization of their production processes and by eliminating waste. With the primary focus on developing artificial intelligence solutions to help manufacturers, Oden strives to empower factories to achieve perfect production with the power of AI.