Global competition, rapid innovation in process and logistics, market volatility, and shifting regulations require manufacturers to anticipate tomorrow’s challenges, circumstances, and demands well in advance.
The good news? Predictive analytics provides your manufacturing operations with the ability to extract valuable insight from the complex and diverse data you’re already gathering, seeing well beyond the horizon into future opportunities.
The challenge? You might not know where to start. For those unfamiliar with predictive analytics, there’s hope. A smorgasbord of use cases are already in practice from Industry 4.0 manufacturers, finally maximizing the data from your SCADA systems, automation tools, and other sources. They’ve identified straightforward paths to greater performance, leaner operations, and higher profit margins. Here’s how the right data and analytics partner can help you bridge the gap – and a few examples of how using predictive analytics in manufacturing is an ideal application for your business.
How Do You Start Leveraging Predictive Analytics
Meaningful ROI depends on creating the right foundation. For any manufacturing predictive analytics solution to be successful, you’ll need the following foundational elements:
A Single Source of the Truth
The data in your organization is often complex and more than a little chaotic. JD Edwards data alone is often inscrutable to those unfamiliar with F1111 table names, Julian-style dates, and complex column mapping. That’s just one source system. The different data formats pulled from ERPs, MES platforms, QMS software, and other source systems only complicates matters.If you want to extract real value from your comprehensive data, we can help you create a single source of truth. Even if your early use cases lean toward a specific department (operations, quality assurance, supply chain management, etc.), manufacturing is so holistic that it always helps to have the option to tap into your comprehensive data.
Accurate and Consistent Data
The accuracy and consistency of data impact the ability of any organization to make effective predictions. In the manufacturing industry, the range of different data types from a variety of sources makes data quality management a priority and that there are clear relationships across your master data. Otherwise, you’ll be unable to identify discrepancies or duplicates in your data that can capsize your predictions about everything from future demand to workforce needs. We can help you to develop consistent quality across your data ecosystem to ensure your insights are accurate.
A Defined Data Strategy
For predictive analytics or even reporting to offer the greatest value, your organization needs a firm data strategy designed around your highest priorities. Even if your high-level business goals are solidified in your mind, you still need to determine what choices or actions will realize those goals. We can help to bridge the gap between technology and your business goals, achieving them with the shortest route.
With the magnitude of data at your disposal, you’ll likely need a centralized data lake to different business units to access your panoply of data. As we’ve mentioned, that requires consolidating all of the different source systems (ERPs, MES platforms, etc.) into a single source of the truth, a feat you can’t achieve without data ingestion.
By implementing data ingestion, we can help you to extract data from various sources, transform it into the appropriate format, and load it into a consolidated storage system a predictive analytics solution can use to unveil transformative insight.
There’s no one-size-fits-all when it comes to centralizing your data – even in the manufacturing space. All the different processes and business units within your organization require your data lake or other hub to offer customized accessibility and functionality. By conducting an assessment of your organization, we can determine the right specifications for your predictive analytics tool – and any other data science applications your organization might need.
When all of your data is centralized and validated, your internal BAs and data scientists actually need to access the data. Through custom development or an out-of-the-box solution, we can help to create dashboards and portals that enable your team to ask questions that empower them to anticipate demand, manage resources, detect potential risks, and maximize your ROI.
If the last big change you made in your organization was to automate processes, then you’re falling behind the curve. Connecting your plants with tech-forward solutions requires you to embrace the interoperability of your enterprise systems and leverage IoT solutions to your fullest. Rather than jumping on the latest trend, we can help your business identify the quickest wins that can transform your profits, performance, and productivity.
Manufacturing Use Cases
With the right partner, it’s clear you can implement effective predictive analytics solutions. But how can you derive the full value of this analytics solution right from the start? These four use cases offer easy wins for any manufacturing organization:
The machinery used to fabricate new products or maintain operations in your facility endures high-impact, punishing processes. The extreme pressure, temperatures, or range of motion these parts or components undergo make regular replacement a must. An unexpected breakdown can cost as much as $22,000 per minute – depending on the complexity and necessity of the particular machine.
Many manufacturers are seeing the potential threat and implementing a quick win with predictive maintenance. Preventative maintenance routines only gauge conditions in the moment, whereas predictive maintenance uses the aggregate data from real-time sensors on parts, components, or machines to more accurately anticipate:
- When they need replacing
- When they are performing outside of normal parameters
- The probability they will fail within specific high-volume periods
- The likely cause of failure
- Which equipment presents the highest short-term risk
- What type of maintenance activity best solves the given problem or error code
This analytics-powered practice is becoming even more powerful. Through automation and even machine learning capabilities, predictive analytics programs not only receive automated readings but can send out automated maintenance requests. This streamlines the entire process and can reduce maintenance costs by 10% to 40%.
Enhance Manufacturing Execution Systems
The transformation of raw materials into finished goods is more dynamic than most manufacturers acknowledge. Raw materials, machinery components, and supply costs fluctuate due to material availability, shipping location, seasonality, and global demand at the time of purchase. When the materials are in place, specific phases in your manufacturing processes can inhibit the flow of the production line. Plus, open or closed control loops that are improperly tuned, performing poorly with prolonged excursions from their set objective. Your traditional manufacturing execution system (MES) can react to these issues, but a predictive analytics tool can anticipate problems before they happen.
Let’s say you want to reduce material costs. As many as 46.4% of manufacturers struggle with increased raw material costs among their primary challenges. Your MES platform might be able to analyze historical data, but lack the foresight to predict major shifts in raw material costs. This year, there have been plenty. In August, the price of Nickel surged to $2,000 a ton in one day. In June, natural rubber prices gradually increased after hitting a 10 year low in November 2018. Plenty of other raw materials or supplies are subject to the same volatility. This increase in raw material expenses strains margins and forces many manufacturers to revise their pricing structure to stay afloat.
Predictive analytics can counteract this encroaching profit erosion. Your organization can save on raw materials by creating a more efficient operation. For perishable products (e.g. food and pharmaceutical products) you can reduce mistakes that result in unavoidable waste. Beyond material costs, you can enhance the capabilities of your MES by identifying other significant cost drivers, pinpointing bottlenecks in your operations, and fine-tuning your control loops to improve operational efficiency and profitability.
With how expensive it is to mass-produce goods in the United States, it’s essential for manufacturers to know future demand if they’re going to properly manage their costs. A great example has to do with the seasonality of consumer goods. Think ice cream in the summertime or cold weather attire during the winter. Using the past history of demand supplemented with a few high impact indicators can explain a lot of variability and help plan large capital expenditures or temporary shutdowns.
The idea of demand forecasting isn’t new to manufacturers worldwide, but predictive analytics brings the use of advanced statistical algorithms to the table. Predictive models can account for a complex web of factors including consumer buying habits, raw material availability, trade war impacts, weather-related shipping conditions, supplier issues, and unseen disruptions.
And it can even establish unknown connections between different variables and drivers influencing demand, helping to evolve your supply management practices.
Improve KPI Analytics for Workforce Management
Manufacturers face an uphill battle when hiring. Shortages of skilled professionals and a competitive labor market make smart workforce management essential for the survival of any manufacturing business.
The issue is that multiple workforce management barriers exist in the manufacturing field. Employee productivity is subject to fluctuating demands from consumers or equipment failure. Looking at the Bureau of Labor Statistics data, annual total separations in the industry have been on the rise year over year. This puts manufacturing organizations in a position where they need to predict staffing, scheduling, training, and productivity challenges with greater flexibility.
By working with a partner to enhance your analytical capabilities, you can evaluate a wealth of data from a variety of sources to obtain deep insight into your workforce:
- Consumer demand
- Industry hiring trends
- Internal employee engagement
- Seasonal PTO usage
- Safety incidents
- Employee productivity
- Contract negotiations
- KPIs by employee
Using all of this data to create a predictive model can help your organization to create the right workforce balance (be it contingent or full-time) or even anticipate which employees are on the verge of leaving to keep attrition low.
Do you want to improve your plant’s efficiency? We can help identify the right solutions and uses for you. Schedule a whiteboard session to evaluate your options and start determining how to increase your operational performance and profit margins.
Greg Marsh is a Data Engineer Manager at Aptitive. In his role, Greg facilitates the discovery of business insights from data. From “Big” data like IoT streams or classic relational ERP information, Greg helps companies to unlock the power of their data.