How to Utilize Real-time Production Data and Achieve Better Manufacturing Results
Struggling to find the best ways to utilize real-time production data? This article reviews six steps to achieve better manufacturing results.
Modern manufacturers who thrive in the ever-changing market say their biggest asset is meeting customer delivery schedules. In the pursuit of meeting timelines, quality gets overlooked, and the manufacturing shop floor faces losses and hassle.
Real-time insight into production data is the key to remaining competitive in a world where yesterday’s data is pretty much obsolete today. However, with rising data connectivity, shop floors have transformed into smart factories generating tons of data. This data, if leveraged effectively, can help transform people, equipment, and processes, and enhance decision making.
The Need for Data-backed Decision-making
Information Technology Intelligence Consulting (ITIC), in one of its reports, highlights,
“98% of organizations say a single hour of downtime costs over $100,000; 81% of respondents indicated that 60 minutes of downtime costs their business over $300,000. And a record one-third or 33% of enterprises report that one hour of downtime costs their firms $1 million to over $5 million.”
The only way to avoid downtime and consequent losses is to predict downtime, schedule maintenance, or find an alternative and eliminate downtime altogether. A vision like this needs anticipation and a contingency plan.
But all that doesn’t come from random scheduling. It requires insights, studies of machine performance, capabilities, output, and other information obtained through real-time data from the shop floor.
Data and Digitalization
Machine-to-machine (M2M) technology has enabled multiple touchpoints for receiving this data. But the datasets are siloed and hardly of any value individually. Bringing these sets together and creating an appropriate dashboard helps you gain data on delivery schedules, machine health, material availability, planned maintenance, and greater information to the product value chain.
Figure 1. Present data to the right people at the right time with digitized data. Image used courtesy of senivpetro
Secondly, with rising digitalization in manufacturing, factories have utilized data to enhance efficiencies and overall productivity of shop floor resources. These benefits have been possible due to the evolving concept of Industry 4.0 and its growing application on the shop floor to enable smart manufacturing solutions.
Using Production Data for Manufacturing Efficiencies
Quality Control and Compliance
M2M technology generates tons of data from the machine shop in real-time. You get to know your machine performance, output rate, trends, monitor machine key performance indicators (KPIs), detail work orders, etc.
These datasets can be transformed into interactive dashboards to create charts for anticipating product quality you can promise. You can calibrate your machines to desired quality levels, adjust work centers, and tune product lines and production processes.
Data Advances Quality Management
Quality control (QC) charts help shop floor engineers and QC engineers to identify any offset in product quality and restore stability.
A slight deviation from the standard quality product can trigger immediate inspection and requires machine recalibration. You don’t have to wait until the whole lot is produced or a significant difference is available.
Instead of solely numbers listed in spreadsheets, real-time data can help you plot graphs and charts to visualize change over a significant period.
Controlled Expenditure and Project Costs
Production data for the bill of materials (BOM) can be analyzed to know and estimate the expenses. It shows the total costs, as well as the breakup of costs for manufactured parts and bought-out parts. Secondly, shop floor engineers with inventory information, raw material availability, price comparisons, etc., can schedule the timelines accurately.
By combining these datasets, manufacturing firms gain a clear idea of the entire manufacturing value chain. The planning can be done with greater transparency to ensure data coherence eliminates disputes and inter-dependencies. Machine uptime is also scheduled accurately.
Scheduling Predictive Maintenance
Shop floor engineers and plant leaders must know about imminent equipment failure as well as OEE (overall equipment effectiveness) to prevent unplanned downtime. An unexpected machine failure with no backup stops the entire assembly line and results in heavy monetary losses.
Figure 2. Predictive maintenance helps keep the entire facility running smoothly.
When data related to machine performance is obtained over a long period, predictive analytic techniques suggest definitive patterns within. These patterns are identified by considering specific parameters, such as type of operation, uptime, number of jobs completed, and machine health deterioration over a certain period.
Shop engineers can therefore plan timely maintenance for every machine across the shop and open opportunities for machine autonomy.
Data Dashboards and Systems
Enterprise resource planning (ERP) is arguably the most extensive data source for any organization. Likewise, manufacturing execution systems (MES) give you a comprehensive insight into the company’s engineering design and product lifecycles. But these two systems independently do not necessarily provide a complete picture.
Figure 3. Dashboards and ERPs help drive real-time data. Image used courtesy of pressfoto
Integrating ERP and MES can help you plan the entire supply chain: order quantity, delivery dates, time to production, machine availability, etc. Charts prepared by comparing these datasets show performance updates, compare performances of two drills or lathes, and ensure informed decision-making. This is just one example; the possibilities of information data are bound only by your creativity in using them!
The shop floor connects all the teams, from pre-production, production, design, manufacturing, HR, to sales and purchase. Information obtained from the shop floor affects the performance and collaboration of all these teams. If used effectively, all stakeholders can leverage it for smoother options with higher visibility into their tasks.
For instance, shop floor data helps decide when a particular machine operator completes his shift hours, aware of concerns, output, productivity, etc. Companies can implement JIT (just in time) manufacturing in its true sense with real-time data.
Trends show mass customization is on the rise; off-the-shelf buying (standardized buying) is yesterday’s thing. But implementing mass customization can be tricky, costly, and time-consuming.
CPQs (configure, price, quote) for build-to-order products such as SalesForce or Tacton can automate design stages, custom pricing, and 100% accurate sales quotes. A thorough insight into machine availability through M2M connectivity enables accurate quotes. Manufacturers can also predict the pricing by knowing the availability and demand of raw materials.
Furthermore, as CPQs are integrated with other business systems, more opportunities can be generated.
Figure 4. Siemens PLM software allows team collaboration. Image used courtesy of Siemens
Manufacturers can manage the entire engineering design database and leverage it several times when integrated with PLM (product lifecycle management) or CAD (computer-aided drafting).
CPQs can understand customer buying patterns, shopping preferences, and more with ERP integration. This can essentially leverage different customers’ cross-selling and upselling, show discounts, offers, etc. All in all, the complete buying experience can be personalized for each customer, and the process can be automated.
Machine output is a concern for manufacturing firms and shop floors. It’s a no-brainer that higher machine output and lower downtime deliver higher productivity. You can ensure this flows smoothly by analyzing your data and developing logical decisions based on it.
As stated by Matthew Littlefield, President and Research Lead at LNS:
“Manufacturers with real-time visibility of their shop floor data can raise OEE from 81-94% to 86-95%.”
Getting data is one thing, but ensuring that most functions use this data is the key. OEE can be impacted positively only when all functions work in tandem with clear visibility. Dashboards and charts can help, but how and where you use them is crucial.