Advanced Work Order Analytics: The Smartest Way to Increase Shop-Floor Production

If you put on your high-vis vest, hard hat, and boots and go for a manufacturing plant tour, you can get an idea of heightened chaos on the shop floor. The role of work order management here is important in offering structure and clarity be it handling intricate production lines or complying with industry standards.

The increasing digitization of work order management to adopt Industry 4.0 technologies allows manufacturers to understand operational influences better and optimize their production efforts accordingly. Advanced analytics, the use of AI-ML, predictive analytics, and big data technologies, can help with analyzing and interpreting vast amounts of data generated on the shop floor.

In this article, we look at how advanced work order analytics help shop floor production to adapt to the market's ebbs and flows.

Example 1: Non-Data-Driven Work Order Management in Manufacturing

Consider a facility that specializes in assembling consumer electronics. It operates on batch production principles, where work orders are generated based on client orders, demand forecasts, and inventory levels. It uses a non-data-driven approach that looks like:

Manual work order generation
The production manager manually generates work orders for a single production line, depending on client orders or inventory needs. The information on the product type, quantity, and deadline are maintained on spreadsheets. Now imagine the setup time for batch production scheduling between production runs… This won’t just lead to suboptimal scheduling decisions but has a high risk of errors in estimating setup times and prioritizing orders.

Physical job cards
Every team receives a physical job card highlighting the work order specifics. Team leaders distribute tasks among members as per their skills and availability. They track progress on the same card while manually updating it with the completion of different stages of the assembly. Now, this can be frustrating for teams since updates in job cards might not be propagated to relevant parties on time. The same goes for monitoring the progress that affects project management.

Manual reporting
Team leaders use manual logs to compile reports on production outcomes, any delays, and issues. It’s a time-consuming process to record and may contain human errors like missing numbers or transposed data. These delays or errors in manual reporting can impact the decision-making process, while also making it harder to derive meaningful insights. Manual processes can also create silos of information, may have duplicate entries, and difficulty in auditing.

This is just the tip of the iceberg. As product demand increases, the facility may face scalability issues, inefficiencies in production planning, increased costs due to errors, and variability in product quality.

In Fig 1 you’ll notice a couple of things:

• Work order 1: Material source information is missing. This is very critical for planning procurement and ensuring that the right grade of steel is available for production.
• Work order 2: You’ll see that it lacks specific steel grade and quantity, which are essential details for production planning and quality control. The processing line isn’t assigned as well.
• Work order3: It is missing dimensions and finish type, which are crucial for meeting product specifications and quality standards. There’s no mention of the deadline as well.
• Work order 4: The finish type, which affects the final product's appearance and corrosion resistance, is not mentioned. There’s also no status.

The absence of some details in the work order isn’t something that you can overlook. This will lead to inconsistencies throughout the steel production process. Therefore, there’s a need for a more data-driven approach to work order management. We’ll look at this in the subsequent section of this article.

Example 2: Data-Driven Work Order Management in Manufacturing

A data-driven work order management approach involves adopting smart manufacturing technologies. Let’s use the same example to understand the difference:

Automated Work Order Generation
The facility employs a smart machine monitoring solution to track production metrics, performance, and asset conditions in real time. The use of predictive analytics algorithms takes the retrieved data (usually this model is trained over a period of 3-6 months) to predict production bottlenecks and efficiency issues beforehand. Upon the detection of any potential issue, the concerned system will generate a work order, define the problem, and affected machine, and suggest changes to optimize the production issue.

Dynamic Work Order Assignment
Using databases of employees, team leaders can have a repository of their skills and availability. When a work order is generated, team leaders can oversee it and get it assigned to suitable employees. They can receive work orders directly on mobile devices and update the work order status in real time.

Real-time Monitoring and Reporting
Managers and supervisors can get updated on work order status, maintenance requirements, and equipment health using dashboards and advanced analytics. They also provide insights into trends like equipment malfunctions to make informed decisions on upgrades or maintenance practices.

The data collected from completed work orders can be fed into continuous improvement algorithms to enhance project management and employee training programs.

The data-driven work order management in the same manufacturing facility will look something like this:

Automated work order generation: Parameters such as material levels and other production efficiency metrics are monitored in real time. Look at work order 3. It instructs a “predictive setup check” based on certain parameters that it has been fed.

Real-time monitoring and reporting: The system provides real-time updates on work order statuses, material sources, and predicted issues. This is what a data-driven work order management looks like. It’s obvious how it can enhance a floor manager’s decision-making just by looking at it.

Predictive issue detection: The 'Predicted Issues' column is a direct result of integrating predictive analytics. The tool identifies potential problems such as material shortages, equipment maintenance needs, or process inefficiencies before they impact production. To understand this, take a look at work orders 2, 3, and 5.

Conclusion

The transition from non-data-driven to advanced work order analytics can help you turn all the production data into actionable insights. You can now appreciate the need for a machine monitoring tool that can ingest data from your machines/devices and then:

1. monitor different parameters to understand like lead time, idle time, and changeover time to assess production over a period
2. track the performance, availability, and quality of each machine on the production floor
3. track important metrics like MTTF, throughput, cycle time, etc. which gives you a quick overview of how your machines are performing over a period

At Rawcubes, this is what we excel at. Though you may find many plug-and-play solutions on the market, you’ll quickly realize that they’re not always customizable to your needs. In fact, some of them require you to completely overhaul the technology and processes on your floor to bring data to a specific format—the onus is entirely on you.

We designed iDataOps to counter problems like these. iDataOps is a custom solution that works by treating each machine on the manufacturing floor as a specific system and then onboarding it onto the platform. Our philosophy lies in treating each customer uniquely and addressing all their needs swiftly. Our deployment times, when you work with us, are in days and not weeks and months, unlike other plug-and-plug solutions.

Want to understand how we do this?

Contact Rawcubes today to discover how iDataOps can give your organization the advantage it requires and transform it into an Industry 4.0. smart factory.