Before the emergence of smart factories and the Industrial Internet of Things (IIoT), the division between master data and production data was commonplace and well-maintained.
Keeping this data separate prevented the variation often found in production data from disrupting the uniformity needed in master data. But that was when manufacturing was strictly tangible.
Now, companies are able to implement digital manufacturing processes via smart factories, enabling machinery set up and testing, production simulation, and new product development, all at the touch of a button. To make a digital factory operational, master data and production data must dynamically feed into one another.
The Dichotomy Between Master Data and Production Data
Master data is the static information that typically captures the specifications of things like raw materials, packaging, machinery and other critical product data. When it comes to machinery, examples of master data include fields such as the name of the machine, asset information, average output expectations, product dimensions, tooling needed, and BOMs. Machinery often comes with major expenditures and master data is crucial for successful resource planning, plant scheduling, and production estimations.
Due to the high financial risk associated with machinery, master data generally remains highly governed and rarely changed.Production data is the dynamic information that changes to reflect the daily occurrences in the supply chain. Production data includes WIPs, finished good counts, waste calculations, quality assurance checks, and actual resources used. Taken in live and in real time, production data helps operations leaders make sure the plant is on track to meet daily quotas and to minimize cost-incurring downtimes. Production data is crucial to managing budgets and meeting project deadlines.
How Master Data and Production Data are Typically Managed and Utilized
Master data functions like autopilot on a plane, it sets expected flying metrics and the plane adjusts to maintain the autopilot settings. Following this logic, production data is used as the plane, or the vehicle of adjustment to maintain the settings. But what happens when the presets aren’t optimal for how the pilot wants to reach the destination and don’t take into account factors that affect flying in real time?
In reality, autopilot and the plane itself are constantly influencing the other, updating and adjusting to optimize the journey based on the conditions experienced. Production cycles should be conducted the same way. Master data guides the planning phase of the production process, but actual production rarely goes exactly as planned. Having highly governed master data causes factories to fall into the same inefficiencies, with rare updates to increase productivity and install new learnings. By adding a dynamic element to master data, production data history can be used to better understand your factory’s capabilities and allow you to customize KPIs for each production cycle.
But still, there’s a reason for the ‘view-only’ nature of master data. Master data is vital to help reduce the tribal knowledge surrounding machinery. The same machine often performs differently given the use circumstances it experiences, and to avoid aggregating plant-specific knowledge, a set of master data is produced and left unchanged. Fortunately, the industry has evolved to accommodate this dilemma.
Enter the smart factory, an environment where you can simulate the integration of master and production data without putting your operations at risk, in just a few clicks.
Data Integration to Operationalize Your Smart Factory
The Industrial Internet of Things (IIoT) refers to the already digital nature of manufacturing. The IIoT is made possible by the sensors, computing, and device networks that give modern manufacturing an online presence and are the building blocks for creating a smart factory.
Master data powers a plant’s digital thread; it captures the settings and line layouts, as well as the expected production capabilities. Yet, a smart factory cannot be operationalized without production data. Production data allows smart factory users to simulate the production environment, pulling in dynamic datasets to evaluate different output scenarios, how to integrate new products into the manufacturing schedule, and more accurately predicting depreciation of machinery.
When building your smart factory, it’s important to keep a copy of untouched master data, as having a static version to compare against a dynamic version will enable you to better understand your data. Because consumers' preferences are constantly changing, manufacturing must be prepared to pivot strategies at any given time. Integrating master and production data to operationalize smart factories gives operations leaders the tools to quickly understand their manufacturing capabilities and mitigate risk when deciding how to best accommodate their customers.
Without production data, there is no smart factory, but simply a record of static data mapping physical assets and expectations. To use master data to acquire intangible assets such as knowledge and experience, production data must be implemented.
Using Specification Management Software to Unite Production Data with Master Data
Conceptually, integrating production data and master data has many benefits. But operations leaders often find themselves sifting through mounds of unorganized data, trying to decipher which data type is which and lacking ways to protect important data. It doesn’t have to be this way. With the rise of specification management software, operators can now have both datasets at their fingertips.
At Specright, we’re enabling customers to house both production data and master data in one centralized platform. Capturing this data all starts with specification management. Whether you simply need to organize and house all of your data or are ready to deploy your smart factory, Specright can quickly deploy an enterprise-ready foundation for you to build off of.
Uniting master data and production data in Specright can be done in three easy steps.
Start by ensuring all data is digitized and housed on the platform. Then, map data to processes, enabling you to tie production insights to master data information. Lastly, operationalize your factory by running reports and analysis to optimize processes and leverage resources. Manufacturing is no longer the process it used to be, with consumers functioning as the gatekeepers to goods and services.
With Specright, increase the robustness and flexibility of your manufacturing processes, while keeping costs and operational inefficiency down.
To learn more about how to make your data work for you, contact a member of our team or request a demo.