In today’s increasingly connected and intelligent production environment, manufacturers are inundated with data. It can be challenging to see quality data throughout an entire enterprise, which makes it more difficult to lower costs, advance product quality, decrease waste, maintain compliance and boost profitability.
Enterprise software deployments are often too time-consuming and costly for customers to bother with, making translating data into critical business decisions challenging. Standardizing and maintaining various enterprise systems is equally challenging.
This is where a statistical process control (SPC)-based quality intelligence platform can help. It uses statistical techniques to control procedures and production techniques and its tools and processes can help businesses to observe process behavior, uncover internal systems issues, and solve production issues.
The right cloud-native quality intelligence platform can lend high-level visibility and offer strategic insights, and is tailored for ready, individual access. Leaders can companies can use the data they are already collecting for quality compliance purposes to transform their businesses by decreasing costs, increasing profitability, and making decisive operations decisions.
With that in mind, here are six factors to keep in mind when choosing an SPC-based quality intelligence platform.
With enterprise visibility, businesses can see data from various work areas, such as quality labs, production lines, suppliers, and multiple sites. Businesses can access the data from a site, regional, division, even corporate level. They can then use that data to gain insights for adjusting their operations. A real-time data platform helps companies to aggregate and analyze quality data from all production processes in one standardized format.
It’s important to select an enterprise design that has a centralized engine and unified data repository. All data should go through the same centralized engines including lab measurements, anything that is typed indirectly, or any automated data that addresses variables such as calculation, timed data collection, or sending violation notifications.
A centralized engine ensures that every site connects to the same, singular database in a unified way, allowing an organization to standardize across many sites and achieve consistency. InfinityQS’ Enact unified data repository, for example, by brings together and standardizes an entire organization’s quality and process data in a single repository for unlimited visibility and analysis options, helping companies to ensure that critical business decisions are made off of the exact same dataset.
2. Best practices
Once companies gain insight into their key data, they can then make quicker operational decisions. Ultimately, they can achieve savings across their organization that allows them to improve their business and make customers happy. The InfinityQS Excellence Loop, for example, represents an ongoing cycle of using enterprise visibility to gain insight and transform outcomes. In this way, the company has saved tens of millions of dollars for its customers through drastic reductions in scrap and improvements in throughput.
3. Personalized intelligence
Complicated control charts and advanced statistics sound impressive, but they may only lead to confusion. Day-to-day field operators often do not know which control chart to look at or prioritize, so it’s important to use a platform that presents fairly complex data in a straightforward way. This empowers users to act quickly instead of feeling overburdened by complex analytics. A data platform should present data in a highly accessible visual format based on specific users’ needs. They should not have to scroll through numerous charts and menus, instead they should easily see relevant, actionable intelligence.
4. Intuitive role-based dashboard
Visual representation of a plan and visual indicators of performance are powerful elements of success. Dashboard-based software, such as Enact, enables all users (whether a shop-floor user or an administrator) to access self-populating dashboards that require no configuration. The dashboard populates with display details relevant to individual users’ roles, which includes security permissions and device relevance. For example: data collection required at a specific workstation on a factory floor is merged at that particular location, so when designated users sign into it, the relevant information appears.
5. Actionable intelligence
An ideal real-time data platform does not rely on the traditional quality metrics, but instead offers meaningful, actionable site metric for each critical element. Grading, which gives companies actionable ways to monitor existing operations and to prioritize where to shift resources and investments when needed.
Here’s how it works: Each data stream is issued a score, that can then be attributed to each site, which reflects how that site is performing based on a critical production element. Specifically, a letter-number combination illustrates both how well the site could perform and how it is actually doing.
Ideally, grading should happen automatically in the system. There should not be any extra work to do, no configuration or specific analysis or analytics that a user needs to perform for grading.
Your data platform should incorporate responsive design, adapt to the resolution of devices of all types, and should be designed for the cloud, requiring only a browser to access it.
This universal access enables users to not only remotely gather relevant information impacting their day-to-day operations, but to also keep track of performance, respond quickly, and easily share data on key performance indicators with suppliers, partners, and customers.
Ultimately, the right (SPC)-based Quality Intelligence platform will advance product quality and change the way you view your quality data. Enact's Unified Data Repository, for example, brings together and standardizes all your quality and process data, helping cut costs and supporting superior, faster operations decisions.