The Cost of Poor Quality; and one (smart) way to fix it
According to some experts, the Cost of Poor Quality (COPQ) can range from 5% to 30% of gross sales for manufacturing or service businesses. This is not an exaggeration when you consider poor quality can translate to repairs, re-starting, re-testing, downtimes, re-design, warranties, service calls, complaints, lost business, and even bankruptcy. In Timothy J. Clark’s “Success through Quality“, the aggregated cost of “scrap” ( amounts to roughly 20% annual sales in manufacturing and service businesses. A 20% COPQ implies that within every five-day workweek, one day is spent on scrap. For employees, this translates to 8 hours of a standard 40 hour work week. Now imagine if this 20% became 5%, or even less. Imagine if employees had 5% scrap as opposed to 20%… this would be 2 hours vs 8 and ultimately 6 more hours a week an employee could spend promoting business as opposed to fixing business.
Today, we’ll see how factories can adopt smart manufacturing by leveraging Industrial Internet of Things to improve quality in production. This blog post is meant to help business to better understand how they can economize waste and save millions in scrap each year.
We should start by acknowledging that the measurement of quality answers the question: how many products met the specification (regardless of availability and performance)? Then, we highlight the most common causes of quality losses:
|Scrap||The discarded or rejected material in the operation|
|Rework||Sub assemblies or materials that during the manufacturing process need to be reworked or fixed to meet the requires specification|
|Defective Raw Materials||Non conforming materials received from suppliers|
Even knowing COPW main roots, it’s practically impossible to reach 0% scrap. But there are new processes and technological advantages which can significantly increase product quality and Overall Equipment Efficiency (OEE). Now, we will explore some possible data-driven solutions.
Identify Waste through Value-Stream Mapping (VSM)
Value stream mapping(VSM) is a lean tool that allows users to visualize how materials and information flow through the organization’s product chain accounting for customer requirements and product delivery. We’ll see briefly how it’s done. Here is an additional resource to better help you understand how VSM works.
You might also be interested in this step-by-step guide to create an VSM.
Step 1: Create a Product Family Matrix
To begin you can start by choosing the product or set of products you want to analyze using a Product Family Matrix like this:
This matrix helps you decide which products need efficiency and which products have duplicate processes. If some products run the same processes (such as A and C), you can use one or both for your VSM.
Step 2: Map the Production Line
Next, map the whole process of a product line, including customer requirements and ending at delivery. Here is an basic example of VSM:
VSMs are a collaborative process where a representative of each area should take action. After determining the processes, information and materials flow with a map, it becomes easier to identify bottlenecks in production. This is critical and as the Theory of Constraints posits, a supply chain is as fast as its slowest link. However, identifying this link is not always easy because often businesses look at the consequence (scrap), and not its root (machine malfunction). The value of VSM comes when it gives a holistic approach through the visualization of indicators that tell when a process can be optimized.
The path and intention of smart factories is to change the standard paradigm of manual monitoring indicators to both manually and automatically in real time. This double check approach allows indicators to generate data which is then gathered among different sources, consolidated, computed, and visualized into a platform, showing outputs such as uptime, quality, units produced, etc. This information can then be tested and verified to estimate overall product quality.
Running inspections in each stage of production
There are 4 known kinds of inspections; the pre-production inspection which consists in checking raw materials before production; during production inspections, which allows to have a good idea of average product quality by inspecting the first finished products (10%-30%) as soon as they get off the line; the pre-shipment inspection is the standard quality control solution where inspectors check a sample of finished products when they’re packed and ready to be shipped. To avoid delays, this inspection is started when the 100% of products are finished and 80% of them are packed; and the loading inspection when buyers want to check factories actually ships the right products.
Usually, companies just run the third kind of inspection, which can lead to reduce reliability in overall quality. Some of the reasons are:
- Inspections are performed in a hurry
- Delivery time is considerably extended
- Inspecting and documenting results simultaneously can lead to data entry mistakes
- inspections can be made just in a sample of total production
- Costs increase since inspections are usually done by an external auditor or internal trained employee.
Here is how IIoT solves these issues and makes each kind of quality inspection smarter.
Raw Materials Inspection
Industrial processes can be harmed from the beginning with low quality parts or materials from the start. As contingency, sensors can be placed on the supplier side as well, providing real-time quality status reports of the products to be shipped, then you can compare such data with inbound inspection data for reinforcing quality monitoring.
Also, cloud platforms are even capable to extract data coming from suppliers ERPs to be then be uploaded in your own systems. This makes easier for both suppliers and factories to manage warranty claims, since they could have access to the data during the monitoring. Consolidating data from 3 data streams at time: real-time monitoring using IIoT, ERPs and inbound inspections, reinforces the reliability of using raw materials in optimal conditions, which would prevent, at the end, defective units produced.
analyzing non-conforming products with no manual inspection
The common way to inspect and test products is by doing it manually; operators select a sample of finished products and after determining the Acceptance Quality Limit and inspecting each product individually, decide the viability to send the batch to shipment.
With IIoT, operators can use quality inspection devices to monitor sub assemblies or finished products and measure indicators such as color, weight, voltage or current. As this process takes less time since data readings of multiple products are automatically sent and processed in the cloud, sample sizes could be greater. The larger the sample size, the more information we have about the overall status of the batch.
Container loading inspection: monitoring in a hurry
Final inspections are usually performed in a hurry before shipment. These inspections are usually meant to determine if the right products and amounts are being shipped. Using counters, presence sensors or RFID tags could automate those inspections since sensors’data readings are storaged in real-time in the cloud where platforms could generate automatic reports which can be seen through mobile or web applications.
Remote product monitoring, even outside the shop-floor
Companies can track product quality even after shipping, which might become essential to ensure customers satisfaction. Cloud-based monitoring allows proper traceability of quality in outgoing batches and helps the identification of potential failure before customers complaints. In practice, handling and preservation could be properly monitored, as in cold-chain of perishables such as meat and dairy products, where temperature and humidity alerts would prevent product losses during storage and transportation.
Machine health monitoring
Monitoring non-conforming sub assemblies and finished products could lead to know precisely which machines or processes are failing, but not yet exactly when. Commonly, production managers notice if machines are failing after an extended period of time, whereas daily or weekly. For instance, a machine is expected to produce 50 units a day, and produces as much as 43. The problem is that such malfunction is noticed once production finishes. Machine helath monitoring is based on embeding industrial sensors to monitor different machines components such as level of lubrication, energy consumption, levels of dust or amount of units produced and other indicators that can tell if a machine suddenly stops or progressively descrease its performance. When one of those indicators present anomalies, the direct responsibles for each production line could be notified via alerts and take action, even before performance seems affected.
SPC (Statistical Process Control)
Embedded sensors bring an improved way of monitoring, such as SPC. Both qualitative and quantitative data such as dimension, temperature, pressure or color can be sent to cloud platforms to monitor if variables are behaving between spec limits. In addition to real-time alerts based on internet services such as emails, these cloud platforms allow to have historical records of data behavior in order to generate long-time reports, analyze trends, performance indicators and standardization control.
You’ve just seen how your company can significantly increase quality by using IIoT. If you want to know more about how the Industrial Internet of Things can increase overall efficiency, you can start by watching and IIoT application in action to have a better idea of how Ubidots platform works.