SafetyChain Blog

Specifications are the standards or the minimally accepted requirements for important features (or characteristics) of a product. Many manufacturers also set their own specifications. The confusion between specification compliance and quality can lead to financial loss, wasted time, and so on. For example, a product can fall within specifications but still prove unsatisfactory for clients. Additionally, manufacturers that rely solely on meeting specifications can miss out on opportunities to create more cost-effective processes. Thus, applying several statistical principles can immensely help a company identify ways to positively reform a process and product. By moving beyond the gauge parameters of specifications, manufacturers can boost quality with an efficient, optimized, and cost-effective process that performs better and satisfies the customer base. 

Unlike the previous industrial revolutions that involved replacing existing technologies and assets with modern ones, Industry 4.0 is about mastering the hurdles and opportunities presented by disruptive technologies like big data, machine learning, and AI, which continue to blur the lines between the digital and physical worlds.

Poor quality can waste a considerable portion of an organization’s operating budget. Alarmingly, some manufacturers even have the cost of poor quality factored into their budget, meaning these companies are willing to allow it to persist in plain sight and hinder their overall performance.

While there will always be some factors that affect quality to a certain degree, identifying and addressing the factors contributing to poor quality is critical. This ensures competitiveness, supports ongoing improvement efforts, and minimizes waste. Here’s a closer look at the unexpected costs related to poor quality, as well as how to measure and address them with a cost of quality analysis.

While many process manufacturers understand the potential benefits of digital transformation, often, their vision is clouded by past failed projects. Just 30% of digital transformation initiatives are successful and because they require both time and financial investment. 

Fortunately, we can learn from past mistakes. With a little extra planning and preparation, you can pursue a foolproof digital transformation that supports positive change throughout your facility and sets you up for continuous improvement. Avoid the frustration of attempting a digital transformation and failing by adopting a data-first approach. 

Most process improvements start with plenty of momentum, but their changes don’t always stick. In fact, just 54% of major change initiatives stick long-term—a concerning statistic, considering the number of dedicated resources to these types of projects.

Whether you are a beginner to making digital process manufacturing improvements or a seasoned veteran, to enact change that lasts, we must identify why operational improvements don’t stick in the first place. Often, it’s not the tools used but the psychology behind the improvements that come up short. Improvement requires change, and to support that; we must accept that we aren’t perfect and recognize a need for change. Here, we’ll take a closer look at some of the barriers to change as well as ways to dismantle them.

At its core, manufacturing success is all about quality. Consistently adhering to quality standards ultimately delights your customers and takes you far beyond the benefits of brand loyalty. Determining the cost of quality (COQ) is a complex but essential endeavor; there’s the cost of poor quality to consider and the cost of good quality or preventing issues from happening in the first place. In plants producing hundreds of separate items, tracking the many variables that influence quality can feel like a massive undertaking. And of course, it’s not just tracking this information that facilitates change; the goal is to derive meaningful insights from the data to inform future decisions.

OEE is a critical metric for manufacturing plants to track. With proper, consistent measurement, it allows plant managers to track the overall performance of their facilities and determine the root cause behind line interruptions, unplanned downtime, quality rejections, and equipment problems. This root cause analysis saves manufacturing teams significant time and effort. What might take weeks to diagnose can be uncovered through smart and sophisticated trend analysis, all made possible through OEE tracking software. Here’s what you need to know about deploying this powerful solution in your plant.

The advantages of becoming certified in a Global Food Safety Initiative (GFSI) scheme span far and wide for food and beverage companies. Not only can maintaining GFSI compliance boost your company’s performance, but the initiative also benefits consumers and the food system as a whole. Here are just a few of the most noteworthy ways in which your company can benefit from GFSI certification.

While there many are ever-changing factors influencing the future of the food and beverage industry, one thing is certain: no matter how consumer needs and preferences shift, food production analytics will be at the forefront of business success. Companies that have implemented these powerful tools are already seeing positive changes across a number of key performance areas. Here, we take a look at some of the most significant ways analytics can create game-changing results.

Statistical process control (SPC) is a critical quality assurance activity using statistical methods to monitor and control a specific process. SPC for food manufacturing is an immense endeavor, but it is necessary for controlling the cost of quality, reducing waste, and consistently delivering on-spec customer shipments.