How would you like to go hands-free, maintain visual focus, and save time? These are just some of the benefits of using voice command to control machinery. Increasingly sophisticated natural language processing, based on artificial intelligence, also means that it is becoming possible to issue complex commands by simply telling a machine what you want it to do. This removes the need to memorize specific command words or learn complex menu systems and sequences of instructions.

During the past few years, voice-assisted technology has become very popular in the consumer market. Products such as Apple’s Siri, Amazon’s Alexa, the Google Assistant, and Microsoft’s Cortana have become household names—quite literally, as people use them to turn on the lights and control the heating. This technology is also appearing in the industrial market, where things can be considerably more challenging.

‘Why are our changeovers taking so long?”

If you’ve asked this question on the shop floor, more than likely you were met with blank stares by your employees. Open-ended questions like this are overwhelming, so employees try to find quick answers that don’t really address the problem. They don’t have a starting point to form an answer.

But what if you asked a question with a specific, achievable goal, such as:

“What steps can we take to reduce changeover time by 15 minutes?”

You’ve then provided your employees with a measurable goal in the form of a question. Your workers may feel empowered to answer with some hands-on suggestions for incremental changes, such as reducing setup steps or combining workstations. This in turn could not only reduce changeover time, but also significantly eliminate wait times and inventories.

This approach is often described as kaizen, or “continuous improvement,” which serves as the backbone for lean manufacturing. Kaizen uses the plan, do, check, act (PDCA) problem-solving cycle to encourage manufacturers to use small ideas to solve big problems, such as costly, time-intensive changeovers.

When I first learned quality improvement back in 1989 at Florida Power and Light, the consultants who trained us taught a very specific way to draw a Pareto chart. They’d been trained in Japan, the place where quality improvement first took root during the 1950s, so I took it for granted that the way they drew Pareto charts was the authentic and best way to do so.

A Pareto chart combines a bar graph with a cumulative line graph. Using the way we were taught to draw a Pareto chart (figure 1), the bars are touching, making it extremely easy to visually compare levels from one bar to the next. The bars span the entire available space along the x axis. The cumulative line graph springs from the bottom left corner of the first big bar, and each subsequent point is plotted from the corresponding top right corner of its bar.

Do you know what a retailer and a tightrope walker have in common? They both have to balance. For the tightrope walker, the logic is clear. But what’s the balance that a retailer is looking for?

A typical dilemma of shortages vs. storage costs

Although the dilemma of shortages vs. storage costs is applicable to any product category, it’s much more painful with perishables. If their quantity can’t meet the demand, retailers should be ready to see a frown from an unhappy customer who didn’t find her favorite dairy, fruit, or vegetable on the shelves.

However, staying on the safe side by ordering more perishables is hardly a cost-effective solution. Perishable products require special storage conditions, and their shelf life seldom exceeds a couple of days, which means retailers must address disposal issues. So, it’s easy to understand why retailers, by all means possible, try to find the optimal balance between storing too much and too little.

A way of handling this dilemma with data science

There is a way to handle the storage/shortage dilemma efficiently: It’s via a deep neural network (a DNN), the most advanced data science approach.

According to a report by PwC, industrial sectors worldwide plan to invest $900 billion in Industry 4.0 each year. Despite these growing technology investments, only a few technologies are significantly mature to drive measurable quality impacts. Digital visual management (DVM) is one of them, being the fundamental link that bridges the lean culture and quality management in the digital age. 

What is digital visual management?

The vast majority of all the information and communication is visual. The human brain processes visual information significantly faster than text. When a relevant image is paired with audio material, two-thirds of people retain the information three days later.

Organizations dedicated to continuous improvement take advantage of this reality and use DVM to engage staff, provide insight into key information, and to ensure improvement projects are moving forward as scheduled.

DVM collaboration consists of a well-structured and interconnected series of stand-up meetings that take place regularly before a board, where the team posts (on paper or digitally) all the information it needs to steer and improve its activity. This is applicable to any team: from permanent shop-floor teams to top management, including (geographically spread) project teams.

Lean manufacturing is a philosophy focused on maximizing productivity and eliminating waste while creating a quality product. One of the most powerful strategies in the lean toolbox is total productive maintenance (TPM), a system targeting continuous improvement through a holistic approach to maintenance. Avoid delays in the manufacturing process caused by breakdowns and unplanned maintenance with TPM.

What is total productive maintenance?

As long as equipment and tools have been used, they’ve needed to be maintained. In manufacturing, maintenance traditionally was approached reactively, and as Steven Borris writes in Total Productive Maintenance (McGraw Hill Education, 2006), traditionally the surplus of cheap labor meant no one was particularly worried about efficiency or avoiding breakdowns. Following World War II, however, industries and economies were reassessing business and making drastic changes.

In the world of risk management, maintenance of mission-critical equipment drives priorities and budgets. It is the ultimate test of proactive maintenance and smart decision making. Managing assets that “cannot be allowed to fail” is more than an emotionally charged mandate that forces managers into a continual state of alert. It is the harsh reality for technicians tasked with ensuring continuous performance or service. The stakes are high. Fortunately, technology can help mitigate the risks.

The scope and scale of critical assets and equipment vary greatly, from electrical grids and security systems to back-up generators at hospitals, refrigeration in the food and beverage industry, and traffic control for the airline industry. National defense systems and communications systems in the public sector, such as 911 call centers or alerts for fire departments, are other examples of high-tech equipment that cannot be allowed to fail. Whether it involves protecting health of consumers, safety of the workforce, or national security, mission-critical assets require special attention to detail and proactive monitoring of status. Prevention is the goal. Early detection of warning signs makes intervention possible.

Operations management plays an important role in the manufacturing process, but similar to a stage crew at a theater, operations managers do all their best work behind the scenes. The best operations managers strive to go unnoticed, and why shouldn’t they? A seamless supply-chain process should require little to no attention from customers.

But recent tariffs are jolting operations. NAFTA changes, along with tariffs on Chinese imports, are forcing operations managers to step out on center stage. New tariffs on materials like steel and aluminum as well as electronic components could mean disruption in the supply chain process, and operations managers have to work diligently to mitigate any hiccups that crop up for the company and its customers alike. Certainly costs are going to increase somewhere, so companies have to decide whether they’re going to absorb them or pass them along to their customers, both of which are less than ideal options.

About 10 years ago, software testing was perceived as the only possible quality assurance (QA) measure for software, according to the World Quality Report 2018–2019. However, QA has since outstepped these boundaries. The QA process now implies that all stakeholders have a direct interest in software quality during the entire project life cycle. But how should you establish such a comprehensive QA process?

Let’s consider what obstacles you may face while setting it up and explore a real-life example of how software QA consulting can solve QA problems.

Why QA consulting?

An established QA process should build in defects detection right from the project planning stage. QA teams should dwell on bugs prevention rather than just finding them, and regularly measure testing effectiveness using clear quality metrics. Project managers, business analysts, and developers should do their share in establishing a solid QA process.

Most of us have heard of kaizen—continuous improvement of philosophy and methodology. In business, this involves all employees working to improve a company's processes to lean it out, to run with less waste. But most of us who are familiar with kaizen think of it as something you do.

Especially, we think of kaizen as something you apply to an existing operation or process, or in terms of mounting a “kaizen blitz.” We tend to think of it as is trying to fix something that’s already broken. But what if you applied kaizen principles before your organization was actually up and running?

“The ideal time to think about using kaizen, or continuous improvement, is really phase one, or the feasibility study of construction or building out an existing manufacturing facility,” explains Dan Chartier, managing director of Kaizen Institute North America. “It’s important to get involved as early as possible in the project. This helps in assessing the efficiencies of the plan before it gets designed and constructed.”