Top 5 Reasons Digital Manufacturing Fails

With the ability to digitize and automate virtually any process, digital manufacturing has become increasingly popular in recent years. However, there can be a number of factors that cause digital manufacturing to fail.

Inaccessible or irrelevant data, operational knowledge gaps, poorly defined processes, lack of standardization and inability to scale are some of the most common reasons digital manufacturing fails. For example, if a plant does not have access to real-time analytics and comprehensive data then it may struggle to keep up with the fast-paced nature of digital manufacturing. 

It is important for any organization contemplating a switch toward digital manufacturing to make sure their team is fully educated on best practices and processes to ensure a successful transition.

Reason #1: Inaccessible or Irrelevant Data

One common problem manufacturers face is having too much data but not being able to harness it in an effective way. This can lead to uninformed decision-making due to information overload. Furthermore, this issue is often caused by problems with collecting and storing data as well as integrating new data sources. Collecting accurate and relevant data is essential for understanding how your factory operates currently so you can make improvements where needed.

Reason #2: Operational Knowledge Gaps

Another big reason digital transformations fail has to do with what’s known as operational knowledge gaps. This means there are inconsistencies between the way things are supposed to happen and the reality of what actually happens on the factory floor. These discrepancies often occur because processes haven’t been properly documented or understood from the outset. As a result, teams end up working in silos without adequate visibility into other areas of production. To avoid these issues, it’s important to establish clear lines of communication and accountability throughout your organization from the start.

Reason #3: Poorly Defined Processes

A third major obstacle occurs when organizations try to implement new technologies without first taking the time to understand their existing processes. You can’t just add new tech onto an existing system expecting it to work miracles if you don’t have a good foundation to build upon. It’s crucial to define all your processes clearly and document them before trying any sort of implementation project — otherwise, you risk developing mismatches between different systems down the road.

Reason #4 Lack Of Standardization

The next challenge arises when factories try to standardize their approach but lack company-wide coordination to do so successfully. Trying to achieve uniformity becomes much harder once the business starts to scale rapidly adding multiple locations and facilities into the equation. To counter this problem, manufacturers need to put forth concerted effort come standard protocols and practices everyone follows regardless of circumstances they face along the way.

Reason #5 Inability to Scale

And speaking of scalability, perhaps the biggest reason digital transformations fail has to do with the fact that these implementations simply are not designed with scalability in mind from the outset. Scalability is key when introducing digital solutions as companies can no longer afford to simply create a solution that works for their current needs—it must grow with the company and accommodate changing trends. Because of this, digital transformation plans must be made with scalability in mind from the start or else businesses might find themselves investing heavily in temporary solutions that waste both valuable time and money.

Now that we’ve covered the most common reasons why digital manufacturing implementations fail, let’s talk about what you can do to avoid them – short-term goals vs long-term goals and having achievable milestones along the journey.

Inaccessible or irrelevant data – When data is difficult to access or find, it can stall production and lead to errors.

When data goes missing or unorganized, digital manufacturing can quickly be thrown into disarray. Without accurate and up-to-date data, production can become stalled and errors can arise. Finding the right information in a timely manner allows for smoother operations and helps to ensure that the production process is efficient. 

Additionally, having access to relevant data helps create solutions that are tailored specifically to the project at hand, thereby making the best use of resources available. It’s no wonder then why accessible and relevant data is key for those in digital manufacturing fields – it not only helps with efficiency but also creates solutions better suited for any given circumstance.

Operational Knowledge Gaps – If there are gaps in knowledge about how a digital manufacturing process works, it can cause problems down the line.

Operational knowledge gaps can be a challenging constraint when it comes to digital manufacturing. When operations teams lack the knowledge required to effectively manage these processes, it can lead to costly mistakes and hindrances (that are difficult to recover from). 

This is why organizations must take proactive approaches to ensure that all operational teams have thorough and up-to-date training on their process workflow, from the factory floor workers to the senior leaders. Cultivating an environment of open communication, ongoing education, and feedback between departments can go a long way in closing potential operational knowledge gaps in digital manufacturing processes.

Poorly Defined Processes – If processes are poorly defined, it can lead to confusion and errors during production.

Poorly defined processes can be a major roadblock to successful digital manufacturing. When processes are not clearly laid out, staff can become confused and make mistakes that slow down or derail the manufacturing process entirely. 

Establishing clear processes and procedures allows everyone involved in production to understand the workflow from start to finish. Encouraging team members to ask questions when encountering something unfamiliar will also help clarify a process and keep production on track. With improved processes, manufacturers can ensure their projects are completed quickly and with fewer errors.

Lack of Standardization – Without standardization, each manufacturer may produce products differently, which can cause compatibility issues.

When it comes to digital manufacturing, a lack of standardization can have a majorly negative impact. Every manufacturer may produce products differently if they do not have standardized processes in place, and this discrepancy can cause compatibility issues when combining parts from various sources. 

This can lead to production delays, customer dissatisfaction, and a lack of coherence between departments. Standardizing processes is paramount for the success of any digital manufacturing strategy – without it, operations may come to a screeching halt due to incompatible components.

Inability to Scale – When a digital manufacturing process is unable to scale, it can limit growth and hinder productivity.

The inability to scale digital manufacturing can be a frustrating roadblock for a business. When production is unable to keep up with the demand of customers or requirements of the organization, it can be disastrous for a company’s growth and overall productivity. 

It’s important to determine whether or not an existing digital manufacturing workflow is scalable, and if necessary make adjustments before expanding current efforts. Investing in strategies such as automation, design optimization, and agile manufacturing are all potential methods that manufacturers can use to make sure their processes are able to scale easily.

What to Do When Digital Manufacturing Fails 

Digital manufacturing provides numerous benefits to manufacturers but it also comes with its own set of risks that must be managed properly in order for it to be successful. By understanding these risks and implementing the proper measures to address them, manufacturers will be better equipped to handle any potential issues that may arise from using digital manufacturing processes.

With the right approach and preparation, manufacturers will be able to enjoy the many benefits that come along with digital manufacturing while minimizing any potential problems they may encounter along the way.

At ProcessMiner, our research and development team has developed a data science solution that has helped an F500 and one of the largest plastic injection molding manufacturers to reduce scrap rates by more than 50 percent. When ProcessMiner began this journey, we knew it was going to be a game-changer in the manufacturing industry. Fast forward to a few years later, we made a significant impact in the pulp and paper and plastics industries. 

Our industry-leading autonomous control solution delivers process improvement recommendations and parameter control changes in real-time to the production line. Our platform ensures high-quality output while driving reductions in scrap, defects and waste commonly encountered in complex manufacturing processes.

Below are a couple of successful examples:

(1) ProcessMiner team developed a data science solution that helped an F500 plastic injection molding manufacturer to reduce scrap rates by more than 50 percent.

(2) Our artificial intelligence platform achieved unprecedented autonomous chemistry control for the tissue mill. Using a closed-loop controller in conjunction with quality parameter predictions, the mill was able to control its strength chemistry autonomously to ensure optimal chemical feed and adhere to target parameters. As a result, there was a 25% reduction in wet strength chemistry, a 33% reduction in wet tensile variation, and a 98% increase in target adherence.

Digital manufacturing offers a wealth of benefits for manufacturers looking to improve their production processes—but only if done correctly! While there are many potential causes of failure when working with digital manufacturing technology, there are also strategies available for avoiding them altogether if implemented properly from the start.

By ensuring that data quality is high, standards are enforced across departments or teams, and processes are established before starting any project—manufacturers can set themselves up for success with their next digital manufacturing endeavor!


Final Thoughts

In conclusion, digital manufacturing challenges are real and require a proactive approach to mitigate potential risks. Without proper data access, gaps in operational knowledge, well-defined processes, standardized procedures and the ability to scale up production, any digital manufacturing process may not only be inefficient but also result in costly errors down the line. 

With the proper preparation and tooling, however, digital manufacturers can anticipate and address these challenges early on in order to ensure a smooth and successful production run. 

To make sure your venture into digital manufacturing is successful, it’s important to understand each of the aforementioned challenges and put systems in place prior to getting started with any production process. This effort will pay off over time when all goes as planned without any unexpected hiccups.

Benefits of AI and Continuous Manufacturing

Achieving success as a manufacturer requires staying ahead of the competition. This means continually innovating, investing in resources, and leveraging new technology—including Artificial Intelligence (AI) and continuous manufacturing. In this blog post, we’ll take a look at why these two strategies are essential for modern manufacturers.

The Benefits of AI in Manufacturing

Artificial Intelligence (AI) has grown to become an increasingly powerful tool for manufacturers. With its ability to analyze vast amounts of data, AI can help manufacturers make more informed decisions about their processes and operations. For example, it can provide insights into optimal production times, energy usage, inventory levels, and more.

Additionally, AI can be used to optimize customer service by providing accurate answers to frequently asked questions or helping customers troubleshoot technical issues. 

In addition to being a useful tool for analyzing data, AI is also being used to automate certain tasks in the manufacturing process. This helps free up staff members from tedious tasks so they can focus on higher-level objectives such as product design or customer relations. By using AI-powered automation solutions like robots or drones, manufacturers can improve efficiency while reducing overhead costs.

Automation & Efficiency

AI technology has enabled the automation of many tasks in the manufacturing process. This reduces the need for manual labor, helping to save time and money while increasing production capacity. AI-based systems can also be programmed to continuously monitor production lines and adjust settings if necessary, ensuring a consistent level of quality control throughout the entire process. Additionally, automated machines offer more accurate results than those achieved through manual labor alone. 

Cost Reduction & Improved Quality 

The combination of AI-driven automation and continuous manufacturing leads to reduced costs in areas such as energy consumption, raw materials, waste disposal, and personnel expenses. Furthermore, this streamlined process is able to detect errors during production much more quickly than traditional methods, leading to higher-quality products that meet consumer expectations more easily. As a result, manufacturers have greater flexibility when it comes to pricing their products since they are able to create them with greater precision at a lower cost.

Data Analysis & Predictive Maintenance    

AI-driven machines are now capable of collecting data from sensors on their own during the production process in order to analyze performance metrics such as temperature fluctuations or pressure levels in real time. They can then use this data to inform predictive maintenance strategies that help prevent breakdowns before they happen. This further reduces downtime costs while increasing overall efficiency by allowing manufacturers to fix any problems before they become too serious or costly. 

Continuous Manufacturing Operations           

Continuous manufacturing is another strategy that has been gaining traction in recent years due to its potential for increasing efficiency and reducing costs. As its name suggests, continuous manufacturing involves running production lines around the clock with minimal downtime in between batches or product runs.

By eliminating costly stop-and-go production cycles and keeping machines running continuously, manufacturers can greatly reduce labor costs as well as save time and money on consumables like raw materials or energy sources like electricity or gas. Plus, thanks to advances in technology like machine learning algorithms that detect anomalies before they occur, continuous manufacturing operations are now more reliable than ever before.

Unlocking the Benefits of AI and Continuous Manufacturing

At ProcessMiner, our research and development team has developed a data science solution that has helped an F500 and one of the largest plastic injection molding manufacturers to reduce scrap rates by more than 50 percent. When ProcessMiner began this journey, we knew it was going to be a game-changer in the manufacturing industry. Fast forward to a few years later, we made a significant impact in the pulp and paper and plastics industries. 

Our industry-leading autonomous control solution delivers process improvement recommendations and parameter control changes in real time to the production line. Our platform ensures high-quality output while driving reductions in scrap, defects and waste commonly encountered in complex manufacturing processes.

AI and continuous manufacturing have revolutionized the way manufacturers work by reducing costs while improving product quality and consistency. Automated systems can monitor production lines for errors at all times while collecting valuable data that helps inform predictive maintenance strategies which prevent downtime before it happens. Ultimately, this combination of technologies helps manufacturers cut costs while increasing efficiency and delivering better products faster than ever before—allowing them to stay competitive in today’s marketplace!

Manufacturers who want to remain competitive must continue innovating by leveraging new technologies such as Artificial Intelligence (AI) and continuous manufacturing solutions. Implementing these strategies enables manufacturers to use their resources better while creating a safer working environment with fewer stoppages or delays due to faulty equipment or human error.

It also enables them to keep up with consumer demand by producing goods quickly and cost-effectively without sacrificing quality control standards or customer satisfaction levels. Ultimately, AI and continuous manufacturing help ensure that your business remains profitable while staying one step ahead of the competition!

Challenges and Benefits of AI-Driven Autonomous Chemistry Optimization for the Pulp & Paper Industry

Today, pulp and paper manufacturers are under immense pressure to improve efficiency and cut costs. One way they address these challenges is by turning to AI-driven autonomous optimization.

AI-driven autonomous optimization can help paper and tissue manufacturers improve efficiency in a number of ways. For example, it can help identify and correct process anomalies in real time, optimize production schedules, and predict equipment failures before they happen. In addition, by automating key processes, paper manufacturers can immediately achieve cost savings.

Benefits of AI-Driven Autonomous Control for Papermakers

That said, there are both benefits and challenges when it comes to AI-driven autonomous chemistry optimization for papermaking. In this blog post, we’ll explore both sides of the coin. At the end of the day, despite challenges, AI-driven autonomous chemistry optimization systems have come a long way and are here to stay for good.

It’s important for papermakers to understand how this technology works so that they can take on the challenge and get ahead of their competitors.

Benefits of AI-Driven Autonomous Optimization for Papermakers

As we mentioned, there are several benefits associated with AI-driven autonomous optimization for papermakers. Here are just a few of the ways that this technology can help your business:

  • Improved Efficiency: One of the main benefits of AI-driven autonomous optimization is improved efficiency. This technology can help identify and correct process anomalies in real time, optimize production schedules, and predict equipment failures before they happen. In addition, by automating key processes, paper manufacturers can immediately achieve cost savings.
  • Higher Quality Products: Thanks to the improved accuracy and consistency made possible by AI-driven autonomous optimization, businesses that implement this technology often see an increase in the quality of their products. This leads to happier customers and repeat business. 
  • Reduced Waste: With AI-driven autonomous optimization managing your production process, you can rest assured that your company produces only the highest quality products. This helps reduce waste and keep costs down.
  • Cost Savings: By utilizing AI-powered autonomous chemistry optimization to accurately monitor and control everything from raw material consistency to pH levels of bleaching baths, it’s easier than ever to adapt effects in the process to meet desired outputs. As a result, papermakers are able to reduce costs with improved automation accuracy and maintain better quality control.

Moreover, AI-driven autonomous chemistry optimization offers additional benefits, such as faster response times relative to traditional chemistry approaches, enhanced insight into operating process dynamics to allow for optimization, and improved energy conservation through proper monitoring of chemical usage. All of this contributes significantly to cost savings for papermakers looking for efficient solutions in their production processes.

Over a six-month period utilizing autonomous optimization, mills are realizing a 25% reduction in wet strength chemistry usage.

Challenges of AI-Driven Autonomous Optimization for Papermakers

While there are many benefits associated with AI-driven autonomous optimization for papermaking, there are challenges that businesses should be aware of before implementing this technology. 

Here are potential challenges you may face: 

  • High Upfront Costs: One of the biggest challenges associated with AI-driven autonomous optimization is the high upfront costs. This technology requires expensive hardware and software as well as skilled personnel to operate it effectively. 
  • Complex Implementation Process: Another challenge related to AI-driven autonomous optimization is that the implementation process can be complex. This technology requires careful planning and execution to be effective. Failure to adequately implement AI-driven autonomous optimization could lead to disruptions in your production process and decreased efficiency overall.

To avoid these challenges, working with a reputable provider like ProcessMiner ensures an easier experience implementing this type of technology. 

ProcessMiner’s AI-enabled platform is designed to deliver process improvement recommendations and optimal control parameters in real-time so you can achieve better efficiency on your pulp and paper production line.

The predictive machine learning systems will provide autonomous proactive guidance through our online system for reducing costs typically seen at manufacturing facilities across the board – from saving money by scrap reduction or improved quality control outcomes as well!

Over a six-month period utilizing autonomous control, mills are realizing a 25% reduction in wet strength chemistry usage.

Smart Manufacturing and Autonomous Optimization for Pulp and Paper Manufacturing

Critical to optimizing the performance of plant operations and reducing quality variations in pulp and paper manufacturing is proactive and accurate process control. Smart technology is being adopted by the pulp, paper, and packaging industries; this digital transformation in manufacturing reduces the consumption of raw materials and process variability, drives down costs, increases throughput, and gains a competitive edge with customers.

How to Attract a Younger Workforce in the Manufacturing Industry: The Challenge of Perceptions

The manufacturing industry is facing a skilled labor shortage. As the industry looks to the future, one of its biggest challenges will be attracting a younger workforce. The problem is that the current perception of manufacturing jobs is inaccurate. Many people – especially millennials and members of Gen Z – still think of manufacturing as dirty, dangerous work with low pay and little room for advancement. However, this couldn’t be further from the truth.

In reality, manufacturing jobs have come a long way from the days of manual assembly lines. Today’s facilities are clean and safe, and workers are paid a good wage for doing skilled work. On top of that,  manufacturing facilities are equipped with high-tech equipment, including computers, robotics, and artificial intelligence.

However, despite these advances, the manufacturing industry still struggles to attract young workers. In order to change outdated perceptions, manufacturers need to do a better job of marketing themselves as modern and dynamic workplaces where employees can have fulfilling careers.

To attract a younger workforce, manufacturers need to focus on five key areas:

  • Artificial intelligence and technology: Emphasize the high-tech aspects of the job. Many people don’t realize how high-tech manufacturing has become. Facilities are now equipped with computers, robotics, and artificial intelligence. This high-tech equipment is operated by skilled workers who need to have training in computer science and engineering. By emphasizing the high-tech aspects of the job, manufacturers can attract workers who are interested in these technologies.
To attract a younger workforce, manufacturers need to focus on four key areas
  • Offer competitive salaries and benefits: Salaries in the manufacturing industry have lagged behind other industries for years. But as the shortage of skilled workers continues to grow, manufacturers are starting to offer competitive salaries and benefits packages. This is especially true for positions that require advanced skills or training. By offering competitive compensation, manufacturers can attract top talent to their facilities.
  • Provide opportunities for career advancement: Many people see manufacturing jobs as dead-end positions with no opportunity for advancement. But this just isn’t true. There are many opportunities for career advancement in the manufacturing industry. The path to career advancement and management may vary depending on the company, but it typically includes several steps. For example, a worker may start out as an operator and then be promoted to a supervisor position. From there, the worker may be promoted to a manager position. director role, vice president, etc. So, by providing these opportunities for career advancement, manufacturing companies can attract and retain workers.
  • Company culture: When it comes to company culture, how can the manufacturing industry attract young people? It starts by understanding what they want. A study compiled data from more than 1,000 respondents between the ages of 18 and 34 to find out what motivates them when searching for a job. The results showed that 80 percent of respondents said “a company’s culture and values” were very important to them when considering a job. Other important factors included “opportunities for career growth” (78 percent), “competitive pay” (74 percent), and “work/life balance” (70 percent). Clearly, culture and values are key when it comes to attracting young talent.
  • The education system and the changing needs of the manufacturing industry: There is a perception that you need a four-year degree to get a good job – when in reality, many good-paying jobs in the manufacturing industry only require a two-year degree or vocational training. Changing this specific perception of manufacturing jobs could motivate more young people to pursue them. One way this can be done is by partnering with schools and community organizations to provide career exploration opportunities for students. We also need to invest in vocational training and apprenticeship programs so students have the opportunity to earn while they learn. By taking these steps, we can ensure the skilled labor shortage does not become a crisis for the manufacturing industry – or for our economy as a whole.

The good news is that the manufacturing industry already has a lot to offer in terms of all of the above. And for the most part, manufacturers have always put an emphasis on company culture, social responsibility, and continuous improvement. 

In terms of continuous improvement, ProcessMiner is the industry-leading AI platform delivering unprecedented autonomous improvements in manufacturing operations. Leveraging the latest state-of-the-art artificial intelligence and machine learning technologies to help manufacturers optimize their production processes, ProcessMiner automatically identifies process improvements and in real-time, implements them autonomously, without the need for human intervention. It’s a manufacturing game changer.

In terms of company culture and social responsibility, now, more than ever, workers are looking for opportunities to use their skills to make a difference in the world. Young people want to know their work is purposeful, and that they are contributing to something larger than themselves. Luckily, there are plenty of stories of businesses in the manufacturing industry making positive social and environmental impacts—it’s just a matter of getting those stories out there! 

The Manufacturing Skilled Labor Gap and Social Media

Fortunately, the internet provides plenty of opportunities for manufacturers to share their stories and connect with potential employees. Platforms like Instagram, Twitter, Facebook and LinkedIn offer creative ways to showcase your company culture and values, as well as highlight any community initiatives you may be involved in. Video content is also extremely effective in this regard—after all, millennials are said to be “visual learners.”

By creating engaging content and promoting it across social media channels, you can reach a wider audience of potential employees and get them excited about working in the manufacturing industry! 

Final thoughts… The manufacturing industry is facing a skilled labor shortage, which we all know, will only worsen as baby boomers retire en masse in the coming years. The time is now for manufacturers to start thinking about how they can attract a younger workforce. 

By taking steps now to rebrand manufacturing as an attractive option for young people, you can ensure that your business will have access to the talent it needs to thrive well into the future.