The Challenges of Electronics Manufacturing Services

Last week I attended the IPEC 2017 and gave a speech about our vision of the future of electronics manufacturing and the challenges we need to first overcome to start moving towards it. Here are part of my thoughts to share in written form.

The Future of Electronics Manufacturing

Like everything in this world, electronics manufacturing is in constant change. In all likelihood, the methods in electronics manufacturing management in the future will be drastically different compared to today.

We already see a movement towards fully automated “lights out” factories, where a major share of manual workplaces are automated and human workers replaced by robots. Complex manufacturing environments themselves are turning into enormous autonomous machines. And operating this kind of a shop floor will be as simple as operating a dedicated machine where we just have to focus on inputs and outputs – not how the insides are operating.

Therefore when looking at a global network of manufacturing, shop floors may be viewed as a network of huge production points, some, like machines on a shop floor, as logically linked as a daisy chain—and all virtually connected to each other.

We see the future of electronics production management as virtual factories—located in economically reasonable locations, their production can be managed from virtually anywhere via the Internet. As a company or private user you can host or rent a virtual manufacturing service and manage it despite the distance between you and the physical entity.

What about EMS?

Lights out manufacturing expects that the environment rarely needs any major re-configuration or setup change. This is the exact opposite of how EMS (electronics manufacturing service) works.

EMS companies must get by with a very high mix of products that are mostly produced in minimal amounts. For example, bigger tier 2 EMS companies may produce 50,000 different product designs and 50 million products a year (so the average lot size is around 1,000 pieces) by using 5 billion components globally.

This is a huge picture with many details to focus on, especially when production is shared between tens of globally distributed manufacturing plants, and those, in turn, consist of multiple shop floors and hundreds of machines that have to work perfectly.

Electronics manufacturing is a complex process. You must focus on big picture and the production line and even machine-level details, all at the same time. But the reality is that those two perspectives are rarely aligned at the same moment in time.

The challenges of EMS

So why is it so hard to keep an eye on the big picture and focus on details at the same time?
 What separates us from a future of full automation and virtual manufacturing? There are many reasons, but everything starts with simple yet complex challenges.

We have identified two main “pain points” that almost every electronics manufacturer faces today:

Data collected inconsistently
  • Fragmented visibility of analytic data

Interviews we conducted with electronics manufacturers have revealed two key numbers:

  • 19 out of 20 companies admit that they possess data but feel that they are rarely using it to learn
  • 18 of 20 companies feel they cannot view the big picture fast enough, and have to rely on outdated data

Causes and Amplifiers

The reasons why we face these challenges are simple – there is no standard way to collect data from machines. There are locally-generated log files, a variety of databases, and limited connectivity due to the lack of options or proprietary protocols, etc. This causes lots of manual work and micro management with Excel sheets to prepare the collected data for analysis. The analytics exist but how you see it is scattered – like comparing 10 different Excel sheets from different departments, because each one has their own format for reporting the status.

Most machines in a production line are already digitized; they produce tons of numbers every second. Huge amounts of data are generated by a production line every day. But as the data are not unified it’s very difficult to get real time value out of it—all because we must use time wasting, semi-automatic methods to see details in the bigger picture.

Also, the methods we use for data visualization are relatively static. When we look at our production management dashboards, we see dry KPIs that are more oriented toward showing what happened yesterday, rather than attempting to project what will happen today. For example, OEE: it is good to know it and understand what’s behind it, but learning from this KPI takes just too much time, especially in environments where mistakes on the shop floor may cost thousands of euros per second.

There are also amplifiers for the challenges. When there are only one or two lines with less than 100 workers in a manufacturing plant, it is easier to handle both the big picture and those pesky technical details, because the production manager can ask for updates from shop-floor operators and engineers whenever needed. But when production grows by a number of different products, the management process becomes exponentially more complex.

The trend is that the mix gets higher and volumes much lower. The more technologies advance the more they support mass customization. This, in turn, demands much higher scaling capability, plus greater flexibility, as there is a larger variety of products to manufacture at the same time. Issues that may be solvable by simple communication between people are no more—because it just takes too much time and is not accurate enough for the company’s growing needs.

A shop floor is like the kitchen in a restaurant. If you have seen the reality show “My Kitchen Rules,” you probably know that precise planning and domain experience are crucial to succeed, but in the kitchen there is time pressure. Things don’t go the way they’re expected, and if the customer is not served well he may go to another place. It’s a great example of focusing on the big picture and keeping track of the details at the same time.

You want to be aware of what’s going on and see multiple steps ahead at the same time. It’s easy when you have prepared and everything goes as planned. But conditions tend to change at the most inconvenient moments.

Data-driven approach

We want to solve these challenges and make shop-floor management much more transparent and forward-looking than it is today. As the two main challenges are basically lack of data at a given moment in time, we focus on data.

The first mission is to make use of existing data. In most cases the data exists, but managers or other responsible parties cannot view it the moment they need it. In some cases there are no real time views—only reports from the previous day or week. We want to change it by taking the data and visualizing it for you.

As the machines in production lines produce a huge amount of data, we only have to collect it and use it as wisely as possible. Our point is not to put too much effort into improving KPIs, but rather focus on what actually happens on the shop floor.

By combining the activities of different parts of the shop floor, we create a virtual shop floor out of connected machines and production points. This is a purely data-driven approach and will likely change how we see shop floor management in the near future—as it has changed how we see information technology today, compared to 10 years ago.

When joining electronics manufacturing and information technology, we see electronics factories as our development partners and advisors.

In essence, electronics manufacturing service is a global business. Connected factories are located in a variety of countries around the world so that the meaning of a single manufacturing plant has diminished slightly. Therefore, the approach to problem solving must also be global. As different regions have their own perspectives on production, to solve challenges is possible only with a cross-border knowledge exchange.

Our solution is a cloud service that connects factories around the world within one virtual environment. It functions as one central service, but at the same time each factory can be individually managed.

It is important to understand that the digital transformation of a production environment is an iterative process – there is no silver bullet. Solutions come only with deep cooperation between IT and manufacturing disciplines.

Connect with us!

We’re open to cooperation with electronics manufacturers who face similar challenges. Contact us by signing up for the demo on our website to receive more information about simFactory.

Link to original slides.

The 4 Industrial Revolutions (by Christoph Roser at

What is Industry 4.0 and how did we get here?

When talking about simFactory, it is impossible to ignore the concept of Industry 4.0, the new industrial revolution. Although most people are familiar with the latter term, many assume it only refers to the changes that took place after the introduction of steam and water-powered production methods in the late 18th and early 19th centuries. However, they don’t know that we are in fact already at the cusp of the 4th industrial revolution.

Indeed, the first industrial revolution started when the first mechanical loom was invented in 1784. Hand production methods were replaced by machinery and small workshops evolved into the factory system that allowed for production on a more massive scale. It wasn’t until 100 years later that the second industrial revolution began, between the late 19th and the early 20th century. With the spread of electricity, the second revolution introduced major industrial developments, such as the assembly line and mass production. The period between the second and the third revolution lasted for only a few decades. Starting from the 1970s, the rapid adoption of electronics and IT enabled further automation of production in factories. The 4th and current revolution began in the 2000s, taking automation even further, and revolves around cyber-physical production systems.

The 4 Industrial Revolutions (by Christoph Roser at

In cyber-physical systems, physical components, such as 3D printers, drones and robots, and digital software components, such as data analytics and sensor technology, are aggregated into a network of interacting elements. While the initial inputs and final outputs are customarily physical, information often transitions between physical and digital states during the manufacturing process. For example, it is possible to scan a physical component and model a digital representation of this item based on the scan. These digital data can then be turned into physical information again by 3D printing this component.

Another aspect of information digitization is the concept of digital twins, or device shadows. A digital twin is a computerized companion of a physical asset that enables real time monitoring, diagnostics and prognostics of the asset. With the ability to collect massive amounts of data from different systems, and combine and analyze these data, we can use the emerging patterns to predict future activities. For example, we can model different scenarios that might happen with the asset and how these events affect the related elements in the cyber-physical system.

Based on this information, we can start to proactively or pre-emptively address the issues that are most probable or have the greatest impact. Seeing the whole data, not just the view of one single element in the system, helps us detect root causes and fix them, instead of just fixing the symptoms. This adds complexity to maintenance planning and takes some of the human element out of decision-making. However, there is still a need for high-skilled individuals to plan, execute and maintain these systems.

Finding the right people and training them, as well as gathering the financial capital to invest in this human capital, is but one of the many challenges organizations face. They will also need to learn to work with each other in the context of entirely new business and cooperation models. This, in turn, will be followed by changes in data ownership and security. Manufacturers won’t only be concerned with data privacy across the supply chain, but also cyber security. It is crucial to ensure that they cannot be infiltrated and that their factories cannot be hijacked or shut down.

In the face of these challenges, it is important to start preparing for the upcoming changes. As the duration of the periods between technological revolutions decreases, the ability to adapt fast will determine an organization’s survival. Although time is of the essence, companies still have to move judiciously. It is not necessarily smart to adopt every technology in existence, but rather it is necessary to critically assess where one currently is and where one wants to be. From there, an organization can determine what is of the highest priority and thus focus on that first.

The new technological, business and social concepts introduced with Industry 4.0 will unquestionably affect most aspects of today’s manufacturing. While the benefits they offer are tremendous, the scale of the related challenges is equal. It is advisory for organizations to already start making small steps to prepare for the future. In the next blog post we will discuss the possible challenges that the organizations will face, and how to prevent them.