Why perform electric tests?

The electric tests for resistance and voltage for the whole length of the cable are basic indicators for the material and manufacturing quality of the cable core and its insulation layers. Higher resistance shows that the material of the core is of poor quality or cross-section out of specification, which will result in increased power losses. The high voltage withstand test shows the quality of the insulation. If a break in the insulation occurs during the high voltage test then the insulation has a manufacturing defect or is made of poor quality materials or it is simply insufficient. Such low-quality insulation will inevitably result in short-circuit that might cause a fire, damage the electrical equipment or cause fatal accidents. Therefore it is understandable, why many customers require a test report that ensures the quality of their purchase.

Challenges

The standards state that the electrical tests require the cable has to comply with several requirements before a conducting a test. It has to be in the dry state and at ambient temperature when a voltage is applied of the magnitude given in the relevant cable standard, supplied from an A.C. source, between each conductor and all the other conductors and, if any, the metallic layer connected to the earth. Then the voltage has to be gradually increased and maintained at the full value for the duration given in the relevant cable standard.

If one is to manually perform such tests, he is to face many problems and inconveniences. First of all, he has to have A.C. power source that could reach the desired high voltage of up to 4000 V. Second, he should have appropriate calibrated measurement equipment to measure the resistance and voltage and he has to be able to connect them properly and to run the appropriate tests. Third, he has to manually switch between each group of conductors, and they could be many of them. This is not only time-consuming but could also lead to accidents due to the high voltage applied. Last, but not least the operator has manually to write down the test results in his test report and then to keep the records organized and available.

Automation

Siviko ACT 150 takes care of all those problems and has some additional functionalities that facilitate the work of the operator. The core automation of the cable tester is realised with hardware and software by WAGO Kontakttechnik GmbH & Co.

At the core of the automation systems is the programmable logic controller (PLC) Wago PFC200 750-8202. It is a new generation and has many important tasks to perform. The resistance tests are performed by an industrial ohmmeter that is controlled by the PLC. The communication is done through RS-485 (MODBUS RTU), and the date is refreshed in less than 20 ms. The acquired data for resistance is temperature corrected through a Wago 750-461 module. The accuracy of the resistance measurement is very important as it is also used for the localization of short circuits in the tested object.

The voltage test is done with two PQA modules Wago 750-495. With them, one can measure current, voltage, power factor and etc. With those parameters, the PLC controls the applied voltage, compensates the capacitive load of the test objects, it could locate line breaks and it could calculate the consumed energy.

The PLC has 96 input/output that controls and operates all automated processes. All of them get data from sensors and control different actuators that are used during the test procedure.

The human-machine interface (HMI) is entirely digitalized. The only buttons in the system are the four emergency buttons. We have used the modern paradigm for high-performance HMI. The process is controlled through two touch sensors. For the measurement for resistance is used a Wago e!DYSPLAY 7300T, while the overall process is tracked and recorded by an industrial touch computer. The process data is recorded by the PLC in a database located in the industrial computer.  We have used one of the most popular databases – MySQL. The memory loss risk has been mitigated through a UPS Wago 787-870. The system has been programmed with the help of an e!COCKPIT 1.3. that is very flexible and intuitive integrated environment for development, which allows for programming and tracing of parallel processes, the creation of HTML5 visualization and holds numerous libraries. As a result, the system has been programmed with hybrid-built software that consists of Siviko Smart OS that takes care of the databases and the system events and Wago WebVisu that shows the process in real-time. This concept allows the data to be accessed remotely by the managerial personnel without interfering with the ongoing process and it also allows for easy integration with higher-level systems such as SCADA, MES and ERP.

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We are now entering the fourth industrial revolution, whose main paradigm is that information and deeper integration between machines, operators, and management is the key driver of productivity growth. In order to realize this idea, each machine, facility and process in one production must be bound together and the whole data stream should flow into a unified management system where it becomes useful information that helps to optimize the production process.

This deeper integration and interdependence also requires a high degree of reliability of the newly built cyber-physical system and raises the critical need for a secure and reliable connection to the server which manages the production processes. A collision in data traffic can cause huge damage to the production process. For this reason, the modern idea of a “smart” factory would not be possible without a reliable and fast industrial communications network.

For this reason, the network switches in the main communication network ring have to meet a number of challenges caused by the heavy manufacturing process such as vibrations, electrical shocks, high-temperature amplitude, high frequency and electromagnetic disturbances, etc. In addition, the industrial network has to cover a large area with high speed and reliability.

In such systems, client devices are introduced with a variety of protocols and data exchange methods. Therefore, the network switch must be able to handle the industrial protocols of different manufacturers.

With the entry of different devices into a communications network, the requirements for its security and stability are also rising. This should allow logical separation of individual users.

Last but not least, network switches must allow for easy installation and replacement without interrupting the communication.

Project with network switches Wago 852-1305

As part of a project to build a complex industrial control system that integrates SCADA, MES, and EMS systems into a wire and cable manufacturing plant, Siviko OOD has built the first industrial communications network in Bulgaria based on the Industrial-Managed-Switch Wago 852-1305. The topology of the network makes a ring and is compliant with the requirements of IEC 62439-3: 2016: Industrial communication networks – High availability automation networks – Part 3: Parallel Redundancy Protocol (PRP) and High-availability Seamless Redundancy (HSR). Its total length is approximately 1000m, it has 11 network switches,s and serves as the backbone of the entire industrial control system. The Wago 852-1305 provides innovative Xpress Ring Reservation (less than 50 ms recovery) and JET Ring (less than 350 ms recovery). This increases the probability of failure-free operation of the network ring to 99.9%.

Why Wago 852-1305 Network Switches?

First of all, the Wago 852-1305 Industrial-Managed-Switch responds to the challenges presented – reliability, security, flexibility and ease of service. They fully meet the requirements of IEC 62439-3: 2016. The system achieves 99.99% reliability with the Xpress Ring technology, which connects all switches. One of the devices is configured as a connection arbiter. When the ring is in good condition, the arbitrator separates it logically and ensures the correct operation of the logical communication layers. In case the ring is discontinued physically in a given location, in less than 50 ms the arbitrator restores the logical interruption and the data flow is recovered. In addition, the network switches can be supplied with electricity from two different locations. Other integrated features of the switch such as Auto-negotiation, Rate limitation, Port isolation, etc. optimize your network performance and provide a secure network, offering a cost-effective solution in a small but powerful package.

Second, the architecture of the industrial communications network allows users with different roles to be separated and logically connected according to preliminary criteria – local segmentation. This increases the level of security and reduces the exchange of redundant information, which increases the free network capacity.

Third, the basic setup and maintenance of an industrial communications system is easy and reliable. It is implemented with hardware and software from WAGO Kontakttechnik GmbH & Co. KG. All Industrial-Managed-Switches provide data under the MODBUS TCP protocol. All current events are transmitted to an Ignition SCADA system from which real-time key parameters are monitored for the communications network. Each event is backed up in a database, allowing detailed analyses to be made after emergencies in communications devices.

Conclusion:

Siviko has chosen Wago 852-1305 because it meets all the requirements and challenges in building an industrial communications network – flexibility, reliability, performance, good integration with industrial control systems and easy and convenient configuration through various interfaces at an affordable price.

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We often get the following question:

“Could I use a modern production management system without replacing all the equipment? Could the software platform Ignition that you integrate, work with our legacy hardware? “

The short answer is:

“Yes, it can! It does not merely work – actually achieved impressive results.”

In this article, we will tell you about such a project in which our client decided to invest in improving his manufacturing process. He replaced the existing production management system with the state-of-the-art Ignition system without the need to make expensive investments in hardware replacement. As a result, we have achieved significant optimization of the production process and created entirely new opportunities for its management.

The Client

The client in this project is among the leading Bulgarian manufacturers of dry building mixtures, adhesives and plasters – Terazid EOOD. The production of dry building mixtures, which is part of the production of Terazid EOOD, represents a large multi-storey installation for dosing, mixing, packaging of the finished product, transportation, palletizing and storage. The production plant has a number of bunkers that store raw materials, augers, dosing bunkers, valves, rotors, mixers, packing machines, aspirators, pneumatic and electric vibrators, conveyor belts, industrial robots, and etc.

The Challenge

One of the existing constraints of the old production management system was the way it handled recipes. The parameters of each recipe – doses, priority, technological times, were stored in another system and loaded through a separate interface in the programmable logic controller that manages the production. This work organization created problems for the operators because they could not autonomously modify and create new recipes. This is particularly bothersome when creating new products because many changes to the prescription parameters have to be made during the production tests until the desired result is achieved. Imagine how clumsy is the process, if for every little recipe change one needs to seek co-operation from the outside or manage the recipes from another system. In the manufacturing of building mixes, it is of the utmost importance that the recipes are executed correctly and without any variation in quality. For example, for tile adhesives, a small difference in chemical additive ratios can significantly change the hardening time, making it difficult for construction workers to do their job properly. Therefore, the existing situation in recipe management had to be changed.

Together with the customer we analyzed the current state and decided that the existing SCADA system could not be upgraded and should be replaced altogether. The new system has been developed on the industrial application platform Ignition due to its many technical and licensing advantages. Ignition communicates equally well with both hardware and databases, and its modular architecture and built-in tools allow its user to easily customize their system by building the exact applications they need. Indeed, the new system has become more than SCADA – industrial control system – and it is a logical first step in the digitization of the production process.

The Solution

In just 3 months, we built a new system that achieved the desired results. In addition to the basic new functionality, production and recipe management, we have created a completely new organization of the human-machine interface (HMI), we have added automated reports of spent materials, completely new systems for alarm events, trends and a number of other improvements.

The new recipe management functionality has enabled operators and technologists to create, edit and load prescriptions themselves through the production management system. This allowed them to be much faster, more accurate and more flexible, both during the standard production process and during the development of new recipes. Another great benefit of digitization was that all of the recipe features are now included in the system and are accessible and visible to the whole team – management, technologists, maintenance and operators. In addition to the core functionality, we also added a number of improvements, such as a calculation of the estimated remaining production cycles for which there is enough available material. This estimation helps the operators to easily track the available materials and to plan the re-fill of the bunkers thus avoiding unplanned downtime of the production process. Another great benefit of the new system is that it allowed for better use of the existing hardware by “unlocking” features that were previously unavailable – such as additional tuning capabilities, and so on.

Have you ever thought about how important an HMI is in managing a manufacturing process? According to a study conducted in the United States, a large share of the serious industrial incidents are due to a badly created HMI. Apart from the major incidents reported in the media, there are many small but permanent losses of quality and productivity that often go unnoticed. Screens cluttered with unnecessary information, burdensome colour schemes, non-intuitive navigation, ambiguous data, “buried” settings, missing alarm events, etc often mislead or confuse the operators who are responsible for running the process in real time.

In order to present the important information clearly, simply and unambiguously, we have decided to completely revamp the existing HMI by creating a new intuitive navigation scheme, simplified graphical representation and a new high-performance colour scheme. We simplified the graphical representation of the processes by removing all the elements that do not carry useful information. This allowed us to show the entire production process on a single screen and with a quick look to capture its current state. Furthermore, under the new colour scheme, we’ve focused on important messages requiring attention. The screens are mostly in the neutral colours grey and white, real-time production data is blue, and alarm states and events are in the attention-drawing colours yellow, orange and red. All this allows operators to track the process even when they are meters away from the screen and do other work.

Another feature that is mandatory in today’s production is the alarm system for certain events. It helps operators to pay attention to the critical conditions of the production process, thus avoiding accidents or product deviations. In the new alarm system, we’ve created alarms for a number of key parameters for the production such as too high or low in raw material levels, an emergency stop activation, and more. Alarms have different levels of priority and are stored in an alarm log where it is written exactly who, when and how responded to the event.

In the new system, we have built up different levels of user access, which allowed user groups with a different interest focus to operate without conflict in a unified production management system. For example, it allowed operators to manage the process, while at the same time technologists observe and optimize it without the risk of stopping a machine or workflow. Furthermore, we have provided remote access to the system and the management staff in the office building via encrypted SSL communication.

Another important benefit of an industrial control system is their ability to extract data from production and turn it into useful information that helps everyone in the team. Ignition’s ability to communicate equally well with both hardware and databases has allowed us to build powerful data visualization and reporting tools. Production data is now available in real-time through the built-in Ignition tools, and in addition, we have created an algorithm for the spent amount of resources on a daily basis. This allows accurate and rapid accountability and also helps in improving production planning.

Conclusion

The new production management system has improved the production process by making its management much easier, more reliable, and more flexible, without having to invest much in hardware upgrades. The system has eliminated a number of constraints and has created new opportunities for the production team to do a good job. As a result, our client can now focus on the most important thing for him – creating a high-quality product with high productivity and with the ability to continuously improve processes.

This project is a good example of how important the role of modern production management systems is, even in places where a legacy of different machines and installations has been created over the years. According to us, this type of system is the first step towards building a “smart factory” and digitizing production. As we said at the beginning, their implementation is possible and the results are impressive.

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One such opportunity was Principles of Manufacturing (PoM) MicroMasters program offered by the Massachusetts Institute of Technology (MIT) and organized by its renowned Mechanical Engineering department. It is offered online on the educational platform edx.org. It is equal to one semester of the Master of Engineering in Advanced Manufacturing and Design degree that is offered on-site. I enrolled in the program in August 2019 have just completed it (June 2020). I am very happy with what I have learned and I would like to share my experience and key learning points. I think that people interested in manufacturing might find the information useful. In addition, some could even find the motivation to enroll in the PoM program as well.

1. What are the Principles of Manufacturing?

1.1. Defining Principles of Manufacturing

What do Principles of Manufacturing mean? I think that this is an important question that has to be explained in detail, as this is the very reason why the PoM program was created. I will define both terms as they were explained in the program by the faculty.

First, let start by defining what a principle is. There are many definitions, but one of the best is that a principle is a basic truth that explains or controls how something happens or works.

Second, what is the definition of manufacturing is and how does it differ from other human activities? Manufacturing is production in volume in which there is a continuous flow of “new” materials, going through a system of unit processes. This is how it differs from artisanship or the production of single prototypes. Furthermore, it combines multiple supplier streams and has to create a product with minimal variation. Manufacturing is a massive undertaking and demands huge capital investment in the form of fixed assets, materials and components, and labor. The ultimate goal of any manufacturing unit is to meet customer demand and to be commercially viable over time.

Principles of Manufacturing, therefore, stands for the basic truths that explain how the complex world of manufacturing works. They are a set of elements common to all manufacturing industries that revolve around the concepts of flow and variations. All of them are analytical methods to model and control flow and variation at four levels of the enterprise:

• Unit process (component) level where one has to consistently meet specifications on a unit basis.
• Factory (production system) level where there is a flow of “new” materials through a system of rate-limited and failure-prone processes.
• Market (supply chain) level where there is a flow of materials and products through a network of producers and customers.
• Company (business) level that takes into account the financial and management aspects of volume production.

1.2. The Principles of Manufacturing support technological development

Those principles are fundamental because they are applicable to any industry and support technological development at any scale and at any level of technological sophistication. Furthermore, they can help in all sorts of decision-making because they are based on data and analytics. They could be used to improve tremendously the operations no matter what the state of technology is.

The flow of materials through variable processes and systems has been around since the first industrial revolution. Even in the new era of Industry 4.0, this still holds true. No matter what the technology is, in our physical world there is always a flow of materials through variable processes and systems, where they are being transformed into the final product.  In fact, one cannot do a proper transition into Industry 4.0 without knowing the principles. Technologies such as digitization, industrial control systems (SCADA or MES), artificial intelligence or machine learning work really well when the manufacturing process is well understood in detail. Then all the gathered and analyzed data makes sense and could be turned into informed decisions and actions. In order to have smart factories, one first needs smart people.

1.3. Key Performance Attributes of Manufacturing

The faculty at MIT has defined four main attributes of manufacturing process performance, which are key in evaluating how well a manufacturing system performs:

• Rate – The rate metric provides a measure of how quickly a process takes place. At the machine level, rate can be reported as the number of parts or unit operations that can be completed in a given unit of time. Alternatively, rate may be reported as the cycle time, or the time it takes to complete one part or process a unit volume of material. A faster rate is typically more attractive when comparing processes, notwithstanding attributes that may suffer if rate is increased such as tool life or dimensional quality. The rate for a given process depends on the process physics as well as on the specific part design, equipment capabilities and the overall organization.
• Cost – The cost of manufacturing a product is often a major component of its sales price; as a result, understanding the cost drivers of each process is essential to understanding the influence of design on manufacturability. To assess the cost associated with a particular process, expenses incurred can be classified into two categories: fixed costs and variables costs.
• Quality – The quality a consumer perceives in a finished product may be influenced by many factors: durability, aesthetic appeal, etc. These attributes are often subjective, and one cannot please everyone. From the process perspective, quality can be measured as the ability to hit a target specification consistently over time.
• Flexibility – flexibility in manufacturing is the ease with which a process can be adapted to produce a different part or design. There is not a convenient quantitative metric for flexibility, yet a more flexible process requires a lower cost and/or a shorter amount of time to adapt to a new part design than a less flexible process.

2. Courses in the PoM program

The PoM program has eight online courses and four final exams, which represents the equivalent of one semester of coursework at MIT at a Master’s degree level. They provide a fundamental basis for understanding and focus on the analysis, characterization, and control of flow and variation at different levels of the enterprise through the following subject areas:

• Manufacturing Process Control: Modeling and controlling temporal and spatial variation in unit processes.
• Manufacturing Systems: Modeling and controlling flows and variations in manufacturing systems with stochastic elements and inputs.
• Supply Chain for Manufacturing: Operating and designing optimal manufacturing-centred supply chains.
• Management in Manufacturing: Understanding the uses and flow of business information to start-up, scale-up, and operate a manufacturing facility.

In the next sections, I will describe the key points in each of those courses.

2.1. Manufacturing Process Control I and II

The first part of the course is about modeling and controlling temporal and spatial variations in unit processes using statistical methods and mostly statistical process control (SPC). The SPC was first developed by Walter Shewhart in 1924 while working on an assignment to improve the voice clarity of the carbon transmitters in AT&T telephone handsets. AT&T was producing millions of telephone handsets and inspecting each produced unit was virtually impossible.

Shewhart’s observation was that most defects come from a variation in the manufacturing process. He observed that the flow of “new” materials through the processes has inherent random variation, which may result in variability in physical dimensions and properties. If those variations are within the targeted specification limits, then it is all right. If they are outside the targeted specification limits then the product is defective. For example, say that you have to machine a shaft with a diameter of 7mm with a tolerance of ±0.1mm. If the diameter is 7.1mm or 6.9mm, then it is within specification and it is acceptable. In contrast, if it is 6.89mm or 7.11mm, it is categorized as defective and has to be either re-worked or scrapped.

There are two major sources of variation – deterministic or random. The deterministic variation has a cause that can be identified and corrected. This could be an operator’s fault, tool wear, defective raw material, ambient climate, etc. However, there is also a purely random variation the source of which could not be determined or could not be corrected. If one could detect and correct the deterministic variation, he would end up with a purely random variation. This would mean that the process is in statistical control and keeping it in control reduces waste and improves quality.

When the process is in statistical control if we collect batches of data from the processed products and plot it, over time the distribution of the data will start resembling normal (also known as Gaussian). It will look just like this figure:

From the above figure, one could observe that 68.2% of all points should be within one standard deviation (noted with the Greek letter σ – sigma) of the mean, or that 95.8% should be within two standard deviations from the mean, etc. For example, if we take the previous example in which we measure a diameter of a shaft and say that its mean is exactly 7.00mm and the standard deviation is 0.05mm, then 68.2% of all measured points should fall between 6.95mm and 7.05mm.

Once we have determined the mean and the standard deviation of a stable process with no deterministic variation, we can start running statistical process control. In essence, it means that with each measurement recorded, we have to determine if what we see is still the same normal distribution or a deterministic variation is kicking in and it has changed. In other words, each time we record a new data point we have to determine if this what we expect to see based on the parameters of the known normal distribution or something has changed and the process is no longer in statistical control.

This is done with the help of a control chart like the one shown below. When the collected data points are plotted over time we get the following graphic:

The blue dots are what we expect to see if the data indeed follows the expected normal distribution. It should not resemble patterns, and it should be within the set control limits. If we start to observe unusual behaviour – for example, data points outside of the control limits or clear patterns – for example, 8 consecutive points in one direction, we have to stop the manufacturing process and examine if there is a deterministic cause for this variation.

The red dotted lines are the control limits. They are commonly set at ±3σ. Therefore, the probability that a point will fall on either side of the control limit is just 0.2%. It is usually a signal that the process is not in control any more. In this case, the production has to be stopped and examined for causes of variation.

SPC is a tool that is very useful for optimizing process quality over large flows when testing all produced parts is either too costly or impossible.  It could help in detecting problems before they result in large quantities of defective products or even avert this happening altogether.

While the first part of the course was entirely dedicated to the topic of SPC and passive observation of the existing process, the second one was about active modeling and minimizing variation for the sake of process improvement. The course explains the most common techniques for determining causes of variations and different methods to achieve process optimization and robustness. I will not go in detail about this part of the course, as it is very technical, but it definitely gives a good glimpse of what one can do if we have data available from the manufacturing process.

For me personally, those two courses were the most difficult, as they involved a lot of statistics and mathematics. I have studied statistics 15 years ago, but I had to invest significant time in dusting-out my knowledge. I also had to learn how to perform some basic operations in Matlab. I will definitely use those skills, as in the meantime we have become an integrator of the SPC software by Sepasoft, which is a natural extension of our work with the Ignition platform. Understanding and appreciating randomness can be useful in other aspects of our work such as in the design of new machines and equipment or in calibrating certain automated processes. In one of our latest projects in which we created a machine for quality control through machine vision, we used such techniques to calibrate the machine.

2.2. Manufacturing systems I and II

The second course in the program is about modeling and controlling flows in manufacturing systems with stochastic (random) elements and inputs. The overarching theme is that flow in a factory system has variability that is caused by the unreliability of machines or equipment, variations of rate, setup times, inventory buffers sizes and levels, blockage of starvation of machines, etc. Factories are viewed as random dynamical systems. The focus of the course is on quantity – in particular how to meet goals for a specified quantity at a specified time. This course has fascinated me the most from the very beginning as it is naturally linked to the lean manufacturing concept. which I studied during my MSc in Management degrees.

It was a tough and rewarding course. The first part of the course sets up the theoretical basis for modeling manufacturing systems such as probability, stochastic processes, and queueing theory. Then it reviews some quantitative models of single-part-type flow lines with unreliable machines. It also explains the notion of bottlenecks, the influence of inventory buffer sizes on production rates, and the basic ideas that make the Toyota Production System so interesting. Eventually, when the mathematical models cannot handle all the complexity of the real-world complexity one can use simulations.

A textbook for this course was the book “The Goal: A Process of Ongoing Improvement” by Eliyahu M. Goldratt. This is one of the best business books that I have read and I highly recommend it. Mr. Goldratt is also famous as the inventor of the Theory of Constraints, which is a popular managerial concept. Furthermore, I also used simulation software called AnyLogic for the first time. You can try it as well – it has a free version. The course uses a lot of animations to explain the basic ideas, which makes it fun to watch.

2.2.1. Unreliable machines

A big part of the course revolved around unreliable machines put into a chain of manufacturing processes. Unreliable machine means that it can fail at times and stop producing with some mean-time-to-fail and mean-time-to-repair ratios. So the nominal production rate is decreased due to the unreliability.

Between each machine, there is an inventory buffer. When a machine produces a part, this part is stored in the inventory buffer downstream from it. If a buffer is full, then the machine is blocked and cannot produce anymore. If the machine upstream of it fails and the buffer upstream is empty, then the machine is starved and is forced to stop production.

A simple presentation of this chain looks like this:

There are at least two important takeaways from this setting that has practical implications.

2.2.2. Bottlenecks

Seldom in real life all machines in a chain have the same production rates or the same reliability levels. It means that there is always a machine that is either slower or less reliable than the rest in the chain. This machine is called a bottleneck (in the figure below it is Machine 2). One cannot produce more than the bottleneck can handle as it either blocks or starves the other machines in the chain. Therefore, one should strive to optimize the efficiency of the whole chain rather than to get 100% productivity at each separate machine. Otherwise, it will result in the accumulation of a lot of unused inventory and the overall productivity of the chain will not change at all. One should instead focus on the bottleneck and make sure that it is worked at its maximum – it should never be idle, it should never be blocked or starved and when it fails, it has to be repaired as fast as possible.

It is also useful guidance of future investments – the best returns come from investments in the bottleneck. When a bottleneck is no longer such, due to increased capacity or improved efficiency, usually a new one appears. Sometimes, the bottleneck might not even be physical, but a result of incorrect internal policies or procedures. The concept of bottlenecks is a powerful tool.

2.2.3. The role of inventory

The inventory has a tremendous impact on productivity and is a very good indicator of what is happening in the manufacturing chain. It acts as a buffer between the unreliable machines and allows other machines in the chain to continue working, while one is down for repair. If there is no inventory buffer at all, it means that when a single machine fails all others in the chain will have to stop working as well. However, having inventory costs money and space. The higher productivity levels can be achieved with infinite buffer sizes, however, this is unpractical. Nevertheless, placing strategic inventory buffers of various sizes could have a tremendous impact on the productivity of the chain.

2.2.4. The usefulness of data

I cannot cover in detail all the whole course in this article. However, I can say that the course does an excellent introduction on how to design systems to maximize rate and minimize cost. If one is to apply those principles in the real world, having correct and up-to-date information from the production floor will be extremely useful. This explains all the talk about getting and analyzing data from the production floor in the last few years. For example, if one has an Overall Equipment Efficiency (OEE), Track and Trace, or SCADA system in place, such as Ignition, he could then use the gathered information to optimize the manufacturing system. Furthermore, the data could be used to create simulations. It is far cheaper and faster first to play around with simulations before committing huge amounts of time, labor and money in real-life projects.

We have also successfully applied the knowledge in a project of ours: Robot Palletizing Optimization. We identified the bottleneck, examined it, and eventually improved its performance significantly. The introduction of a buffer had a huge effect on the rate – we improved it by more than 75%!

2.3. Supply Chains

The third part of the PoM is about operating and designing optimal manufacturing-centered supply chains. The emphasis is also once more on uncertainty, but this time in terms of demand, supply, and logistics. The chief task is that one has to manage his inventory and capacity so that he can achieve the level of service that he wants.

There are two major topics in the class – managing inventory and planning capacity and network design. The inventory could be modeled through many different methods. Some of them are basic, such as the newsvendor problem, while others such Economic order quantity model or the Guaranteed-Service Model is more advanced. They are widely used in the industry and the course provides examples from Procter & Gamble, HP, and Intel that have optimized their supply chains and inventory using such methods and of course software solutions.

The second topic of planning capacity and network design for me personally is more interesting. There is extensive use of optimization modeling and a huge part of it is done in spreadsheets with Excel Solver and Open Solver that are freely available plug-ins in Excel. Actually, one can use those tools for so many more tasks than modeling capacity and this is what I really like about them. Similar, but more complex and powerful solver tools have been used in the US companies for decades. I still have not heard of anyone using such tools in Bulgaria. Perhaps there is no such dire need as the companies are smaller and supply network designs and capacity planning is simpler. Or maybe I am just ignorant and such software is used as well. I do not know.

Another interesting topic in the course is how to design a network in order to achieve better flexibility. The total flexibility means that each machine or operator or factory (production unit) can produce all products. However, this is either too expensive to achieve or unrealistic. The course instead introduces a framework of evaluating the benefits of flexibility and it actually shows that limited flexibility, if deployed in the “right way”, can achieve the benefits of total flexibility. The “right way” is to design the network so, that a production unit could substitute at least one other production unit for a specific product. One has to create the longest possible looped chains, by which the production resources are linked to more and more products. In this way, one could dynamically shift production capacity for a product, which achieves almost the same benefits as in the total flexibility case.

Overall, it was a challenging course that involved a lot of modeling and calculations. Nevertheless, it was also very rewarding, as I understood how supply chain management is done in some of the world’s business leaders. I am grateful that the lecturers had very concise and understandable explanations of seemingly complex topics. This course would be very useful for professionals involved in inventory management, capacity planning, or supply chain management.

2.4. Management in Engineering

The last part of the PoM is about understanding the uses and flows of business information to start-up, scale-up, and operate a manufacturing facility. One could only imagine how much variability there is on this level of decision-making. There are many constant changes in terms of the flow of capital, sales, energy and resource purchasing, market knowledge, and variations in competition, personnel, etc. So it is essential to link the business objectives to the production objectives.

The first part of the course covers topics such as business plans, sales and marketing, financial accounting, project planning, and intellectual property and licensing. Since the whole program is mainly targeted at engineers, this course is broad and does not go into much detail. My academic background is in business, so it was arguably the easiest course for me. However, I did learn a few new things about writing a business plan and I even used it in our own company. I am quite happy with the result. The template that I used was created by Derby Management and could be downloaded free from their website: here.

The second part of the course was organized in case studies that cover different aspects of business management such as leadership and business culture, marketing, operations, technology, etc. This was more difficult but quite interesting as the case studies were well made and informative.

3. Efforts needed to complete the course

All eight courses took 34 weeks to complete and I had to spend approximately 18 hours per week. Therefore, it is a massive effort, especially if you are not familiar with statistics and probability. I advise you to take a course on those topics before starting those courses. You should also be very familiar with Excel and feel comfortable with algorithms and mathematical models. You do not need to be an engineer to complete this course and you do not need to understand in detail certain manufacturing processes.

To pass the course, you will have to do homework assignments every week and pass an exam at the end of the course. You will receive points for both homework and exams and will need to get at least 60% to get a passing grade. I personally got four A (90 – 100 %) and four B (76 – 89%), with two of them just 1 or 2% away from the A grade.

A big plus of the program is the faculty. All of them, have excellent theoretical training and a good pedagogical approach. However, they have worked on many real projects in various companies and industries. That is why they have a very practical approach to everything they teach.

4. Conclusion

I believe that the Principles of Manufacturing MicroMasters program could be useful to many people involved in manufacturing. Even though it is a significant investment of time and effort, it is totally worth it. Those principles are truly universal and can be applied in any industry at any level of technological sophistication. Even the advent of the new technologies that are part of Industry 4.0 will not change that. Just on the contrary – Industry 4.0 will introduce new technologies that are significantly more focused on extraction and analysis of data. Data is useful only when one could make well-informed decisions based on it. If you are not familiar with how your business works, then this data is meaningless and such an investment will never payback. And if you are, it will be of great help in increasing the commercial success of the manufacturing business in the long-run.

We have always been committed to increasing the awareness level of the local industry and we have been supportive of organizations such as Lean Institute Bulgaria, Trakia Tech, Stoos Network Bulgaria, and the Professional Association of Robotics and Automation that also want to spread better managerial practices and new technologies. There is a visible trend that local managers and engineers want to increase their knowledge and perfect their skills. I hope that this trend will become even stronger because we need smart people in order to have smart factories.

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Industrial robots are among the most promising technologies for increasing manufacturing automation. They offer great flexibility due to their three or more axes of movement and the ability to be reprogrammed and repurposed. Furthermore, they are very dependable, extremely precise, and now with the increase in computing power can make a sense of their surroundings. They could be used in many industrial applications such as palletizing, arc or spot welding, material handling, machine tending, assembly, pick and place, painting, milling, deburring, 3D printing, and much more.

Although industrial robots are machines like any other, they have always captivated the human imagination because of their elegance, speed, power, and precision. They have become a symbol of high-tech manufacturing. You can read more about industrial robots and their advantages and disadvantages on our website.

Historic development of industrial robots

The first industrial robots have appeared in the 1960s. The first real growth spurt in the USA occurred as automotive manufacturers automated their weld shops. A second growth spurt appeared in the 2010s due to increased technical capabilities and lower prices. The industrial robots’ price has decreased by more than 50% since the 1990s.

Due to collaboration between the state and Japanese robot manufacturer FANUC, industrial robots were first adopted in the 1980s. In 1983 a factory called “Beroe” in Stara Zagora was opened. It was delivering approximately 80% of all industrial robots and manipulators in the Soviet bloc. The production ended in 1990 with the fall of the Soviet bloc. The use of modern industrial robots has experienced significant growth in the last 10 years, due to increased technological usefulness, lower robot costs, increased ROI, and foreign direct investment in new manufacturing facilities. 

Current development of the industrial robots’ market

The stock of industrial robots in Bulgaria and worldwide

In 2019, there were 2.7 million stand-alone industrial robots installed worldwide. The global stock of robots has increased by 373 240 units in 2019 and the expected growth is 12% per year. The average robot density in the manufacturing industry in Europe was 114 units per 10,000 employees in 2019.[1]

Annual Installations of Industrial Robots Worldwide [thousand units], source: McKinsey & Company

There are no detailed statistics on Bulgaria, but the country is currently among the least robotized countries in the EU. One of the main reasons is that there are still no OEM car factories in the country. Traditionally the automotive sector is the biggest user of robots in the other European states. Most probably as of 2020, there are approximately 1000 to 2000 installed and functional industrial robots in Bulgaria.

Drivers of the market growth

McKinsey & Company has published an excellent report on the robotics market and we recommend you to have a look at it for in-depth information. The growth of the industrial robots’ market has been primarily driven by:

• An increasing variety of models – A larger variety of robots enables new applications. From the first electrical 5 axis, microprocessor-controlled robot in 1974 the number of models has increased to more than 300 models today. Furthermore, robots have become larger and can handle heavier loads due to an exponential growth of payload from 6 kg to 2,300 kg. They also feature more axes and require fewer controllers. In some cases, more than 30 axes can be synchronized by one controller.
• Greater technical abilities – Greater precision enables new applications and safer use. For example, working alongside humans to automate tasks that could previously not be automated. Mobility is another technical advancement, opening the area of intralogistics automation to robotics applications.
• Increasing labour costs – As the cost of manufacturing labour is rising, the payback for robotics is becoming ever more attractive. This is not just valid in the industrial countries (e.g., 24% rise in manufacturing labour costs in the US since 1990) but also in traditionally low-cost countries such as China or India. Bulgaria is not an exception as labour costs in the manufacturing sector doubled since 2010.
• Accessible talent – While robotics engineers were once rare and expensive specialists, people with the skills required to design, install, operate, and maintain robotic production systems are becoming more widely available. This observation is also valid for Bulgaria, even though the local market is in its early phases of development.
• Ease of integration – Advances in computing power, software development techniques, and networking technologies have made assembling, installing, and maintaining robot cells faster and less costly than before.

New trends in industrial robotics 

If robots could do more complex “smart” tasks and their integration is faster and cheaper, they will be used by even more companies. The general expectations in the industry are that there will be three main areas of improvement over the next decade – adaptability, ease of programming, and ease of integration. Those are usually grouped together as advanced robotics.

Adaptability: major limitation in the use of industrial robots

Adaptability is the ability of robots to make sense of the surrounding environment and to adapt their activities accordingly. Despite the great advancements in the field of robotics and continuous efforts to make robots more and more sophisticated to match the capabilities of human beings, they are still far less adaptable and intelligent than humans.

Creative and knowledge-based jobs are less susceptible to automation. This is also true for jobs that involve physical labor, but take place in unpredictable environments. We rely on human ingenuity and our ability to learn and adapt without being trained for every possible scenario. For example, a manager can order a worker who is drilling to transfer to a new working station that is painting, packaging, or polishing. The relocation and learning of the new task will happen fairly easily and fast. The reason is that learning new things comes naturally to humans.

That is not the case with robots. First, they are restricted by the reach of their arm, the functions of their tools the availability of sensors, etc. Even more important is the fact that robots are not able to act any different from what they are programmed to do.  Furthermore, robots still need a predictable and very well-structured inflow of inputs (e.g., boxes, parts, or other materials). Even the most advanced robotic systems of today that use AI algorithms and machine learning to train themselves are far less adaptable than humans.

Adaptability: new advancements in robotics

Nevertheless, the advances in machine vision and other smart sensors, advanced grippers, and machine learning applications will increase the adaptability of robots. This will allow robots to adapt their functions and behavior to changes in external conditions. This will allow them to work in more unstructured environments. An interesting example comes from FedEx that has managed to implement robots in an area that a few years ago was impossible – robot pickers in the warehouse. This is a very difficult task for a robot because the work is very messy and unstructured. Check out this video that will tell you more about the increasing adaptability of robots:

The increase in adaptability will also enable more applications in which robots work safely alongside humans. When collaborative robots are equipped with advanced embedded vision systems and versatile grippers, the addition of machine learning creates a highly complex and capable robotic system. Furthermore, it could increase interactivity when working alongside humans. When humans and collaborative robots begin to work on more complex tasks together, communication will become necessary. Collaborative robots could be built to recognize certain voice commands or hand gestures, avoiding the need to reprogram, causing downtime in the middle of a project.

Another restriction of industrial robots is their inability to move. This is also changing as robots can now make a better sense of their environment and move safely around, performing useful tasks. The most interesting examples in this area come from the company Boston Dynamic and their various models of robots:

The practical application of those robots may be still limited. However, this will surely change in the coming years as robots start performing more “smart” tasks on their own.

Ease of programming: another major barrier for industrial robots use

Industrial robots were originally developed to serve automotive body manufacturers. Cars are produced in high volume, and because the products are so similar (low-mix) the same robot program can be used for several years. This means car manufacturers can amortize the cost of their custom system integration over the high number of cars made. Moreover, in this context, reliability, speed, and precision are more important than ease of use and simplicity of programming. Many robots on the market today were optimized with these constraints in mind.

Ease of programming: simplification of programming and offline programming

Now, there is an increasing interest to adopt robotics from industries that have high-mix and low volume production. Ease of programming is of paramount importance for the faster and cheaper initial integration and improved maintenance and re-programming over its life-cycle.

There are two major trends in this direction. First of all, the robot manufacturers are simplifying the programming of their robots. Today, most industrial robots are programmed in the same way that they used to be in the 1980s. Engineers do it through an online touch pendant and the use of functions in a proprietary programming language. However, this has been changing, especially in the collaborative robot market where the programming has been simplified and made accessible even for non-programmers. For example, the new FANUC CRX collaborative robots have a touchscreen tablet with a drag and drop programming interface, which makes it easier to create and adjust programs. We expect to see this trend of programming simplification in the more conservative industrial robots segment as well.

Second, there are increasingly powerful offline robot programming applications that allow you to create your robot application first in the digital world by using CAD models, before applying it in the real world. This allows for the automation of the programming of many different applications faster and with less downtime. However, still, a major limitation of this way of programming comes from deviations between a CAD model and the physical world. With the increased adaptability of robots, offline robot programming applications will become more useful. You can read more on the topic of offline programming in this article.

Ease of integration of industrial robots

There is a lot of non-value-added work in the robotics deployment due to the lack of standards across the highly fragmented robotics industry.  Each robot maker has its own unique controller and operating system (OS). And they each support different communication protocols, which in many cases is not free to use. There is no single dominant OS on the market and no single vendor dominates more than 15% of the industry. As a result, there is a technical challenge of integrating technologies that were never meant to work together. And the challenge of coordinating different vendors and project stakeholders.[2]

We expect that this situation will improve over the next decade. First of all, there are already attempts in the industry to develop OPC UA (Open Platform Communications Unified Architecture) information model for robotics communication.[3] This will simplify the communication with the robots and their integration with other applications and devices such as PLC, SCADA, MES, IIoT or ERP.[4]

Furthermore, with the increased adoption of robotic applications, there will be an increasing interest in simplifying the integration and maintenance of the robots. Many companies have accumulated robots from different vendors over the years and maintaining them is very expensive and challenging. Each vendor has its own proprietary controller and software and having experts that understand and support each and every brand equally well is difficult. One solution would be to have a common operating system for all robot brands and there are already options available. There is an open-source initiative called the Robot Operating System (ROS) and an increasing number of start-ups that offer robot OS that is independent of the robot vendors.

In a messy physical world Integration of industrial robots will remain complex

We do not know whether they will come to dominate the market in the same way as Windows or Android did in the computer and smart-phone market, as there are many unique barriers in the robotic market. It is very likely that the market will remain fragmented. However, the market will surely put pressure on the robot vendors to open-up their software and make it easier to integrate and maintain.

However, unlike computers, which deal with digital information, robots must deal with the physical world as well. The information world is clean as it is made of zeros and ones. The analog world is messy as it has infinite variability. So even if robots had standardized hardware, communication protocols, and software, the fact is that the world around them can never be standardized. So long as robot programs must be customized to handle real-world objects, robot deployment will remain complex.

Summary: advanced robotics will transform industrial operations

The development of adaptability, ease of programming, and integration will give rise to what is termed advanced robotics. Advanced robotics systems will transform industrial operations. Compared with conventional robots, advanced robots have а superior perception, integrability, adaptability, and mobility. These improvements permit faster setup, commissioning, and reconfiguration, as well as more efficient and stable operations. The cost of this sophisticated equipment will decline as prices for sensors and computing power decrease, and as software increasingly replaces hardware as the primary driver of functionality. Taken together, these improvements mean that advanced robots will be able to perform many more tasks more economically than the previous generation of automated systems.

References

[1] Executive Summary World Robotics 2020 Industrial Robots

[2] Lean Robotics, Samuel Bouchard

[3] VDMA and OPC Foundation – OPC UA Information Model for Robots

[4] OPC UA Robotics Companion Specification

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