Toronto, Jun. 12 2020 – COVID-19 has had a big impact on manufacturer’s operations, increasing costs and reducing output. The majority of manufacturers during COVID-19 are experiencing a slow period with fewer orders and reduced staff. Now is a rare situation when manufacturers have resources available to implement improvements.
The silver lining is that COVID-19 has created a great opportunity for leadership to work on getting leaner, faster, and smarter. The latest release of ShopFloor Pulse AI engine helps quantify how much faster manufacturers can get. It identifies the individual parts with the greatest opportunity and provides guidance on which production activities should be improved.
In this un-precedented-time, Production Managers are further challenged because outside experts can’t enter into their factories. To overcome this limitation, Esprida has designed and developed a remote installation process to enable contactless installation and support of factory performance monitoring solution. With the contactless approach, Production Managers are able to implement real-time machine monitoring during a slow period when it is most convenient.
Production managers have also been challenged when analyzing reports from MES and ERP systems, one comment we’ve heard is that there are too many reports, and not enough information. One underlying issue is that ERP reports rely on user input and planned data rather than actual data from the machine equipment itself; the reports are often a week late, contains bias and gaps in the data. “We saw a great opportunity to help manufacturers extract data from machines and give them real-time, unbiased dashboards and historical analysis reports,” says Asad Jobanputra, Director of IoT solutions.
We developed an AI engine that analyzes simple machine events in real-time to provide meaningful information for manufacturing leadership. “We’ve been using ShopFloor Pulse and for the first time, we’ve had real-time actual data about part cycle times, and operators know if they are on-track without having a supervisor looking over their shoulder.” One client reported, “The cost-savings opportunity is huge for us”.
A continual improvement strategy is any process within a workplace that helps keep the focus on improving the way things are done on a regular basis. this could be through regular incremental improvements or by focusing on achieving larger process improvements. Production managers are often given a target to improve efficiencies by 2% or more every review period.
Manufacturers that focus on improving continuously become more competitive over time and can maintain their advantages in their industry. Companies that simply maintain their current performance levels but don’t make any improvements are falling behind the performance of their competitors and their sector at larget.
Taking on-going objective measurements help identify, prioritize and measure the effectiveness of the efforts being made. ShopFloorPulse, is an accurate independent measurement system that is objective, real-time and doesn’t require any manual recording steps. If you want to introduce continual improvement tools to your senior leadership, we prepared a presentation you can download and customize.
Continual Process Improvement Model
Many methodologies are available for continuous improvement. Finding the right what for your sector is important, we evangelize using the latest industry 4.0 real-time technologies. However, all continual improvement models have a similar cycle:
Making ongoing improvement in performance, commitment, strategy and process all help build up the company’s bottome line. This image also illustrates that any improvements in these four categories will also help build up improvement in the overall quality being produced by the facility.
When implementing continuous improvement processes, it is critical that employees are involved from the bottom up. Employee involvement, especially from operators, is critical because they are the ones that will have the best suggestions, if changes are pushed upon them there may be a backlash for being forced or penalized for higher performance standards or following processes that they don’t agree with.
A modern twist to Plan-Do-Check-Act
Another helpful concept is the “plan do,check, act” process. This is a cyclical process that traditionally walks a company or group through four steps of improvement. By continuing to cycle through these steps, improvement is always being worked on and evaluated.
Each step build on the previous step and feeds on the next. We recommend integrating the check step and the do step using the latest industry 4.0 technology.
Typically operators get feedback whether they are on target or not until the end of shift, end of week or end of month – well after they have an opportunity to make corrections. We recommend providing immediate feedback throughout the day via ShopFloorPulse so that if they are ahead or behind they know throughout the day and can make corrections continuously.
Plan – In the planning phase, teams will measure current standards, come up with ideas for improvements, identify how those improvements should be implemented, set objectives, and make the plan of action.
Do – Implement the plan that was created in the first step. This includes not only changing processes, but also providing any necessary training, increasing awareness, and adding in any controls to avoid potential problems.
Check – Taking new measurements to compare with those taken prior to the change is an important step here. Analyze those results and take any corrective or preventative actions to ensure the desired results are being achieved.
Act – All the data from the change is analyzed by management teams to determine whether the change will become permanent or if further adjustments are needed. The act step feeds into the plan step since once a change has been fully implemented, it is time to begin looking for new ways to make further improvement.
There are a lot of benefits for any manufacturers to implement additional process improvement tools. There is a log of new technology available today from Kaizen, Advanced ERP reports, to Industry 4.0, Smart Factory, statistical analysis tools and more. ShopFloorPulse is one very good option (amongst many) you should take a closer look.
Our approach for continual improvement is very simple: provide visibility and synthesis of what actually happened on the factory floor, directly from objective machine data. The goal is to enable operators, shift supervisors, production managers, plant managers to:
Improve output with real-time monitoring of operator and machine performance
Reduce downtime costs by responding to issues faster, leveraging real-time machine notifications
Uncover performance improvement opportunities with data analysis
Prevent downtime with downtime root cause analysis
Provide supervisors tools to track effectiveness of performance improvement projects
You want to introduce these ideas in the most effective way possible. It would be a loss to pitch continual improvement program and have it shot down, makes it that much harder the next time.
We suggest you prepare by presenting continual improvement tools are like any other investment.
Any continual improvement tools are an investment of dollars, time and training for a potential outcome, and come with particular risks. As an initial step, just to gauge the level of interest we have prepared a short presentation that you can modify and adapt to your organization.
Download Presentation to Introduce Investment in Continual Improvement Tool.
In challenging times, the organization needs to reduce costs, be more efficient, increase throughput all at the same time. While investments in new machinery may be costly and risky, investments in the team and resources you’ve already made may have a better results.
The potential benefits of these tools can be significant, we hired production managers from Honda and Toyota who said to expect efficiency increases of 5% to 50% depending on the maturity of the organization. These tools offer a great return at low monthly operational cost.
All of us are in the midst of facing the COVID-19 crisis.
Notices about infection rates, and business closures are becoming the norm.
A large population of people who are sick or vulnerable or travellers who are in self-quarantine. I also think about the impact on businesses, employees, suppliers and customers alike. The biggest employer in this country is private businesses, individual owners with personal assets at stake trying to serve customers and pay their employees.
We are falling into a deep recession unlike anything before, and it is going to be tough. “Even during previous recessions,” noted Ellen Zentner, chief U.S. economist at Morgan Stanley, “no one’s been told you can’t go outside or you can’t gather.”1 It’s tough for individuals who have debts, contractors whose projects have ended abruptly, hourly employees who can’t get hours because businesses are closed, employees with sick parents, people struggling to make mortgage payments. The manufacturing industry, like many other industries, is going to be hit very hard.
This is an opportunity for you to treat your customers, suppliers and especially your colleagues with kindness.
The best advice I’ve heard is, let’s face it with kindness. This is an opportunity for you to treat your customers, suppliers and especially your colleagues with kindness. Everyone is under pressure; treat others with kindness, the easiest way is to work together, and help each other.
Here is how we’ve dealt with the COVID virus: From fairly early, when the news broke we
encouraged our employees to first take care of themselves and their loved ones
at home. Second, we are fortunate as a
technology company, we made arrangements for people to work from home. We enacted several policies from our disaster
recovery situations: employees are in touch with each other, employees have
secure access to files and applications over VPNs, and can run meetings
online. Lastly, we worked with key customers
and suppliers and informed them that we are able to continue service of our
applications and services.
Sincerely from the entire Esprida family,
Wishing for health and safety for you and those close to you.
Esprida provided a real-life walkthrough of Smart Factory / Industry 4.0 project from needs assessment / business case creation / solution design at Silicon Halton. In this talk, we discussed use cases scenarios for production managers, supervisors and operators, and show cased related software interfaces and hardware sensors used to retrofit onto old manufacturing equipment.
Esprida worked with a metal fabrication plant that had a large welding department. The company manufactured a variety of items such as Metal Trellis, brackets and fixtures, and as a result, most metal components needed to be welded together.
The challenge they had was that most of their welding machines were manual and productivity varied dramatically between welder to welder, and day-to-day. They wanted to put in a measurement system so operators could see how they were doing compared to the past, and supervisors could track progress without measuring productivity bothering operators.
The welders they had were very old, purchased in the mid ’80s and didn’t have any network connectivity or PLC controllers to extract data about the operator, part, contacts or number of welds. The welding machines were controlled by a foot pedal that lowered the electrodes until a connection was made.
Custom Industry 4.0 Welding Sensor
Esprida installed a custom sensor specifically designed for welding machines. The sensor is a digital counter that tracks the number of welds made. The sensor was tucked away inside the welding cabinet so it doesn’t interfere with operator functions in any way.
Operator Smart Factory Tablet
Each station was fitted with a tablet, it enabled operators
to lock / unlock the welding machines when they login
enter in a part number
enter in part quantity
enter work order number
This tablet also provided the operator a constant status on the number of welds per part, number of parts remaining and allowed them to give feedback when things slowed down (i.e. waiting for materials).
The supervisor dashboard was configured to show the status of all welding cells within a department. It quickly allowed the supervisor to see which operator was fast/slow, and ask why, and also see how many parts were remaining on an order and quickly estimate when the operator will be complete and ready to start a new task.
This project was a quick implementation for esprida, It took advantage of custom sensor technology capabilities we tend to use when trying to get Industry 4.0 automation on old machines and productivity.
The new measurement system and dashboard saved me 30 minutes a day, and increased welder productivity by 12%.
The system made welders and supervisors more aware of welds per part, how long it takes to complete a part, and also reduced the supervisor interactions with welders. The welders who were slower discovered that they were doing extra welds which were slowing them down. Reducing the number of welds made more consistent quality and performance between all welders. Initially, there was some resistance from welders but was better received when they realized the supervisor was interrupting the process less often, and they had an opportunity to explain (rather than being blamed) why things were slow.
About Silicon Halton:
We are a grassroots, industry-led, technology-focused community of Freelancers, Solopreneurs, Entrepreneurs, IT Professionals, and Students committed to technology, community, and growth. The IoT Peer2Peer is a forum for technology providers and adopters to share experiences and learn from each other. To learn more visit, Silicon Halton.
User-centered design practices are an effective method to design products. But who exactly are the users of your connected product? Usually, there are many more users of your product than what you typically think of in a first pass. This article provides a method to systematically identify all the users you need to think about when planning your product.
Let’s suppose you are building a predictive maintenance solution for a commercial refrigerator. The product is for operations managers in restaurant chains. The value proposition is to reduce the number of service calls by half. In this example, who are the users of your predictive maintenance solution?
chef (who controls and use the refridgerator)
restaurant manager (who is responsible for the restaurant)
director of operations (who purchases your product for many restaurants)
repair technician (who performs repair)
franchise operations (who does remote monitoring)
distributors (who sell your product)
engineering (who analyze your product data)
your support team(who support franchisees)
So where do you start?
First, think about all your users in three types. Customers: Users within your customer’s organization. For connected products, there are often direct users, manager users, purchaser users and more. Partners/Vendors: Users outside your company who help sell, install or maintain your product. Internal: Users are people within your company including operations, engineerings, sales etc.
The number of users often increases with the complexity of your product. We find the best guide is to identify all the users who are involved in every stage of the customer lifecycle.
User personas define the user archetype who will interact with your product. You should define their demographics, level of experience, technical knowledge, challenges and goals. This provides a perspective with which you can analyze each requirement.
The Internet of Things is coming fast (if it hasn’t already) and isn’t waiting for anyone. The challenge is when it will arrive and when should you release your IoT product.
Perfecting Products Faster
Traditional (physical) product design needs considerable cycle time to collect customer feedback, implement changes, and relaunch the product. It requires customer visits, taking time and resources. Then figuring out how to address those needs in your product takes even longer. The turn around time to release a refined manufactured product can take up to a year.
Connected products have radically shorter product development cycles for customer feedback, implementation, and updates. Connected Products provide a 24/7 window into your customers’ business. The information gathered clearly tells you what customers do with your product; how they use your product; and most importantly how they make money with your product.
Launching product updates is also much faster since connected products are updated remotely with over-the-air updates. We estimate remote updates enable you to release new versions of your product seven times faster than traditional companies.
So having a competitor hit the market with an IoT product one year before yours gives that competitor the advantages of a seven-year head start.
Bruce Sinclair author of IoT Inc.
Enterprises that fail to enter their market at the right time with an IoT offering will face strong headwinds, and stragglers will have difficulty to capture mind-share or market share. However, there is more to it than just a speed-to-market race. IoT changes how companies compete and in the process, changes the playing field in ways that are not obvious today. Connected products can change your value proposition and as a result, change the way sales teams, marketing teams and even finance teams think about your product, customers, and resellers.
As profit margins shrink and consumer expectations rise, its more important than ever to optimize for quality in your operation, and reduce rejection rates. Fortunately, new smart factory technologies help manufacturers work smarter without increasing labour costs or materials costs. Today manufacturing organizations and their leaders can easily embrace available IoT-enabled smart manufacturing solutions.
We share two manufacturer’s experiences on the impact of managing quality, reject rates, and rework; and four specific ways technology can help reduce reject rates. They explain why monitoring, managing and reducing reject rates was so important to them. It’s surprising how similar their concerns were even though their manufacturing processes couldn’t be more different.
High-Volume CPG Manufacturer (Scenario 1)
The first manufacturer produces consumer goods products, using a mix of manual and automated production lines for mixing, melting and packaging. The production team is large and includes full-time and part-time machine operators, as well as shift managers, production managers, and process control engineers. They operate multiple plants. Each plant has multiple high-speed lines that produce about 100 units per minute. The average retail price per product is approximately $20.
The second manufacturer produces pipes for oil pipelines, their manufacturing technology is based on metallurgy and coatings, their team is largely made up mostly of full-time staff, PhD’s in metallurgy and management team. The quality control processes typically take a few weeks to complete. The average price per unit, a pipe segment, is approximately $100,000.
Reject rate are a major problem in several scenarios:
Low salvage value The difference between the retail price and salvage value (selling products in secondary markets) for ‘manufacturing rejects’ is 20% of the full retail value. Its a huge drop in revenue only magnified by the size of the production run. Rework Depending on the type of error, the rejected products can be fixed and sold as a final product. This can save materials costs, but, the labour cost per unit goes through the roof while disrupting other production orders. In many cases, it’s cheaper to sell them as ‘manufacturing rejects’ in secondary markets than to spend the extra time to fix it. Finding errors late On a high-speed production line, like the consumer goods manufacturer at 100 units per minute; when there is an error during production and no one catches it for 30 minutes; that is $60,000 worth of scrap that has to be thrown away. On a slower production line, like the oil pipeline manufacturer at several weeks per unit; when there is an error during production and you find out at the end of the cycle, the impact is $100k for every bad unit in the run.
Production managers are searching for additional technology
to improve, beyond existing policies to ensure processes are followed, safety
specs are met, and sources of errors are minimized. Industry 4.0, Internet of
Things, Artificial Intelligence, Machine Learning, Predictive Analytics are popular
technology buzzwords, but they are looking for specific new IoT technology that
reduces rejection rates:
Esprida LiveIntersect Solutions to Reduce Reject Rates
Sensors to monitor bins
This solution is quick-win; an easy way to add IoT sensors within your manufacturing process. The high-speed line at the first manufacturer had quality control sensors, however, they don’t have any intelligence around the waste bin that reject units fall into.
The problem is that the waste bin needs to be checked manually during the production run. Adding IoT sensors to the waste bins automates the manual process. Connecting them to LiveIntersect automatically detects the number and rate of error units. When there is a sharp rise in error count, then the LiveIntersect system automatically: turns on audible alarms; turns on a red light on the production floor; sends notifications to the shift manager via overhead screens and their smartphone.
Machine data solutions to enforce quality control
It is common to have variances between the official and actual standard operating procedure (“SOP”)/recipe for a production run. The official SOP specifies machine parameters for RPM speed, temperature, time for every production run; however, on a day-to-day basis, we’ve seen operators use this a guideline, they feel products will be better by tweaking these parameters because of changes in humidity, input materials and other factors. These secret recipes are also a source of error, produce fluctuations in product quality, and increase reject rates. This is magnified when many operators are all doing things their own way.
Esprida LiveIntersect captures machine parameters directly from control panels and keeps track of production runs, records differences between results and parameters. Rather than asking the operators, yet again, to stick to official SOP after a failed run; you can show the data to operators that the production parameters are already optimized and shouldn’t be tinkered with. Data from LiveIntersect can also be used to increase quality and/or profitability for the entire plant. If you haven’t already optimized machine parameters, then the data from LiveIntersect can be used to optimize for production time, production speed, product quality, product consistency. Implementing this as the new standard will improve plant performance without major changes to costs.
Predictive Algorithms to Reduce Reject Rates
Your existing production data can be used to predict failures. The challenge is often having the skill set and time to: cross-reference data from your multiple systems (ERP, Inventory, CNC machines, Milling Machines, Quality) and apply AI algorithms to find predictive patterns.
The predictions are based on analyzing operators, machines, jobs, and program parameters. Knowing the likelihood of failure helps you have a plan of action: operators that need guidance for certain jobs; products that are more sensitive to machine speed, or temperature
Real-time algorithms to reduce reject rates
In scenarios when machine data is available in real-time, you can prevent errors during a production run. Like predictive algorithms, real-time data from CNC machines, and other production machines, is used to calculate real-time predictions. These predictions can trigger warning notifications on smartphones or overhead screens to help shift managers and production managers make adjustments and reduce the likelihood of scrap.
in manufacturing can access the latest innovations in IoT sensors, machine data
processing, artificial intelligence to increase margins, quality and more. This
article provides a glimpse into just one opportunity (reducing reject
rates) unlocked by the latest in smart manufacturing and IoT technology. Take
your first steps in learning more about the potential and downloading
this report. Use the abundance of data to unlock hidden insights.
This article was produced by Esprida Corporation. For more information on how smart IoT technology in your manufacturing facility, fill out this form.
The article will introduce how and why to build IoT solutions in Python using Zerynth and LiveIntersect IoT application enablement platform. Zerynth provides a software development suite that enables programming firmware in Python on 32-bit Microcontrollers and easy connectivity to LiveIntersect IoT Cloud.
During this article you will learn:
how microcontrollers are used in IoT and Industrial IoT applications (e.g. ESP32)
how to use Python to read data from analog and digital sensors attached to a microcontroller powered by Zerynth;
how to use Python to easily exchange data between an Espressif ESP32 board powered by Zerynth and LiveIntersect IoT cloud
We’re thrilled to announce the Zerynth support for LiveIntersect 8<insert link once published>, helping businesses unlock insights from device networks in just a few lines of Python.
Python on ESP32 in just a few clicks using Zerynth
Configure SSL Certificate [ or set ctx=none for non-ssl traffic ]
SSL certificate must match the LiveIntersect server provided in the configuration
Configure LiveIntersect Parameters
Edit the JSON file with the LiveIntersect parameters including:
baseUrl : address of LiveIntersect environment, typically https://sandbox.liveintersect.com
apiKey: apikey secret associated with the organization that you are using. (Note: API-Key included in the example config will not work in your environment. Please contact Esprida LiveIntersect to get the new API-key)
srNo: unique identifier for the asset
assetName: user friendly name for your asset
assetTypeCode: identifier for he asset type model of the asset.
register_asset() – registers assets or sub-assets to LiveIntersect cloud
get_asset_info() – downloads all asset configuration from the LiveIntersect cloud
post_metric() – sends sensor data, and telemetry information to the LiveIntersect cloud
post_attribute() – sends configuration data (such as communication frequency, model information or other parameters) to LiveIntersect cloud
Try it yourself
Get started today with Zerynth and LiveIntersect
Bring your microcontrollers to life with Python instead of C with Zerynth Studio and manage your devices at scale with the LiveIntersect integration.
When writing or planning use cases and requirements for connected products, you have to consider new scenarios that don’t apply to traditional products. We find that it’s these new scenarios that often get overlooked early on, and cause re-work later on during development.
The customer-lifecycle is a systematic approach that describes how customers will use your product from initial purchase to final retirement. This article describes the product lifecycle for IoT products which will make it easier to plan and prioritize the development of your product.