As we start to move into the next phase of mitigating the COVID-19 pandemic, factories are going to open up again. However, hesitation is likely, especially given the number of outbreaks that have happened in meatpacking plants. The factory floor is not an easy place to practice social distancing and most factories are not designed to be operated remotely. Let’s look at how real-time contact tracing can help.
OSHA has released a number of guidelines to help factories navigate the emergency. They include flexible work hours, barriers between workstations, and wearing masks. But the fact remains that outbreaks in factories are going to be likely for a while, and these outbreaks have led to plants being shut down for days while they are deep cleaned and sanitized, which is a considerable cost. It can cost hundreds of thousands of dollars a day to shut down and sanitize the building.
The best solution to this, in addition to promoting personal hygiene and following guidelines, is to track potential outbreaks early and isolate affected workers. "Contact tracing" has become a buzzword, but it is key to ensuring that a plant does not experience a major outbreak.
Contact tracing is a process that activates when you have a confirmed case of COVID (ideally lab-confirmed, but presumed cases should be considered as testing is not always available). The process involves tracking down everyone who has had close contact (within six feet for more than a couple of minutes) with the patient over the two weeks before the confirmation of the case. Those people can then be tested and/or isolated. This allows you to catch and isolate potential carriers even if they are asymptomatic or presymptomatic. With a disease that produces a high percentage of asymptomatic carriers, contact tracing is essentially the only way to avoid further extended shutdowns and balance the economy and public health.
Contact tracing slows an outbreak by removing potential sources of infection (or establishing that they are not infected through a negative lab test). Additionally, it can improve worker confidence by assuring them that infections will be tracked and that they will not be sent home without cause. This can help make employees more willing to come back to work, although it should be combined with transparency about procedures and good hygiene. It can also make visitors more comfortable about entering the building and interacting with employees, although social distancing should be maintained where possible.
Without contact tracing, it is not possible to keep a fast-moving, highly infectious virus under control, and it is simply not possible to safely and efficiently reopen plants. Nobody wants their factory to be on the news as the center of another outbreak.
Traditionally, contact tracing is done by interviewing the patient and then calling all of their contacts. This, of course, relies on human memory, which is often fallible. It is also a highly labor-intensive process. Human resources have to track down every employee who worked with the patient, and obviously you can't count on people being where they are supposed to be at all times. Did they use the break room near their work station or did they trek somewhere else because it was out of coffee? Did they stop to chat with a coworker on their break? While micromanaging employee location tends to reduce morale, tracking it in this situation is absolutely vital from both a safety and a business perspective.
It also requires a level of agility that your staff may not have. It has to be done quickly and it is not always reliable. Before you know it, you're having to send everyone home and shut down the plant for days. For small offices and workshops, manual contact tracing can work well, where everyone knows everyone and you have a relatively small number of employees and locations to deal with. For large factories, it is potentially cost-prohibitive.
Thankfully, there are ways to support contact tracing that reduce the amount of labor needed and increase speed. Modern technology can help by tracking the location of employees. You may already have systems in place to do this for purposes of productivity, making sure everyone evacuates in an emergency, accurate timekeeping, etc. Employee tracking can support many things, including in-building contact tracing.
Businesses should also be willing to be generous on paid sick time not only for people who are infected but for those in quarantine, including employees quarantined because of exposure elsewhere or exposure of a family member from whom they are unable to isolate themselves. Employees should be properly incentivized to report infection or exposure that happens off the clock and should never be penalized for doing so.
Real-time tracking supports contact tracing by keeping track of the location of employees at all times. This means that you know the real (as opposed to planned) location of employees, and have an accurate record that does not depend on human memory. Badge-based tracking systems can track visitors as well as employees, and visitors are asked to provide their contact information so they can be reached in the event of an alert.
The tracking system records when people are within six feet of each other, and for how long. It does not take into account the use of masks and other protective gear, which may reduce the risk of infection. The layout needs to be programmed into the system, so it does not flag as close contact when people are on opposite sides of a wall or significant barrier.
If you are already using Bluetooth for employee and asset tracking, you can use that infrastructure and merely need to add software that helps assess risk and send appropriate alerts. Location-based solutions can give a graduated assessment of risk, allowing employees and supervisors to make meaningful decisions and reduce the cost of unnecessary testing or absenteeism.
Otherwise, contact tracing can also use existing Wi-Fi services, although Bluetooth tends to be more accurate. If you have been considering implementing real-time tracking now is a good time. The system can be integrated with asset tracking to help reduce losses, spot production bottlenecks, and improve procedures. This means that the infrastructure remains in place for other purposes and can easily be reactivated if there is a later wave of COVID or an outbreak of a different infectious disease. It can even be kept in place to help track more common threats, such as norovirus or Legionnaire's disease, in a way that manual contact tracing cannot be, because of the higher expense. By reducing occupational exposure, it will continue to reduce absenteeism in the future.
When an employee or visitor reports that they have tested positive for COVID-19, the system will check their contacts for the previous 14 days, and assess the amount of risk experienced by those contacts.
The system then informs supervisors so that exposed workers can be sent home. Potentially, as testing becomes cheaper and easier, on-site testing could be deployed to ensure that employees are tested before they leave the office. Visitors can be notified so they can make their own informed decisions.
By notifying workers at high risk of exposure, the system reduces downtime, reduces the number of employees who have to be sent home, and saves money on testing. It can also be used to track locations where the employee spent a lot of time so that extra cleaning can be done as needed, particularly of high-touch areas.
The user will receive reports that support compliance and also help them keep track of places they have been. This can also be passed on to supervisors so that they can address risky behavior such as lingering in break rooms in close quarters. It can be used to enforce temporary closures of high-risk common areas such as kitchens, or assess actual use so that sanitation procedures can be optimized (which will remain useful long after the pandemic).
The BLE tags used in the badges are affordable enough that a lost badge or one not returned by a visitor is not a major cost. Badges can easily be distributed and collected and visitor badges can easily be sanitized or, alternatively, a phone app can potentially be used to reduce social contact further. Badges can also be used to facilitate contactless access, which also helps with infection control.
Kontakt.io has put together the COVID-19 Contact Tracer Webapp, which works with a Bluetooth-based employee, visitor badges, that provides a report of people who are likely to have been exposed. This can then be used to determine who needs to be tested or isolated. The system is designed to control spread, reduce unnecessary testing, and decrease employee and visitor anxiety. It can also be used to improve sanitation and decontamination plans by tracking actual usage of high-touch areas. Our app provides compliance reports, shows locations on a layout so that actual contact can be determined, and ranks exposure in a graduated way for proper risk assessment. Contact us to find out more and schedule a demo.
Among the brand names associated with the tools and machines that help warehouses run smoothly, Linde is among the most recognized. As a manufacturer of forklifts, pallet trucks and other tools used for transporting inventory and processing orders, Linde has built a global presence, with operations in over 100 countries and several regional branches. Now part of the KION Group, the world’s second-largest manufacturer of industrial trucks and one of the leading suppliers of automation solutions for intralogistics, Linde has a deep understanding of the needs of the warehousing industry.
A subsidiary of Linde, called Willenbrock, specializes in selling and handling customized forklifts—around 2,700 a year—that are adapted to the requirements of different work environments. The customization involves the addition of various elements and attachments suited to the needs of warehousing and industrial workspaces.
The very nature of delivering individually customized forklifts, or any other machinery, presents its own logistical and organizational challenges. Unique orders mean unique specifications, each with different parts, staff and processes involved. At the scale of Linde Willenbrock’s operations—more than fifty fully-assembled custom forklifts a week—issues arising from such a fragmented process are magnified and inefficiencies can have real costs.
That’s exactly the lesson that Linde Willenbrock learned as they began to encounter problems related to operating under a paper-based system, where the entire order process was documented manually. Search times for parts and a lack of transparency into production and supply chains became an issue. Operational efficiency began to suffer and gains in throughput proved to be more difficult to achieve. Add to this the fact that Linde Willenbock’s work is spread over two facilities, in Hannover and Bremen, and the need to digitize workflows became evident.
“While we were collecting data online, our physical operations were still non-digitized and we weren’t making data-first decisions,” says Ulrike Meyer of Linde Willenbrock. “This lack of transparency in intralogistics, assembly and supply chain processes meant that we were losing money.”
Click here to watch our ‘From Discovery to ROI: Linde Willenbrock Case Study’ webinar >
Simon AI was just the solution Linde Willenbrock needed.
With digital tracking of physical processes, handled by Simon AI - Kontakt.io’s Location IoT Analytics Platform application, insights into order statuses and item locations helped to transform Willenbrock’s operations. Search times have been slashed as staff can now instantly locate the parts they need but the most impressive impact has focused around the way that orders are now processed.
Previously, the order processing team received the order and then informed the logistics team about the components that would be needed to fulfill it. Then, once the components arrived, the assembly process started with a worker going to find the forklift to be customized, which could take up to half an hour without real-time location capabilities. Instead, each forklift had to be checked and compared to the order form—maybe it was the first one checked, maybe the last. When the right forklift was found, it was time to repeat the process, only this time with the components needed for customization.
Now, with Simon AI, everything is monitored and tracked in real time. All stakeholders in the process are notified of new orders instantly, and the location feature directs workers straight to the forklift they need and the parts that will be added to it. No more searching through the entire inventory, looking for a particular piece, no more wasted time and no unnecessary delays in getting orders started.
I wasn’t very familiar with such solutions before this project, but after using Simon AI, I found it very intuitive to use and helpful for my job. It’s much better than old fashioned paper-based solutions. I can now create and track the orders from anywhere and quickly locate the items, which helps me speed up my work. It is exciting for us to get this new technology.
-- Daniel Luckyman, Team Lead Order Processing
Using Simon, Linde Willenbrock can track the status of any particular order, see what stage it’s in and what’s left to do and use the analytics to better understand workflows and identify issues before they have the potential to delay production. Dealing with various problems is part of any industrial or manufacturing process but now, with Simon AI, they can be dealt with and corrected at an early stage before they begin to create bottlenecks that threaten output and on-time delivery.
Kontakt.io’s Simon AI has tremendous potential to change our and our customer’s business operations. It’s having a direct impact on our costs and, even more importantly, it allows us to deliver the best possible customer service.
-- Ulrike Meyer, CIDO
Want to learn more about how Simon AI location analytics can improve your decision making and transform your industry? Contact us.
We talk a lot about the hardware and software involved in gathering and applying location data but in this post we’re going to take a step back to look at the big picture behind Real-Time Location Systems (RTLS). The whole point of using the tools that make RTLS work is to collect data about location, movement and processes but how is that information used to achieve better business results?
The location data collected by RTLS a means to an end. The end, obviously, is the ability to identify useful business insights with the help of that data. Pairing location information with gaps in business performance creates the foundation for new approaches that reduce inefficiencies and improve business outcomes.
Different verticals have found a number of innovative and beneficial ways to leverage location data gathered via RTLS. Digitized processes have enabled managers and strategists to gain unprecedented insights into how people, objects and materials move in physical spaces and identify opportunities for optimization.
Given its complex nature, it should come as no surprise that industry and manufacturing has been the setting for a very long list of location data applications. From the moment raw materials come in one door to finished goods being shipped from another and everything in between, the industrial space can benefit in many ways from the streamlining made possible through the visibility provided by location data.
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Here are just some of the applications of locations data in industry and manufacturing:
Manufacturing and industrial facilities need to register and track materials at different stages of the process. Hand-held scanners, requiring manual scanning and extra manpower, have traditionally filled this role but now real-time location data is helping to automate the process. RTLS is now a key part of supply-chain management right from the very start of the manufacturing process.
Location data helps to save time and reduce staff levels by processing the intake of new materials and automatically adding them to inventory. Other processes and activities can proceed without having to wait for slower manual methods to catch up. Also, material inputs can be more quickly moved to warehouse shelves or the production line instead of gathering in one area and perhaps interfering with other work going on. Also, when onward movement is accelerated, downtime for associated equipment like forklifts and pallets is reduced.
The proper allocation of factory inputs is a component of material handling. In complex spaces with so many moving parts and potential issues that can slow down production, it’s important that materials be accessible when and where they’re needed. With location data gained from passive monitoring, materials can be more easily assigned to workstations and assembly points, keeping them better supplied, avoiding downtime and facilitating resupplies when needed.
Probably the application most often associated with location data, being able to instantly locate assets has obvious business benefits. Avoiding or reducing downtime is the first one on the list, with less need for staff to spend time locating tools and equipment needed for a particular job. Also, expensive equipment can be protected from theft by using geofencing to ensure that it is kept within virtual boundaries that you set. If tagged tools or equipment are taken beyond those lines, an automatic alert can be sent to the appropriate manager.
Asset tracking also has value at inventory time (or at any time you want to take inventory). With the touch of a button, you can count the number of all assets or a particular tool or piece of equipment.
Here we mean the useful aspects of being able to digitally tracking employee locations. This visibility is commonly used in several ways in industrial and manufacturing environments.
We can start where the staff starts their time in the facility—at the time clock. RTLS has become a new kind of digital timeclock for millions of employees around the world, precisely documenting their arrival and departure times. Apart from creating a reliable a searchable database of information (which can be used to document employee presence and absences as needed) it also eliminates human error in the clock-in, clock-out process. No more forgetting about time cards when all employees have to do is walk past a sensor with a beacon card or tag on them.
Tracking staff locations also has applications for safety and security issues. Access to potentially hazardous areas in work environments can be regulated through granting or restricting it as appropriate. Staff movements can also be archived for documentation purposes in the event of an accident or other issue requiring visibility into who was where and when.
The same principle can be applied to security concerns in industrial facilities with stored valuables, expensive machinery or other similarly sensitive assets that are not generally accessible to all. RTLS can be used to control access to these areas in order to ensure that only authorized personnel can gain entrance.
The worldwide network of cargo ships, trucks, rail cars and freight airliners is the backbone of global commerce. An industry that handles 80% of all consumer goods and 90% of global trade by weight is certainly large enough to create any number of opportunities of location data to create new efficiencies and savings.
With the advances in hardware battery life and the development of tags and beacons able to withstand more demanding environments, RTLS-based location data is now as mobile as those ships, planes, trucks and trains. Automated processes similar to those found in other verticals are now streamlining processes in supply chains and reducing administration costs (which can constitute 20% of the overall costs of shipping).
Real-time location data informs computer applications that schedule loading and unloading operations, accelerating distribution at ports and cutting overall downtime. This contributes greatly to the reduction and sometimes elimination of manual tracking and human-error while boosting productivity and efficiency.
When it comes to tracking cargo, location data can go far beyond simply placing it on a map. Real-time updates can trigger alerts when shipments are behind schedule, which can in turn trigger automated updates sent to distributors, customers and other stakeholders, allowing them to plan accordingly and reallocate their assets in the meantime if possible. From a customer service perspective, this is a valuable tool not only for preserving relationships but for avoiding costly returns and claims.
Sensor-enabled tags can collect information about environmental conditions during shipment, including temperature, vibration and moisture/humidity, documenting when and if goods were damaged en route. This is important for chain of custody issues, cold chains and any number of sensitive goods. The documentation this data provides can help to settle customer disputes and protect stakeholders in the event of losses during shipment. RTLS provides opportunities to create event-triggered alerts and the tracking of environmental metrics that help to guarantee that goods in motion reach their destination in the proper condition and, when they don’t, to identify the link in the chain where the problem occurred.
When certain goods can’t get wet, too warm or cold, or have to be handled with care, sensor-enabled location data systems can provide the information you need to learn where things went wrong.
End-to-end solutions based on location data and bringing higher container ROI, easy and reliable goods documentation reducing port times in an industry where that can easily save $100,000 an hour.
With large facilities full of expensive equipment, sensitive areas and many people in need of assistance, the Healthcare industry is tailor-made for the many applications of location data.
Given the size of many healthcare facilities and the sometimes remote areas when staff sometimes find themselves alone with patients, the ability to remotely call for assistance has obvious appeal. Unfortunately, the threat of injury, whether accidental in the line of job duties or as a result of aggressive patient behavior, is a real concern in this field. Whether in hospitals, nursing homes or residential facilities, having to deal with volatile situations alone is a serious professional hazzard. When these situations occur, a tech solution is needed to call help fast.
RTLS can address this need with button-enabled tags and sensors that let staff easily send such an alert. Whether it’s a dangerous situation with a patient or other emergency circumstances that require additional help, assistance is literally a button push away. There’s no need to waste valuable time returning through long hallways or face some threat alone when staff can be equipped with this “panic button” at all times. Peace of mind has real value for medical staff when they know they don’t have to face these threats alone.
Like their counterparts in manufacturing, doctors, nurses and other specialists in healthcare use sophisticated machinery that often moves away from where it’s needed. That equipment can be quickly and easily found in the maze of hospital halls and rooms when it’s tagged as part of an RTLS deployment. This reduces time wasted searching for it—in an environment where every second often counts—and increases the quality of patient care. Due to its value, such equipment can also be geofenced into a defined area, triggering an automated alert when it is taken beyond those boundaries and safeguarding
Location data in schools? Absolutely! In fact, educational settings often take advantage of all the applications listed above, just slightly adapted to their unique needs.
Schools and universities have valuable assets too and location data lets them find, manage and inventory them just like a factory, transit company or hospital would. Schools obviously have plenty of students, too, and their attendance can be accurately recorded in the same way as the workers in a manufacturing facility. There may be circumstances where an academic institution wants to restrict student access to a particular area or grant it only to members of a certain age or grade. This too can be done, just as it is in different industrial or other contexts. In very large areas of schools that are used intermittently, they may want to control heating or energy costs by monitoring which rooms are used and ensuring that heating and lighting are managed accordingly. Again, just as in shipping and transport, sensor-enabled tags and beacons can record environmental conditions and generate data that can be leveraged into better ways of organizing schedules and people flows to optimize the use of resources.
If even schools can get so many uses out of location data, then surely there must be something in your own business that can benefit from RTLS!
If want to discuss how real-time location data and analytics can improve your business, we’re ready to talk.
The shifting sands of the technological landscape force occasional changes in the places where lines that mark one field from another are drawn. Those lines get blurred, walls get knocked down and evolution is often in the direction of convergence as differences slowly disappear.
Information Technology (IT) and Operational Technology (OT) have traditionally been considered to be two distinct fields but the advent of connected devices has brought them close enough that at times it can be hard to tell where one ends and the other begins. The data-driven computing of IT and OT’s focus on management of physical assets are no longer surrounded and separated by clear boundaries. And whereas until recently they existed largely independently of each other, they are now integrated in a deepening relationship that is redefining industries.
In fact, success in IIoT is now predicated on deep integration between IT and OT. The remote control, monitoring and safety benefits of IIoT solutions need industrial environments where sensors, machinery and networks are seamlessly integrated. IIoT is driven by a constant digital conversation between two halves of a working whole.
This represents a fundamental change from the status quo from just a few years ago. Communication has always been an integral part of IT, not so much for OT. The input of massive oceans of data gathered from OT has become the basis of a symbiotic effect that pushes both of them forward—the benefits of information produced by OT has focused efforts to improve the computing speed and capacity of IT, which is then able to better handle and leverage data from OT, which makes innovation in IT even more rewarding, which then increases the utility of OT inputs, which then….well, you get the idea. Back and forth we go, ever faster, and we’re reaching new heights every day.
The intertwining of IT and OT has implications for many personnel roles in these fields. While we’re unlikely to see coders suddenly working with machinery or database administrators fine-tuning some delicate instrument on the factory floor, those in leadership positions, like CIOs, increasingly need to consider the ways that physical and virtual assets work together and how they can influence business strategy. Leveraging new sources of operational data depends on IT know-how and aligning the missions of everyone involved is a management challenge.
As the roles of the people in an organization change, so do the organizations themselves. IT and OT leaders need to recognize the value of cooperation in advancing and leveraging convergence. Making this a priority may require a top-down push and company-wide support. This can’t happen without cultural and organizational transformation since companies can’t expect IT & OT to converge if there aren’t structures, processes and the right environment for such convergence. The path forward is difficult if the road isn’t paved first.
This change in approach needs to include everyone, right up to the very top. Just as CIOs need to have increased input into business priorities and plans, the role of the COO should also lean more towards driving innovation and transforming processes through the use of technology and the data those processes generate. But getting from plan to results takes more than good intentions. Among the many interesting takeaways from an insightful report from Deloitte is how rarely leadership for digital strategy and execution is clearly defined.
This is a problem. Instead of ad hoc arrangements that just respond to the latest crisis, a solid plan with clear areas of ownership needs to be in place and senior leadership has to assign it to the express lane. This new IT & OT hybrid may be new and in a dynamic state of growth, but the business need to establish a framework for managing it remains.
We believe that the convergence of IT & OT should be promoted and nurtured beyond the technological, security and process levels. It needs to become a deep, business-critical integration on the organizational level. Stakeholders on all sides need a seat at the table to have a voice in building the framework for this deeper integration and avoid things like disastrous downtimes caused by a lack of communication (as described well in this report from Cisco).
The digital and physical assets of a business can’t be viewed as separate, unconnected components of the same operation anymore. Anyone waiting for those lines in the sand that I mentioned at the beginning to re-form and stay put before they act is going to be left behind by those who can understand and adapt to a changing scene. The IT & OT transformation is much more than an interesting side note to larger industry trends, it’s fundamental to any company looking for the way forward to a digitally transformed future.
If you’re looking for a software suite to enable IT & OT convergence in your organization, check out Simon, our operational experience platform. Schedule a demo today and find out how it can help you improve efficiency and safety with AI and IoT.
Industrial spaces are being relentlessly transformed by Industry 4.0 / Industrial IoT (IIoT), and a whole new world of innovation is still to come. Making manufacturing and related facilities “smart” by connecting people, machines, and assets and enabling communication between them has already brought about unprecedented automation and optimization of workflows. This hyper-efficiency is behind the entire concept of “lean” operations as applied to a number of fields and, given that these ideas reached critical mass just a few short years ago, it all makes you wonder what we can look forward to just over the horizon.
But the driving forces behind this rapid progress are powered by an integral but often overlooked component that helps to deal with the incredible amount of raw data generated by connected devices and identify the most useful elements. Separating the signal from the noise is made infinitely easier by algorithms based on artificial intelligence (AI). It’s thanks to AI that data gets processed not only faster, but smarter and in a way that can deliver relevant and measurable business results.
Without some mechanism for sorting through massive amounts of information, IIoT would quickly drown in its own data. And it’s not just a matter of the volume of data points, which is quickly approaching the limits of our ability to comprehend large numbers, but the kind of information that’s being gathered. Industry 4.0 relies heavily on data generated from a place where it was previously unavailable—on the factory floor.
When every production asset is generating data, it becomes both a blessing and a burden. The blessing part is easy to see when equipment, workflows and processes can be optimized based on analytics informed by machine-level insights. Inefficiencies don’t have anywhere to hide when you can shine a light on the individual components of anything.
The burden side of huge amounts of data is equally obvious. What do you do with all those numbers? How can you process and analyze them fast enough and draw conclusions before the next wave starts flooding the decision-making process?
Edge computing has become a partial solution to this problem. The idea behind it is that not every single data point is relevant or needed, so you reduce the data generated on the edges by sensors to the most important ones. After all, you don’t need to know what’s happening to your machine every second, right? Obviously you want to know when a machine requires your attention but otherwise it’s best to just let it do what it does.
And that sounds like a good option when there are 125 billion devices connected to a network. Anything that reduces the need for human intervention is going to scale up to massive benefits on a network that big. Just think of a single factory with, say, 500 workers on site and hundreds of assets, all of them generating a constant stream of data. Even with edge computing, it’s still a lot to deal with. How do you use it all to improve and grow without spending hours on organizing, analyzing and discussing it? How do you use the data to make business-critical decisions when there’s so much of it and just.won’t.stop.coming.in?
This wave of numbers from the micro level of production processes would easily overwhelm attempts to understand it without some kind of filter for separating the meaningful from the meaningless. This is where artificial intelligence comes in.
Artificial intelligence, or machine intelligence, is essentially the field of computer applications that allows devices to mimic human intelligence by making decisions that lead to various outcomes. With the right inputs and direction, machines can learn and solve problems at a rate and volume that computing power takes far beyond human capabilities.
This is the ideal solution for situations like the data overload created by IIoT. Machines can wade through oceans of information and determine what’s worth keeping and what is simply distracting bits of nothing much better and faster than we ever could. With the power of AI, data gathered through IIoT can be leveraged into any number of beneficial applications that are manifested in use cases all over the business world.
Artificial intelligence is invaluable in workflow optimization processes. AI spots patterns and identifies inefficiencies, for example showing you weak spots in your floor design, routes, processes, and more. Similarly, it can boost inventory optimization, analyzing your materials, supplies, and work-in-progress goods to give you recommendations on reductions, purchases, and material flows.
But AI capabilities go beyond what isn’t working now. Algorithms also enable you to look into the future and see what is likely to stop working soon, or even further, prevent it from happening. A good application of this superpower is in predicting downtimes. AI analyzes the performance and availability of your resources and shows you current and future roadblocks that may cease the production if things keep progressing as they do. A similar use case is predictive maintenance, a term traditionally reserved for equipment. Here, AI predicts a potential failure of a machine and proactively schedules maintenance, saving you the time and burden of doing it on your own.
Finally, AI helps with forecasting and production planning, showing you how likely you’re to ship the order on time (or what to do if you’re currently not), when will be the best time to replenish materials, what will be the margin on the order you’re processing, and so on.
If AI sounds like the Holy Grail for a continuous improvement manager, well... it is. All of these AI applications not only keep your production running smoothly, they also help you achieve your best possible performance with the most efficient use of your resources. This means better productivity, reduced labor costs, lower inventory, and, consequently, better profitability.
The best part?
All these insights are proactive. Powered by AI, you don’t have to ask for the data and spend hours analyzing reports and spreadsheets just to spot a flaw that happened a week ago. The data comes in you actually need it so you can solve issues as they arise and rest assured because you know that as algorithms learn and improve, there’ll be less and less to fix.
If you’re interested in what else IoT can do for manufacturing facilities, take a look at our white paper on how it can help achieve truly lean operations and solve 2020 challenges. You can download the white paper here.
Modern strategies for asset management have undergone an accelerated evolution in the last two decades. Although you can still find those white stickers with barcodes on every piece of furniture in many offices (what is this, 2005?), more sophisticated solutions are delivering more and better business benefits than ever before.
The limitations of traditional asset management systems like barcodes or passive RFID have made them inefficient compared to the advanced tools available today. Current options not only reflect the capabilities made possible by advances in technology, but also increased business needs and the growing complexity of processes. Asset management has grown from basic ways to measure inventory to an integral part of the optimization of any operation.
Asset management solutions available today often leverage RTLS (Real-time Location Systems). They are fully automated, seamless, transparent and supply analytics that directly boost performance and overall efficiency in addition to their conventional role as a way to know where movable goods are located. Human input has been reduced to a minimum, bringing increased accuracy while freeing up employees to contribute to more productive aspects of the company’s operations. Deploying asset management solutions is also faster and easier than ever, requiring less support to set up and expertise to run.
The bottom line is that the tools that help you monitor your bottom line are better, easier to use and more accessible than they were just a few years ago. But what exactly are you getting with next-generation asset management software?
Let’s list a few of the biggest reasons why factories, warehouses, production lines and other facilities are making the move to more advanced asset management software. We’ll start with the obvious benefits that come with the ability to track physical assets in real time, but taken a step further. For example, wouldn’t it be useful to be alerted when a particularly costly asset was idle for a certain amount of time? How about a notification when something is taken outside of a predefined area?
The list doesn’t stop there. Modern asset management software supplies useful data that can be applied to various aspects of any operation:
- Monitor the status of environmental conditions around certain assets
- Know instantly when assets are ready to be reallocated or redeployed
- Use historical data to calculate costs of use and down time
- Leverage access to vital information about the location and status of movable assets to work smarter, plan better, and make changes in resource allocation as needed in real time
This is way beyond just knowing what you have, if it’s available and who’s using it (although those are vital fundamentals of any asset tracking strategy).
Automated asset tracking is not only highly accurate, but it also relieves employees of the obligation to log equipment in or out, letting them get on with more productive uses of their time. It also reduces the need for physical counts for inventory purposes, saving time and money.
Can you afford to not know where costly assets are at all times or if they’re being properly utilized?
The applications of RTLS for asset tracking can be adapted to optimize any physical environment or business context. This customizable functionality creates a wide variety of ways that asset management software can improve workflows and refine processes.
With detailed information about movements, patterns and use times, it’s easy to better coordinate, for example, supply chains, loading bays, the management of inventory/stock levels and essentially anything that involves moving parts. There’s no need to rely on guesswork or trial and error when you can use hard data to get all the pieces in just the right places.
Bottlenecks that don’t stand out to the naked eye get exposed when movements are digitized. Asset management software can send out alerts when predefined limits are reached (like when an area is over-occupied) or when tools, inventory or supplies are moved beyond a certain point or misplaced.
The improvements made possible by optimized asset management produce immediate and measurable ROI. Both COGS and CAPEX are moved in the right direction when inputs and assets move under the best possible conditions. When you can attach a cost to every stage of the processes in your operation, it’s easier to focus on cutting the fat and replicating what’s already lean.
Our recently-introduced Simon AI combines all the most useful and money-saving features of asset management capabilities. It offers plug-and-play deployment so you don’t need a dedicated team of tech experts to get started. The software that drives it consumerized, making IT/OT convergence simple and avoiding the need for coding. It’s also easy to integrate third party devices and access points into the system, meaning you can keep existing infrastructures or use hardware of your choice.
Simon does more than just track assets. It plugs into all your physical workflows and uses AI-driven recommendations helping you improve productivity and safety across your operations. Check it out and schedule a demo to discover ways it can help you understand and adapt your workflows.
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There’s an interesting but often overlooked difference in the way the costs of different inputs in the manufacturing process are measured.
On one hand, there’s the cost of parts, materials and other physical resources. The price of each of them is clear, right down to the piece, pound, meter or whatever unit of measure applies. Whoever’s running the plant or production line knows exactly how much it costs to buy another bag, barrel or box and how many units of output they can turn that cost into. The cost per job or order can be calculated precisely when it’s time to box everything up and deliver—it’s just simple math.
But that kind of precision is rarely demanded for the other, often larger, cost input in manufacturing. Labor costing remains more art than science for too many operations. Here, the calculations rarely go beyond anything more sophisticated than multiplying the hourly labor rate by the number of hours worked and adding things like payroll taxes and various benefits. There’s nothing about how much time was actually spent on a particular job, any downtime that may have occurred or anything apart from the fact that a given worker was physically present in a facility for eight hours.
And it’s on the basis of this model—full of estimates, guesswork and assumptions—that crucial decisions about margins, pricing policies and profitability are made.
Doesn’t something seem a bit off here?
The conventional system for measuring labor inputs is an old-school time clock. Punching in and out has been the default method for registering work time for over a century. But when you really think about it, time clocks are only good for telling you when a given worker started and finished a shift—there’s nothing about who did a certain job, what exactly what they did and where the work was done.
When you dive deeper, you can see more drawbacks of relying of time clocks for labor costing. All of them contribute to the challenge of accurate labor job costing, which is critical to calculating margins per order and, by extension, setting reasonable pricing policies.
Time clocks and the cards they stamp are inaccurate, reflecting when employees punched in and out and not necessarily when they started and stopped working. Employees often forget to record when they started or finished a shift. Time cards don’t reflect when someone left a workstation or for how long. They don’t tell you anything about how much time was spent, for example, looking for tools, on breaks or simply idle for whatever reason.
This lack of insight makes cost optimization impossible, since time cards don’t show activities that generate costs but should be minimized or eliminated. In short, time cards really only tell employers how much their labor costs are but not exactly what they’re paying for.
This just isn’t good enough for the demands of today’s business landscape.
IoT solutions have brought cost transparency to various aspects of manufacturing by digitizing workflows and creating visual representations of processes, employee movements, bottlenecks and other areas that were previously difficult to optimize. Now, with the tracking and analytical capabilities of IoT applications, it’s possible to get accurate insights into not only how much time employees spend on the factory floor, but exactly how that time is spent. That means costs can be assigned to specific segments of any process, thus enabling true labor costing structures that better inform larger business decisions and strategies.
Where time cards simply show what time they were punched and not when an employee began or finished working, IoT solutions detect precisely when workers entered or exited specific areas in a facility. There is no need to remember to clock in or out since that obligation is lifted from employees and their presence is recorded automatically.
With IoT, inactive time can be detected and the cause of the inactivity identified. Inefficient distribution of production inputs or physical workflows, awkward spatial arrangement of plant resources, bottlenecks in any process—they can all be more easily recognized when employees can be tracked at scale. This is something even the best and most attentive management team cannot achieve with simple observation alone.
Being able to monitor all movements in real time means gaining a smooth, automated and transparent view of what’s happening on the factory floor. It’s also error and cheat-proof, providing a reliable record of who was where and when. It’s all saved in the application as well, so historical comparisons can be made and improvements tracked.
IoT solutions make job costing easy and transparent while also highlighting areas for improvement. So not only can labor inputs be measured as precisely as material inputs in manufacturing, but efficiencies can be gained by ironing out small wrinkles in the process that are often invisible to the naked eye.
And again, on top of the obvious business benefits of cost transparency and optimized processes, accurate job costing provides a better foundation for crucial decisions about pricing policies and preserving necessary margins. A clear picture of what each job costs, alerts to opportunities to boost productivity and the elimination of estimates when it comes to setting your prices—what else do you need?
If you’re among the study group of the LEA Global research, you have reasons to be excited about 2019 and beyond. According to the report, 48% of manufacturers expect their international sales to increase in 2019. This bright future, however, doesn’t come without obstacles. The same paper also lists the three top barriers to growth; talent shortages, competition, and profitability.
This basically means that this year, the top concern of manufacturing companies is being able to meet the demand, offer good pricing and lead times, and maintain high margins, all at the same time. How do they do it? By reducing waste and maximizing productivity. In other words, by becoming lean.
The concept of lean manufacturing isn’t new but it’s still (or maybe more than ever) applicable to today’s manufacturing environments, making it very tempting for operations executives. Being lean, therefore, means maintaining or increasing the output while keeping the resources at the same—or even lower—levels. This doesn’t exclude hiring more workers or buying new equipment, as long as existing resources are fully utilized and the new addition is there to create direct value.
Where manufacturing firms struggle with inefficient workflows, downtimes, excess work-in-progress items or inventory, and wasted time, lean methodology aims to redesign all processes and resources to focus solely on what creates value. The benefits? Higher margins, smoother management, happier customers. Everything that a manufacturing company of 2019 needs.
Although lean manufacturing is about smoothing out your operations, the path to lean is not always without issues. The challenges start right at the beginning, with the question of how to identify inefficiencies.
In order to improve something, you have to know what needs improving. This requires observation and accurate measurement. You have to ask yourself questions like:
What is your profitability per order? What affects your capacity and how long does each process take to complete? What is the ratio of value-added vs. non value-added time of your workers? What exactly causes the unproductive time? Is the machine used at its full capacity?
Are you able to provide quantifiable answers? Many companies may have anecdotal insights but these are usually biased and always inexact. Others use clock-in systems which are more accurate but prone to error and missing a lot of data on what’s really happening during each job and between different jobs or stations.
This means that in the majority of cases, operations executives are left with guesswork, which is rarely the path to good conclusions. And it only gets worse as you progress on your own lean path. How will you know if you’re improving or not if there’s no data you can base your assessment upon?
Accurate data is key to helping you make decisions that take into consideration all parts of your facilities along with their cross-dependencies. Only then can you evaluate whether your lean efforts are successful.
At this point, it’s only fair to ask where all this accurate data is supposed to come from.
The answer is simple—from technology that digitizes all your workers, assets, and workflows. In other words, the Internet of Things (IoT).
Think about it this way: you can’t follow each worker, forklift, and pallet with a stopwatch and notepad to track their activities. But if the worker, forklift, and pallet are digital, and each of their activities creates a digital footprint, not only can you track them individually; you can track them and their counterparts at scale and over time.
Click — and you see what the worker is doing. Click — you see what he was doing last week. Click — now you compare him against his shift colleagues. Click — you check the completion status of the order. Click — you get a report on all downtimes this week and analyze what caused them. Click — you get the point.
That’s what IoT is about. It means creating a visual representation of your facility and all its workers, machines, equipment, and other assets so you can analyze them with any level of granularity or generality that you need.
IoT enables you to generate data on asset location, flow, and productivity, and tie it into your workflows. Because the process of data collection is fully automatic, IoT gives you unprecedented visibility into your operations without creating any additional reporting burden for your workers or their supervisors. Whenever you want to see how a worker, shift, or the entire facility is performing—or how they’ve performed in the past—you open your IoT application and all of the data is there: accurate, clean, and often visualized in the form of spaghetti diagrams, dashboards, or heat maps.
Can you implement the principles of lean manufacturing without Internet of Things? You definitely can. After all, the philosophy was inspired by the operations of Toyota from the IoT-less eighties.
But it’s not without reason that the philosophy of lean manufacturing is experiencing a renaissance right now. The rise of the Internet of Things and other digital transformation technologies take the initial concept to a whole new level, providing operational transparency Toyota couldn’t have dreamed of. IoT-enabled lean is then about leveraging data to make the right decisions, improving faster, and making fewer mistakes along the way (or being able to quickly adjust).
This perfectly addresses the industry-wide “need for speed”. As the KPMG 2019 Global Manufacturing Report states, nearly two-thirds of manufacturing CEOs say that “acting with agility is <<the new currency of business; if we’re too slow, we will be bankrupt>>”.
If you haven’t gone lean yet, now’s the perfect time.
Our increasingly global economy is driving a hard bargain: innovate or eat your competition’s dust. Risk and opportunity are plentiful in this world and no one is absolved from being outperformed or outmaneuvered. But like most significant changes, it isn’t happening overnight. Pressure is being applied in varying amounts and in different ways leaving companies with enough time and space for choosing their next move. But in manufacturing, competition is fierce and time is running short.
Production systems, often the differentiator in creating a sustainable competitive advantage in manufacturing, have driven a great many companies to adopt best practices like lean manufacturing, six sigma and so on. But it hasn’t stopped there.
The Internet of Things (IoT), the convergence of the physical and digital worlds, has introduced unparalleled opportunities to capitalize on new business models and operational capabilities. It is the disrupter of disrupters and gradually transforming the way industries like manufacturing do business for good.
If you haven’t already started working with IoT or are unsure how it can be deployed in your business, don’t worry. We have advocated the low-hanging fruit approach to IoT before and will do it again (sometimes you gotta stick to what works).
In manufacturing, production scheduling is a low-hanging fruit for IoT and a great place to start your IoT journey. We’ll dig deeper and demonstrate why that is and the steps you can take to begin upgrading your operations before time. runs. out.
Orchestrating a fast and efficient production line requires many moving parts working in unison, synchronized to the beat of orders completed on time and error-free.
While top tier manufacturers are known to follow stringent quality and efficiency standards, seeking higher OEE, fewer losses, and faster changeovers, small and medium-sized businesses are generally less advanced and operate with fewer technological resources and sophistication.
Regardless of the company, in today’s world you are responsible for showing improvement quarter after quarter, year after year. Often in the form of greater efficiency.
Production scheduling is one of the key differentiators in this battle for efficiency.
As a part of manufacturing planning, production scheduling organizes what a company plans to produce and by when.
With variables impacting this constantly changing, MRP/ERP software has become a basic resource planning tool necessary for keeping everything organized. The only problem is that while resource planning tools are great, they are only as good as the data that’s fed into them. They are not enough.
Which is where IoT comes in.
IoT provides what was previously too complex or costly to capture - more data.
Production scheduling is one of the areas where more data can deliver clear and measurable results with more data. More data can empower internal managers with actionable insights to improve their operations in real-time. That’s not just cool, it’s an industry game-changer.
What kind of data do you want that you don’t have already? What kind of data do you even need? These may be difficult questions to answer and part of integrating your business with IoT is figuring these out.
The good news is that there is no limit to the number of data points you can gather and analyze.
Low-cost robust sensors are becoming ubiquitous which means everything from operating conditions of machines, environmental conditions of the facility, precision timing and location-based tracking have all simultaneously become available.
So what can IoT do for you, really?
A robust IoT solution built for industry, like our own Simon AI, is designed to take this data and use it to solve common operational challenges you face on a daily basis.
Let’s assume that you’re a production line manager.
An average day can see many different challenges arise that impact your efficiency in production planning and job scheduling. Someone calls in sick. Tools are misplaced and searched for longer than usual. Unexpected downtime occurs. A range of issues arise that require effective management to work through.
But some are not as obvious as others.
In fact, many areas of improvement are only possible to know by a thorough examination of disparate (and at times seemingly random) data points using powerful analytical tools present in an IoT solution.
The benefits of such an IoT solution are numerous, including:
Time after time we’ve been asked this question and realized that for medium-sized businesses, there was no ideal solution available. So we built it ourselves. Simon AI is an end-to-end IoT solution made for the industrial world to solve the most common operational challenges they’re up against today - production scheduling among them.
We know the kind of financial and operational challenges SMBs endure.
We’ve been working closely with them through location-based solutions for almost a decade. That’s why we built the first IoT solution specifically for SMBs that extracts the value-add of IoT without the heavy overhead and development cycles.
Remember how we said a great start to IoT is to find the low-hanging fruit?
Simon AI was built to capture the low-hanging fruit that IoT provides without the cost and complexity of traditional IoT solutions. We cover that in greater detail here.
Production scheduling can be a hassle when you have to constantly balance and estimate the resources and time required to complete different jobs. With Simon AI, we’ve made it possible to know what is occurring moment to moment by equipping machines and workers with sensors. Then, we can cross reference this data with the production schedule to understand where action is needed faster and more effective than ever before.
New to Simon AI? Learn more about how it can work for your business by requesting a demo here and reading more about how we’re bringing IoT to SMBs around the industrial world below.
Walking into work this morning you greet your colleagues, log in to your computer and check your inbox. You see an email from the COO, your boss. Always challenging you to make improvements to the line’s efficiency and judging by last month’s poor performance, your heart begins to race as you open to read what's inside.
Last month, your assembly line missed the delivery date on 4 separates orders, some incurring penalties of up to $2,000 per day in late fees. This recurring issue of missed deadlines and lack of transparency is starting to escalate, you can see. And you begin to wonder whether your own abilities as an effective manager are being called into question.
“So what are you going to do to ensure we don’t miss a single deadline this month??” the mail reads.
“What am I going to do?” you mutter, astonished. “As if this is all my team’s fault?” You begin typing your mail, outlining your argument that defends your team and shifts most of the blame on to the sales order process. You do bring yourself to admit that at least some of it has to do with your workflow and order processing, but that’s not the real issue, you point out. The sales guys, they’re the ones who submit their orders last minute and then pressure us to deliver on these tight deadlines.
Frustrated, you finish typing your mail. But just before you hit send, you pause: “What if this isn’t the right approach this time?” “Should I be offering ways of actually improving this?” “How could we solve this if we really wanted to?”
Operational managers in factories, warehouses, healthcare facilities and beyond experience the pressure to perform regularly. Increase productivity. Reduce downtime. Deliver on time. etc.
It’s a tough job. And an important one. The operations manager has a critical role in seeing that goods and services are provided on time, within budget, and according to the right specifications. They need to be proactive. When this doesn’t happen, penalties can incur, relationships can be tested, and the blame game can lead to a lack of trust across departments. Things can get messy.
How do you expect performance to improve when you’re working with the same limited information as you were the last time you tried to make a change?
The list of problems and potential solutions might seem countless. Let’s start simple by taking a step back and identifying the issues at the root of our problems.
If you take a look at the problem of labor productivity and forecasting, is the issue that your workers are too slow and easily make mistakes? Or is it that the sales guys submit orders with mistakes that require jobs to be delayed and reworked? Maybe a lack of transparency between sales and operations is making it impossible to align expectations. How can you know for sure?
In order to answer these questions with any certainty, we need to have relevant information to base our decisions on.
For business operations, relevant information comes from collecting data across everyday operational workflows.
Gathering data relies on technology. Right now, the way industry-leading companies are gathering data from operational workflows and using it to make decisions is through a process commonly referred to as the Internet of Things, or Industrial Internet of Things when referring specifically to the industrial sector (IoT and IIoT, respectively).
By equipping physical assets or workers with IoT devices, data on their environment and location can be known, analyzed, and cross-referenced with other sources of information like ERP systems, cloud services, etc. Which means that virtually any action, movement, or process can be used to optimize any business operation. True operational efficiency becomes a reality.
The data you need varies from business problem to company. In our example, the information this operations manager should consider collecting includes:
Data becomes useful when it can be used to make decisions. Knowing which questions to ask is the start. If, for example, you want to understand how productive your workers are, then you might measure how much time they spend in certain zones or with certain parts or materials related to a job.
Collecting data can be done by equipping both the worker and the materials with an IoT device, like a Bluetooth Low Energy tag, to easily measure the precise amount of time it takes to complete a job from start to finish and how long each worker spends on their respective tasks.
Today, the technology and cost barrier to entry for such solutions has dropped dramatically as solutions adapted specifically for small and medium-sized businesses have become available.
Out-of-the-box ready IoT solutions, built to solve common problems in workflow operations, means that you can launch a solution without breaking your budget or the need for a team of engineers and IT professionals to customize it to fit your business. Everything required to track, analyze, and make changes that improve your department’s performance is standard and comes included.
Walking into work this morning you greet your colleagues, log in to your computer and open yesterday’s productivity report while you sip your coffee.
Your factory is “connected”: machines, tools and workers are equipped with devices that transmit data about operating conditions, performance and location. That data is analyzed in real-time to bring near-complete transparency to your business operations. You haven’t missed a deadline since implementation. You love it. And, importantly, your bosses love it.
Now you can identify inefficiencies and make improvements on the fly. Your workflow schedule, including real-time information on each job, is now available to operations and sales, facilitating transparency across your organization. It’s no longer a guessing game or constant back and forth, it’s your superpower.
In only six months, you’ve found problems that have plagued your company for years and implemented solutions to them. You’ve saved your company tens thousands of dollars while proposing new areas of innovation and set a new internal and industry standard leading the way in the digital era. That’s IoT.
This is the transformation all industries are undergoing. While the industrial, healthcare and retail sectors lead, others closely follow. Though the same cannot be said for all companies in those industries.
As part of a proactive management team in warehousing, you’ll have heard it before: “Labor productivity must go up.” “We need more qualified workers.” “Innovate!”
These challenges are complex. The answers to them may seem even more so. What can one company or even one manager do about them? How can your company stay competitive in a hyper-competitive, global economy?
It starts by finding the low-hanging fruit.
In an industry where labor productivity is declining, talent shortage is rising, and accidents and fatal injuries are at a high, your warehouse needs to rise to the challenge, snatch the low-hanging fruit, and enter the digital age on your terms.
And for small and medium-sized businesses, that means easy to use, cost effective and focused on the business outcomes. That’s precisely what you can do, with Simon AI.
The average worker injury costs as much as $38,000 in direct costs and up to $150,000 in indirect costs. For small and medium-sized companies, that adds up fast. In fact, it’s damaging in numerous ways from your company’s pocketbook, to its reputation, and let’s not forget the actual person suffering the injury.
That’s what makes safety a key area for innovation in your company and an ideal launch point for furthering your company’s digital strategy.
Not only could it save lives, safety and security lay the groundwork for solving other common business challenges along the way. That’s the power of location and environmental data. That’s why we start here.
So much data. So little time. At this point, you might be wondering, “why do I need data? where does data come from and how can it help solve business problems?” I thought you’d never ask.
Data is generated by sensors often embedded in hardware like BLE beacons and tags. Once collected, robust analytics scan data sets for patterns. Depending on the use case, these patterns form predictive models that help your business better-predict conditions about the future.
Imagine that one of your workers slips, falls, and injures themselves while walking between the loading dock and inventory. You rush to their aide and proper medical attention is provided.
Important questions immediately need answering, like:
Until recently, to answer these questions meant relying on firsthand or even anecdotal data about the cause and course of action moving forward. Then came IoT. Using data from location and environmental sensors, data could be analyzed to reveal other aspects of the situation that were maybe not accounted for, like that humidity levels were above 80% - the same conditions of the previous two slips as well. It turns out the floor becomes slippery when humid. Mystery solved! From then on you realize the importance of controlling the humidity levels as it leads to fewer falls. Everyone wins.
Unfortunately, traditional IoT solutions such as these were built for large companies with large budgets who can absorb the high failure rate that has accompanied (and hindered) the Internet of Things for years. It’s been a complex and expensive ride for early-adopters.
For SMBs, IoT has been like being stuck between a technological rock and a financial hard place. There seemed to be no way around it.
Then we created Simon AI: to provide industry managers with a solution that streamlines the added value of an expensive IoT platform into one, end-to-end IoT analytics suite built especially for small and medium-sized businesses.
It works because we gather data on location and environmental conditions adapted specifically to answer the pressing questions a proactive warehouse manager needs to know when an accident happens:
On top of that, it automates the tracking and reporting of security incidents (and other common business problems) over time, so as a manager, you don’t need to fill out lengthy paperwork and reports. Rather than relying on firsthand accounts to understand operational problems and create change, data from BLE tags worn by the worker or attached to a forklift can be referenced in real-time to identify patterns that predict and prevent similar accidents from occuring in the future.
Data is the key to knowing these answers and providing managers with the insights they need to solve business problems in real-time.
Kontakt.io is the only company capable of providing a truly end to end IoT solution, built for SMBs, to solve problems of security and a host of other common business problems.
While it may seem an unlikely candidate for technological innovation, safety in warehousing the perfect low-hanging fruit to apply technological innovation. In a digital world, data is the key to identifying and improving business operations giving companies who use it wisely a clear advantage over their competitors.
And the good news is that you can get started as early as today.
Learn more about the value of location-based IoT data in our recent white paper.
Remember when it used to take scores of engineers, months of development, and tens of thousands of dollars to implement an IoT solution?
Of course you do! Because it’s a headache that companies are still dealing with today. Which has made IoT a technological and financial leap of faith - especially for small and medium-sized businesses.
At Kontakt.io, we’re proud to announce that we’re changing this by simplifying the way IoT solutions are bought, distributed and deployed, with Simon AI.
We’re bringing SMBs and agile business teams like you a comprehensive solution for competing in a rapidly changing digital world in an easy to use, plug and play format. No development or IT experience required.
Today, a major shift is underway. For over six years, Kontakt.io has been delivering premium BLE hardware and infrastructure for small and large businesses alike. We’ve learned a lot over that time. Mainly, that IoT is really complex. And no product has been capable of isolating the value-added of IoT at a time and cost scale adapted to small and medium sized businesses.
That’s why we built Simon AI.
A truly end-to-end IoT product, Simon AI isn’t another IoT platform. It’s a workflow-centric analytics suite that delivers SMBs with a flexible, easy to use solution made to solve the most common and costly use cases facing industry today.
We’ve leveraged our know-how as a world class IoT company to create something many others can’t: IoT for all.
Ok maybe not IoT for all - but for many. Simon AI is currently optimized for operational environments like manufacturing and warehousing. Healthcare and commercial buildings are also largely supported. It’s a flexible product that can be adapted to a range of industries and use cases.
Many industries struggle with the same problems, like tracking equipment and business processes, improving their storage and inventory management, and better-securing the workplace environment. We know this, because our beacon hardware is used in most indoor location tracking and environmental condition monitoring cases today.
But not all companies are competing with the same resources. Simon AI is a game-changer for small and medium-sized companies who couldn’t justify IoT adoption because they simply couldn’t afford to fail. Now, you don’t need to worry about failing. Simon AI is a tried and tested plug and play solution suitable for use in nearly any indoor industrial environment.
So how can Simon AI help your business? If you’re like Rob, a proactive warehouse manager, then you know that reducing operating costs is a major KPI to your warehouse’s performance. Non-value adding activities need to be kept to a minimum as every minute counts.
Many of the most common factors that contribute to worker efficiency, a key variable in operating costs, can be defined by location.
How is warehouse space optimized? Where do workers spend most of their time? Where are common areas of congestion? Or accidents?
Simon AI was built for location-based IoT use cases.
By bringing together data from sensors and workflow management technologies, Simon can quickly identify inefficiencies in everyday business operations and deliver warehouse managers, like Rob, with actionable insights.
Simon AI component overview
- Sensors (IoT): Environmental sensors like temperature, humidity, and accelerometer
- Location (RTLS): Locating where someone or something is in a defined area
- Workflows: Activities related to value-adding operations
Other common challenges Simon AI addresses are incident reporting, tracking precise worker and material workflows in a production line, or knowing who is where on the production floor at any given time.
All of these location-based use cases are now easily managed through one platform.
And the best part is that Simon AI is end to end. Why is that important?
Because end to end means simplification. It means you work with one company for the entirety of the solution. We deliver you with everything you need to benefit from next-generation operational excellence without the need to bring in multiple stakeholders.
In this way, Simon AI is the first solution of its kind and the simplest way for SMBs to begin building a winning operational and digital strategy.
Long has the search for a simplified IoT solution that delivers on both ease of use and cost been going on.
As CEO Philipp von Gilsa pointed out, “Simplifying IoT means bringing down costs, speeding up installations, and delivering location and sensor insights to actual stakeholders, bypassing IT and data departments.”
It takes a company with deep expertise and solid partnerships to bring a unified solution like this to market. At Kontakt.io, we’re confident that Simon AI will help small and medium-sized businesses to compete, bring their businesses online, and strive for greater operational efficiency and security.
Have a project in mind? Schedule a demo to discuss your business case and learn more about pricing plans.
