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.
The first thing that investors would look into, is do you have an A-Team to invest in. Specifically, he looks for a team that:
Experienced: Your team should be experienced in the market that you are operating in. You may not have experience in all aspects. However, you can augment your experience by working with advisors or partners to increase your experience level as well as doing more work in the corresponding area.
Gets the job done: Ideally the team has experience working together as a team and delivering results. This develops over time as the team works together. In an ideal scenario team members may have working history from a previous company.
Reads the market and adjusts: Changes in the market can be subtle and not obvious if you are in the market every day. Tip: use tools like the business model canvas that encapsulates the business environment. When you review your past assumptions periodically, they can help you identify and adapt to how your market may be changing.
Invests and learns from the investments made by them and others: As a CEO, you often work as fast as possible with your head down making progress. However its important to come up for air and learn from investments made by others in the same market that you are working for.
Philippe also analyzes the market, usually the market should be big, open to new entrants and amenable to economic rents. The market environment is an indicator of revenue potential.
When presenting your product to VCs there are a few aspects to communicate clearly. Philippe is looking for the product to lead in one or more of these four areas:
Is the product:
There are generally two product archetypes that VCs invest in. 70% of VCs focus on building a better mousetrap for an existing market; and 30% of VCs focus on a brave new world creating a new market that doesn’t exist. Wearables is a good example. Five years ago the introduction of fitbit was was a whole new market and provided high-risk and high-returns for their investors. This changed quickly over time after mass customer adoption and the market became established.
All companies have risks, the important thing is to know what they are and are they manageable? 90% of companies fail because of market risk. Has your product achieved product market fit? Philippe noted that 70% of his due diligence analyzes on market-related risks.
Market Validation Misconceptions
When you think about market, there is understanding the market that you play in, and market validation – two different things. Market validation plays a big role in IoT especially in consumer. For example: companies have raised capital from crowd funding sources is not evidence of product market fit. It does validate that your product works and is used by consumers; but the larger market rarely behaves the same was as early adopter customers. For commercial products having one major customer isn’t market validation either. You need to show that there is an engine to generate and handle 100s and 1000s of customers giving you a small amount of money, rather than one big customer spending millions. The problem with one customer, you focus all your resources on that customer, often to the exclusion of other the larger market.
Other types of risks. The other major types of risks are operational risks, and financial risks. Does the company have the infrastructure and systems to acquire and service customers. Does your company have enough financial capital to accelerate and grow.
Unfair advantage could be technology, funding, partner ecosystem. Often an overlooked area is working with larger firms. Big integrators are looking for leading companies to align themselves to stay at the forefront of the industry. They will be a force for you to be in market, and they have venture arms that you may be able to leverage. As an example Verizon ventures has been an accelerator of many IoT opportunities.
The bigger the competition, the more important the ecosystem is. For example if you’re competing against Microsoft, the more important that you have another big integrator (such as IBM) supporting you .
Geographic Markets Example:
In the chat arena, chat was dominated by very large players when Whatsapp and WeChat started. Whatsapp dominated in the European markets. WeChat dominated in the Chinese markets. They started as similar products but operating in totally different geography made VCs look at them very differently. They were able to grow within their respective markets and evole substantially.
Every quarter, Philippe has board meetings that reviews key criteria for market validation as well as operational risks, financial risks, and operational risks. The level of market validation and risk tolerance changes over time. At early stages you may not have any revenue and you are really focused on market validation. At later stages you may would focus more on revenue, operational and financial risks.
Investment Stages and Investment Size:
The terms for Seed round, Series A, Series B are fuzzy and often discussed based on the size or number of investment rounds. Philippe suggests a different approach: consider what stage you are at based on where you are in product development terms. What you have accomplished is more important the number of rounds of financing you have completed. This is how he looks at the growth/maturity stages:
Family and Friends round: You have an idea, but you don’t have a product or solution.
Seed round: You don’t have market validation, but you have a working product, and some early stage users that are providing usability feedback.
Series A round: By this stage you have 10M+ in revenue, and you have product market fit. Regular customers using your product and generating revenue.
Series B round: At this stage the company has product market fit and you are figuring out the best way to monetize and grow.
Mezzanine round: at this stage is all about execution and growth.
This article is just a primer, but there are many more resources you can tap into to learn more about this space.
Connected products have been very successful, new products such as freezers, airplane engines, coffee machines, to agriculture sensors are doing well. Many product managers are figuring out how to capitalize on the opportunity, however, a major challenge is to forecast the revenue potential. There clearly seems to be a market for internet-connected products, but at what price point? What kind of pricing structure is feasible for my users?
This article will help you think through: ‘how much can you charge for your connected product idea’ and ‘what kinds of pricing models should you consider’. This is based on over a decade of experience (Esprida IoT) helping companies launch connected solutions and support remotely controlled devices.
Connected Products Overview
Connected Products or Smart Products refer to common physical products that have been enhanced with Internet of Things technology (including hardware, communications, and software systems) that are connected to the cloud. A comparison: a traditional coffee maker will make a cup of coffee at the press of a button. A ‘smart’ coffee maker automatically makes coffee when you wake up (based on your smartwatch); it also introduces new revenue streams to re-order coffee beans, it can send you promotions for new coffee brands, it offers’ high-end calibration features on a smartphone to change brew times or coffee temperature. https://www.nespresso.com/ca/en/prodigio-machines-range
Present day coffee machines- Your phone can now make a perfect coffee. The first Bluetooth connected Nespresso machine and its app offers additional benefits such as capsule stock management, schedule brew, machine assistance and care.
A new pricing model for any product is a huge change that needs to be thought through carefully. When you are changing a product pricing model, you have to think through:
Will pricing model be accepted by my sales team?
Will pricing model accommodate commissions for resellers, distributors and partners?
Will existing customers accept the new pricing model?
Should I focus on early adopter customers or main stream customers?
How do I forecast a recurring revenue stream?
When thinking about pricing a traditional non-connected product, companies typically base decisions based on the price of a product, unit quantities and unit margin. When thinking about the new connected products with recurring pricing models, you have to think about the lifetime spend of your customer – not only an individual transaction. It changes the sales & marketing focus towards building a long-term customer relationship and monetizing over the lifetime also known as the Lifetime Customer Value. This is an important concept to consider while thinking about pricing your IoT product. The right approach is to figure out how to create and capture value over the users’ lifetime use of the product or the product family.
Just by adding connectivity, your product can create value in new ways that was never possible before but before we go over pricing structures, it’s important to understand what value can be created;
Many smart products allow consumers to control machines from their smart phones. A smart door bell provides enhanced ability see who is at the door and unlock your door without getting off your couch. Consumers are willing to pay $240 for that convenience compared to a traditional doorbell.
Real time information can cause changes to human behavior, improving your bottom line. For example: a dashboard that monitors machine throughput have been shown to change the behavior of machine operators. When machine operators go on-break, or run the machine at a slower setting, they don’t have to wait for a supervisor to notice, the entire team will be notified via the public dashboard that turns yellow or red.
Many connected product concepts enable the owner to control equipment in a much more granular way and enables them to make their processes a lot more efficient. For example, a freezer manufacturer provides restaurant chains to lock/ unlock the freezer door if the freezer hasn’t been working (i.e. no power) for 6 hours; and control freezer temperature/ humidity/ compressor settings. This control means there is less training, better audit controls, automatic process notifications based on a malfunction. The temperature control also helps them balance energy costs with food and freshness.
Some consumers (e.g. tech enthusiasts) want to have the latest technology. It’s important for them to maximize optimization in anything they do. One example that’s worked well for this group is the latest Series 3 $500 + smart watch from Apple.
Knowing what’s happening in remote locations is extremely important in many service oriented businesses. Suppliers of food stock, fuel, construction material, service parts for remote facilities can quickly save money just by knowing if a service truck roll is needed urgently or if a service truck roll can wait until the next time that a service tech is in the area. The cost to operate a service technician, parts and fuel is expensive and the ability to optimize trips based on better information has a very quick ROI.
Monitoring allows your customers to track a product’s operating characteristics and history and better understand how the product is used. This has important implications to assist with:
· Design of new products
· Market segmentation (based on usage patterns)
Risk is an important attribute that has a huge impact on cost, customer satisfaction, brand recognition and public perception. Many commercial IoT products enable real-time risk monitoring to measure things like driver safety, environmental safety, homes and commercial building safety, and preventive maintenance for products.
After sales service
Based on monitoring data published by smart devices, you can ensure that you can dispatch the right technician, with the right parts, improving first time fix rate. With predictive analytics you have the ability to identify and resolve issues before they occur. Monitoring data can also reveal warranty compliance issues.
Follow up sales opportunities
Product utilization data can be used to create additional sales opportunities.
For example: a check scanner product that needs to be cleaned after every 10,000 documents can inform sales team that a cleaning kit needs to be purchased. For example: a photo printer uses paper and ink as consumables, the printer can automatically subscribe to paper and ink subscription so that they never run out of consumables. (see HP ink subscription)
The machine data can provide an accurate and consistent record of the operating conditions of equipment eliminating the need to manually record data.
For example: Food safety regulations require that fridges and freezers are maintained at a set temperature and the temperature is recorded. Across a large retail chain, training staff to record the temperature every hour introduces training and compliance costs. A more effective way is to have a connected-thermometer that automatically records fridge temperature.
Smart, connected products can be programmed with algorithms to automatically take actions, eliminating the need for supervision activities.
Note: All these values may not exist in the first version of your project.
This section describes some of the pricing models you should be thinking about for your new product concept. There are many nuance implications on pricing and how its positioned. Ultimately, customers need to clearly understand what they are paying for; this means there is a strong relationship between the value creation models above and the corresponding models described below.
IoT products can be sold as traditional product with one time price. For example, the Google Nest Thermostat is sold with a one-time upfront price with no recurring fees.
Upfront + recurring price
IoT products can be sold with an upfront price (covers initial hardware costs) and recurring fees (covers ongoing infrastructure costs). This is similar to a new cell phone plan. For example, the Google Nest Cam has an upfront price for the camera, but has an on-going recurring fee to save data.
IoT products can be sold with no upfront fees at all, and only rely on recurring revenue. This approach is well suited for scenarios where the upfront hardware costs are relatively low and the buyer behavior prefers operational expenses over capital expenses. For example, a warehouse RFID tracking system may charge per node per month with no initial upfront costs.
IoT products can be sold based on the cost of consumables only. This is similar to the razor-blade model where the upfront hardware is free, but every use has a cost. For example, a medical laser for skin rejuvenation costs about $50,000 to $80,000 per year – a significant cost that deterred labs from purchasing many machines, and attracted copy-cat clones. They created a version that had no upfront fees, but charged $25 laser cap required for each customer use for the life of the machine. A clinic with 10 appointments a day, 5 days a week would generate about $65,000 annually.
Usage based pricing
Usage based pricing is based on the runtime of the service. For example: Rolls Royce airplane engines now charge per hour of use rather than an upfront capital purchase. They also provide a service and maintenance plan; so that airlines simply have to pay for using the equipment and not for the maintenance or upkeep of the equipment.
Multiple product price points based on one hardware platform
IoT products can have gold/silver/bronze versions of a product completely controlled with its software. This streamlines product development, manufacturing and support work because they are supporting one set of hardware. For example: John Deere manufacturers a single tractor engine, and offers three versions by differentiating key features like pulling power, horse power, top speed, torque. Each version is remotely upgraded via a software update.
With over the air updates, businesses can continuously improve their products, add new features and, introduce subscription models and charge for remote support and repair.
Connected Support Program
Many heavy industrial products have a service and support contract that may require onsite service, diagnostics and repair. These tend to be expensive contracts.
With remote management offered by IoT, it enables the maintenance model to move from corrective model to preventative maintenance to predictive maintenance to prescriptive maintenance.
For example, the price of an elevator and repair services doesn’t change; however, the elevator can be sold with an additional remote support program. The remote support typically operates at a higher profit margin because most of the work is done using computer automation rather than manpower.
Field Service Program
For long-lived products, the after-sale service represents a significant revenue. With IoT in Field service, the manufacturers can reduce the number of field technicians and truck rolls.
Many field service programs are based on responding to customers reporting issues into a help desk. A connected product can significantly reduce the costs of field service based to identify problems before customers complain, fix issues remotely, diagnose issues and improve first time fix rates. Many companies have found the ROI so significant, that they implement a connected remote support program at no charge because the internal benefits to support operations are huge.
Alerts and auto healing capabilities along with over the air updates, reduces overall warranty and service costs. It is a significant fundamental shift from reactive maintenance, to proactive service and support that can be sold as a separate product on an ongoing basis.
If you are creating a new IoT connected product, then several of these revenue models may apply to you. The revenue structure may also be adapted by customer segment, enterprise accounts, small-medium accounts, consumers, resellers etc.
For a more in-depth discussion on how to create a revenue structure for your product concept, you can request a free consultation firstname.lastname@example.org