Integrated Big Data-as-a-Service (BDaaS): A new opportunity for B2B

Manoj K Nair

Big data as a technology passed through various stages of evolution during the last few years, which still keeps it hot in the list of tech buzzwords! Starting with handling the 3 V’s of data – Volume (of data to be handled), Velocity (of data generated) and Variety (of data generated), it has spread wings to more V’s – Veracity (to ensure data integrity and reliability), Vulnerability (to address privacy and confidentiality concerns) and Value (of information)!

As Google showed the way, collection and collation of huge volumes of data and applying the right analytics to gain valuable insights into the business and optimization possibilities is the key to extracting the full potential of the data-driven industry. Today Chief Data Officers are building strategies to organize their data and to derive business intelligence from it to drive radical transformation of businesses in many sectors such as industrial, retail, logistics, healthcare etc.

BDaaS (Big Data-as-a-Service) is gaining momentum, enabling external experts to take the company’s customer data to the cloud and to provide analytical insights for decision making. Offered as a managed service, it frees up the customer from substantial initial investment and helps offer RoI-driven spending. This article focusses on BDaaS, describing the potential that enables our customers to conceptualize and launch new business models.

Large corporations with structured and centralized ERP systems wouldn’t benefit as much from BDaaS as compared to unorganized sectors comprising of diverse players each with their own fragmented IT infrastructures. For instance, unorganized retail is a heterogeneous sector with a geographically distributed supply chain that spans across medium and small players, having considerable differences in their levels of process maturity. Stand-alone islands of software application are encountered many times and so are ad-hoc (or legacy) structures of data storage and archival. B2B companies providing services to geographically spread out customers in many traditional supply chains like chemicals/reagents for laboratory use, petrochemical (non-fuel) derivatives and medical drugs could benefit from the transformational potential of BDaaS.

Suppose you are a B2B player in one of these or similar sectors, let us take a closer look at your business and customer data! Could your expertise in the industry be leveraged to identify a new data-driven model by “integrating your customer data” to offer new intelligence gleaned from it? This integration gives you the data in ‘sector level’ rather than ‘individual’ customer level. You will be able to identify sector level intelligence and provide it to all your customers which will be mutually beneficial for all.

In order to accomplish this outcome, you will most often need external expertise in big data to work collaboratively with you (or your domain consultants) in order to build a BDaaS platform to offer your customers. The value of business intelligence that the platform brings helps them win in their businesses and their patronage in turn helps your business model succeed.

So has been our experience working with a world leader in the pharmacy supply chain across North America. Besides supplying medicines and medical equipment to their customers, they also provide inventory and patient management software to their customers. The software installed in each of the numerous hospitals gathers transactional data over time. We worked closely with the customer’s consultants on the feasibility of data integration and created a centralized control center using big data technologies such as Spark and Kafka. Hosted on the cloud, the platform captures streaming data from different hospitals and pushes them to the centralized system that offers a metered BDaaS service to end-customers, the analytics insights helping them to optimize their businesses.

The path to big data implementation, however, was filled with several challenges, a few of which are:

Data security

With the regulatory requirements concerning medical information like the HIPPA standards, compliance is mandatory. Only non-sensitive data at a lower level of granularity is collected, that respects privacy concerns of the individual hospitals of exposure of their patients’ sensitive information. This is the key factor to the success of the project both from customer buy-in and regulatory compliance points of view. The collected data is pushed to cloud securely with transport layer security.

Verity of data

The data being heterogeneous and scattered is the foremost challenge while implementing big data solutions. Even though most hospitals use our customer’s software a few others use their own legacy software. Data could be isolated even across the departments in the same organization! We built data collector modules which can be customized easily to collect data from various sources and push it to the cloud. Rationalizing the relevant data fields from these diversified sources and integrating it provides a lot of insight into possibilities of analytics.

Time to market and initial investment

Being a metered service we had to make sure that customer’s cost is kept linear with usage. Databricks big data platform with reliable Open Source Kafka data injector gives us a balanced and scalable framework to meet this objective.

After data was made available from all sources centrally for analysis it was discovered that information on the availability of particular medicines in each hospital along with demand predictability has the potential to reduce the associated transportation costs by around 20%. Data-driven drill down revealed for instance that for a particular area with a prevalence of influenza but with shortage of the corresponding medicine, the system can identify the best possible area (nearest, where there is enough stock but no demand currently) from which this medicine can be arranged. Supply chain demand mitigation by coordinating drug supply between customers can significantly save inventory and transportation cost for customers. More importantly, it saves precious reaction time for their end-users, which would not have been possible without the magic of BDaaS.

In your own strategy to connect your fragmented customer data centrally to provide mutually beneficial information, the role of an experienced big data partner is indeed crucial. Combine the power of your domain expertise with big data specialists to create new data-driven business models which besides increasing your revenues could make you the hub to all customers thereby increasing the bonding of existing ones and attracting new ones.

Maintenance Management & IIOT

Aju Kuriakose

Aju Kuriakose

IoT is changing the world around us. This change is affecting every walk of life including the maintenance industry. Maintenance management used to depend on skills of the maintenance managers to troubleshoot skills and was least data-driven as they have very limited data to fall back on when it came to machine health. However, it is rapidly changing. It is becoming heavily data-driven than skill driven. Advances in wireless communications and data processing enable maintenance managers to gauge the health of the factory in an instant.

We can tell that its no longer a hype but a reality and proof is in the fact that the leading organization- OPC Foundation is spending time in developing the Unified Architecture (UA) Specification for IIoT in the manufacturing environment. The standard is being developed to enable IIOT devices to easily pass information between sensors, machines, monitoring devices and the cloud in a secure and open way. Also OPC, AMT & OMAC have jointly developed  Packaging Machine Language (PackML) and MTConnect which combines OPC UA with existing industry standards to lower cost of predictive maintenance.

Low cost of IIOT sensors is making predicting failure or measuring remaining useful life (RUL)  of a tool a no-brainer enabling maximum uptime at optimum costs . As an example, a drill over course of its functioning will start to suffer wear. As we continue using them regularly at some point of time they become unusable either because the precision of the job falls below the parameter or the drill bit breaks off.  With the combination of Industrial IoT sensors and AI techniques today we can easily predict the remaining useful life of the tool .

Any maintenance professional will agree with me that predictive maintenance is a journey they have to take but IIOT makes the journey easier. Retrofitting existing machine with a sensor to measure machine health becomes very easy. One of the companies where we work with to enable this transitions is OPA By Design. It is a smart device which can be tagged to any existing machine at a very minimal cost to measure 8 different parameters and report it maintenance supervisors via mobile app & cloud. Since the machine is constantly being monitored, any sign in degradation in the health of the machine is alerted instantly

IIOT is also enabling to drive down the inventory holding cost as now maintenance supervisors have better predictability of machine failure and hence they have to stock less spares. It also results in fewer emergency inventory orders and less downtime due to out-of-stock inventory.

IIOT is not changing anything for the maintenance professional except the fact that he can now listen to his assets and make informed decisions based on actual data on the health of the asset. IIOT is not going to fix the problem for him. He will still have to depend on his best technician to fix it reliably

 Link to article on Linkedin

 

 

Reinventing manufacturing tests for automotive electronics

 Ram Mohan Ramakrishnan

Ram Mohan Ramakrishnan

Automotive electronics has been making steady gains in percentage cost of the total vehicle cost world-wide. Consequently, it has been facing some of the same challenges that were faced earlier (and mostly solved by automated tests) in other areas of automobile mass-manufacturing – fabrication, mechanical assembly, electrical components and hydraulic systems.

A typical example is the Electronic Control Unit (ECU) that has become the heart (or brain!) of the modern automobile. An ECU receives inputs from various sensors and sends outputs to multiple actuators, in addition to communicating with other ECUs of related subsystems in the vehicle. Some ECUs implement performance critical functions such as fuel injection, ignition timing etc., whereas others control safety critical systems such as Anti-skid Braking (ABS), Electronic Stability Control (ESC) etc. Therefore an automated manufacturing test station for the ECU is significantly complex in design, involving several pieces of instrumentation, simulation of sensors and multiple automotive communication protocols.

Let’s see if some real-world figures could lend a quantitative perspective to this mass-manufacturing challenge. For instance let’s take the case of a mid-size automotive OEM that sells over a 100,000 vehicles annually, with production in 2 plants of identical capacity. That would mean at least (taking Engine Control) an equal number of ECUs supplied annually by their Tier-1 ECU Manufacturer who needs to manufacture around 8 ECUs in an hour, assuming full 3-shift operations. Assuming 4 parallel assembly-lines, it gives less than 30 minutes to manufacture an ECU! The time available practically for testing ECUs at the End-of-Line (EoL) is even shorter. Assuming 2 parallel test stations, the operator typically would have less than a minute to test an ECU – to load it on the test station, execute the automated tests, to know if it passed or failed, print a bar code and affix it to the passed piece (or dump the failed piece into the reject bin) and unload the ECU, and ready to load the next one! Added is the complexity of different versions of the same ECU that are simultaneously in production. Since batches having different versions of ECU come to the same test station, the operator would need to reconfigure the station for a different set of tests each time. The reconfiguration must be completed typically within 4 to 5 minutes before loading the next ECU type.

Now let’s review how this challenge applies (or doesn’t apply!) to different segments in the automotive industry. It’s a no-brainer that any Tier-1 Manufacturer (or OEM) in the business would have all of this covered in their factory floors already, if not they would hardly be selling! However it is no longer the steady-state in the case of a newly introduced ECU design, be it part of a new brand of vehicle the OEMs plan to introduce to the market, or be it related to an additional feature, like adaptive cruise control, that’s being introduced for a new model variant. Does the Tier-1 Manufacturer have the required engineering bandwidth to design the test station themselves? In the case of technology transfer for ECU design from a global principal, does the Tier-1 Manufacturer have in-house expertise in the early stages to develop a test station on time before pilot production starts? In the case of in-house development of the ECU, does the Tier-1 Manufacturer really have the resources, bandwidth and simply the time to get the test station ready before the ECU design passes all type tests and hits production?

Alternatively, do existing test station vendors for other components, like starter motors, tiltable mirror assemblies or instrument clusters, have the necessary expertise to design such a complex test station? What about ECUs for Electric Vehicles (and hybrids) that are predicted to transform the entire motoring landscape forever! Not to forget the two-wheeler (and three-wheeler) segments, which under the rapidly closing time window of emission control regulations (Bharat Stage-VI in India although behind Euro-VI by a few years, has a 2020 deadline currently!) will be forced to switch to ECU based fuel-injection etc. in a few years’ time in order to legally sell in the market.

Here’s where a little foresight into accelerating the design of manufacturing test solutions could benefit the relevant stakeholders. At Deep Thought Systems, We have designed and developed a reliable, modular and generic platform called TestMate for building manufacturing test stations specifically for ECUs. We have successfully customized Testmate to supply EoL test stations for ECUs to Indian Tier-1 Manufacturers and OEMs in a very short turnaround.

The Human Machine Interface (HMI) of the Testmate, the main part that the operator sees and operates on a continuous basis, is a very generic requirement that consists of rugged enclosure, controls and indications for long years of reliable performance in an assembly floor. They say, and we’ve witnessed it ourselves, that routine use of test stations by the creed of factory operators indeed constitutes a really hash environment! The mounting, orientation, peripherals for viewing and printing, display properties etc. are all ergonomically designed, optimally for continuous usage by an operator over an 8-hour shift (or longer!). We have successfully installed the test station in factory floors where they are being used continuously for years, with zero support calls.

We work with the customer on the ECU connector type, to design a custom cable harness and test fixture that includes the mating connector, with locking arrangement. The fixture design ensures proper contact between the pins of the ECU connector and the mating connector over months of continuous loading and unloading. We equip the customer with spare cable harness to handle the unlikely event of damage due to exceedingly rough/careless usage by operators, which can be easily replaced onsite without having to depend on a service engineer.

Built on the same principles as our other automotive offerings for vehicle diagnostics, testing and simulation, Testmate is capable of communicating with various ECU designs over multiple automotive communication protocols like CAN, K-Line and LIN and messaging standards like J1979, J1939, UDS, KWP2000 etc. We work with the customer to customize it for the ECUs communication specification. Apart from testing continuous engine parameters, Diagnostic Trouble Codes defined for the ECU can also be tested. Containing many building blocks of an actual ECU, for many communication tests the test station appears to the ECU as a peer ECU (sometimes multiple) of the related sub-system(s)!

Testmate can reliably simulate inputs to the ECU, ranging from the simplest ignition key switch to the complex crankshaft position waveform that is a critical input for many engine control functions. It also measures the ECU’s outputs, ranging from the discrete voltages or timed pulses to PWM waveforms to actuators, and evaluates it against defined limits for pass or fail. In addition to functional tests, power supply and other electrical (negative) tests can be performed to test how well the ECU hardware responds to abnormal conditions, like reversed polarity of the power supply, under voltage etc. The I/O instrumentation is completely custom-designed as per the interface specification of the ECU.

The HMI software supports multiple levels of users, with differential permissions defined for each login level, like running tests, modifying test parameter limits, changing the sequence of tests, error message text, test calibration and troubleshooting. All tests are logged for later review by supervisors or managers. For failed tests clear troubleshooting assistance is displayed/logged as to which specific test failed and how exactly, so that the defective unit can be repaired. An ECU may come in twice for tests, once after bare assembly without the enclosure, and once again after the enclosure is fitted.

Finally it all comes together in the hands of the operator, who after loading an ECU has less than a minute to run the automated tests to know if it is a pass or a fail. Pass is good news always, the ECU gets a bar-coded label stuck on it and moves forward to the next stage. However a fail is hardly the end of the road because in order to keep the rejection costs low failed units need to be repaired, with the test station providing precise troubleshooting information to get it repaired quickly. In this context a few pertinent questions for relevant Tier-1 Manufacturers and OEMs are:

1) How much of ECU test station design could be generic, versus how much of it should essentially remain ECU design specific?

2) Does it justify to their business to completely reinvent a unique solution to their challenge in terms of engineering effort, cost or timelines? While large parts of the challenge retain a commonality, which a generic test platform such as Testmate has not only abstracted, but also been customized for specific ECUs and proven on the factory floor.

At Deep Thought Systems, we clearly understand the generic and reusable parts of the TestMate platform which help accelerate the design of EoL Test Stations. A high-performance hardware platform, powered by a real-time operating system and sound embedded firmware design practices ensures fast test execution and that all timing considerations in vehicle communication protocols are taken care of. Thanks to our expertise in digital and mixed signal hardware design, we are able to quickly customize other parts of the test station like I/O interfaces, ECU fixture and HMI software as per the customer’s specification and needs with total assurance of the customer’s Intellectual Property.

Another closely related area for production where we could work with customers to provide a quick solution is the design and supply of ECU Flashing units. Operators use the flashing units to flash the firmware into ECUs after assembly. The design of the ECU flashing unit is greatly accelerated by our generic ECU flashing framework, where the only input required from the customer is the seed generation algorithm for unlocking the ECU, which could be imported into our firmware as a library (in binary form) to protect the customer’s (or principal’s) confidentiality. In conclusion, our expertise and track record of supplying and installing EoL test stations on factory floors and supporting production personnel in the usage and fine-tuning of these systems will ensure an efficient and trouble-free operation for the customer for the entire production lifecycle.

Link to Linkedin article

Crowdfunding- a boon or a burden to Tech Startups

These days we see quite a few technology companies going the crowdfunding route(Indiegogo/Kickstarter) to get to market sooner rather than wait for the traditional way of raising money to build the product. It appears as a beautiful idea if co-founders do not want to give out equity but raise money to get to market.  But I personally feel that this is a double edge sword and entrepreneurs have to be very careful with the choices as it may end up hurting more than helping in the long run.

What I have noticed is companies look forward to crowd sourcing mostly for either one of the following reasons

  1. Raise money to help them accelerate the engineering cycle time and help them reach market faster with confirmed orders  – The challenge with this usually is if you are not far enough into your engineering /product cycle with most problems solved the money raised through these campaigns are in most cases is not enough to get to production and delivery.
  2. Create a sales and marketing buzz which then later helps them to get leverage with retailers and opens up many channels – This is a fantastic model because getting into some of the traditional channels to sell a product is not easy. But these days Bestbuy/Amazon etc. have a separate focus on successful products from these campaigns. So this will enable the startups to get into shelf faster if they are successful. This also gives a better chance of getting picked up by some distributors.
  3. Show the demand the product has in the market to convince tradition VC’s to put in money into the company – This is a good idea only if you are convinced that your product is going to be a runaway success else the chances are that it can do more harm.  Any thing less than a runaway success is going to raise more questions and challenges when startups try to raise money than help.

I have read few statistics and based our experience, the projects mostly do not make it out in time. This ends up damaging credibility with the same customer base which supported the product. And now if the product turns out to be below par after a long wait, we have a very unhappy customer to deal with also.

What I noticed is that many of these companies fail to deliver on time because

  1. These companies are either trying to solve some really challenging engineering problems which need a tremendous amount of money than what they can get from a Kickstarter/Indiegogo campaign. So they start falling behind on development goals and delivery deadlines
  2. They have the right idea and concept but limited experience in delivering products end to end and when they start dealing with it they realize the unknowns are lot more than the knowns and they start slipping
  3. These companies are fighting battles on many fronts and crowd sourcing is just one of the avenues. So they do tend to get carried away in their engineering cycle when they see greener pastures which ends up adding delays.

My personal thought always has been crowd funding is a good platform if you are done with 80% of your engineering . As I mentioned earlier this is because the money you raise from pre-selling this product is usually good to pay for your production needs only. However, if you are planning on doing your core engineering and delivering product based on this money then the likelihood of failure and delays are very high. The only exception I can think of is if the company has a reliable partner/team in a country like India, China where a bulk of the engineering is being done then this money does tend to help even if they are behind in their product lifecycle.

I think backers need to check with the company before putting money in as to how much of engineering is already done and ask to show working prototypes, actual Industrial design mockups, software demos etc. before trying to put money in.  Also, it may help to ask how the money collected is going to be spent because it gives you an idea of the readiness of the product you are backing.

Link to article on Linkedin

Reality check of PaaS Solutions for distributed systems in IOT and Big Data applications

Manoj K Nair

Manoj K Nair

‘Platform as a Service’ (PaaS) in the distributed systems arena is gaining wide adoption nowadays as the cloud is gaining more customer confidence. The latest IDC forecast states “By 2020, IDC forecasts that public cloud spending will reach $203.4 billion worldwide”. They also predict a fast growth in the PaaS segment, precisely in the next five years, Compound Annual Growth Rate (CAGR) is predicted at 32.2% which is very promising. PaaS Solutions for distributed systems have captured the serious attention of big players, like Amazon (AWS EMR), Google (Google Cloud Platform), Microsoft (HDinsight), Databricks (Unified Analytic platform) etc., and the count is growing by the day. The same is the case for IOT, with platforms from Amazon (AWS IOT), IBM (Bluemix), CISCO (Cloud Connect) etc. being the major ones in the growing list.

The explosive growth of PaaS Solutions is boosted by the complexity of DevOps and administration nightmares encountered in distributed systems; we still remember the Apache Hadoop version upgrades that always led to sleepless nights!

PaaS Solutions absorb a lot of complexities of the distributed systems which allows us to,

1.     Do the evaluation of platforms straight away. You don’t need to wait anymore for cost approvals, deployment completion as in the case of On Premises or IaaS deployments.

2.     IOT enabling becomes as fast as just plugging in an agent in your device.

3.     Automatic version upgrades of opensource distributed platforms like Apache Spark, Apache Hadoop, Apache Kaa etc. becomes just configuration changes.

4.     You can enjoy the additional features like Notebook integration, REST API service support etc. provided by the vendors

All fine! But are there any hidden factors in PaaS Solutions that need to be considered? From my experience of the past few years, it is a big YES! Especially for IoT and Big Data applications.

A ready-made dress may still need alterations!!!

PaaS solutions allow us to remain focused on the application use case by simplifying the spinning up of any platform with few clicks. Moving to another platform configuration is as easy as changing a few parameters and doing a restart. Major configurations and optimizations inside the platform are completely transparent to the user, which is an advantage most of the time.

However complete transparency to the system is not always insightful. You may need to play around with platform configurations to tune your application on top of it – scenarios like trying a few customized or new plugins into the platform which can give extra muscle to your application. As the open source incubations are growing rapidly and lot of new innovative tools in distributed systems are getting released every month, you need to have the flexibility to use them on the platform. Debugging or performance benchmarking of the application running over a totally transparent underlying platform is not good news for system designers. So when the platform is said to be transparent, we should also check the level of control we have over the platform.

For instance, while working with a major US healthcare player for collecting their large data streams for predictive and descriptive analytics, we were using Kafka for data injection and Apache Spark Streaming PaaS for data landing and processing. The initial evaluation and selection of the platform went well with standard architectural considerations and we were happy with the platform choice. Once the development of the application’s functionality was over and alpha tests completed, we started looking to make a few optimizations and tuning as part of the refactoring, for which accessing the platform cluster nodes became essential. We requested the platform vendor for access to the cluster nodes, but their reply was disappointing. Their customer support said “It’s completely transparent to user and we do not recommend any access or modification of the platform configurations”. We were stuck!!!

In another case of a Smart Battery IoT project, we were pushing status info from the smart device to an IOT PaaS platform for self-tuning. The data was being stored internally in the PaaS system. Things were working great and we were able to view the data using their custom tools and REST API based limited query access. However in our project, we made a strategy to create a raw data lake into AWS S3 for future analytics. To our surprise we found that there isn’t an option for data export! Being a very basic yet important feature, we contacted the IoT platform technical support. Their response was “Yeah, it is a simple feature, but it is not in our ‘Business priority list’ of features. So, it may take us some more time to do it”. How much more time was unknown! We were stuck again, and had to review the raw data lake policy.

In both cases, our development plans were seriously impacted and we were forced to skip/postpone major use cases, or start looking out for alternative platform to migrate to, although so late into the project. Let’s closely observe the responses from technical support in these two cases for a few interesting facts.

Case 1: “It’s completely transparent to user and we do not recommend any access or modification of the platform configurations

Transparency of platform complexities is definitely an important motivation to opt for PaaS, as it gives a quick, efficient and cost effective way of building the distributed system. But it is important to have an insight into the platform internals and in few cases some control as well. Being a system designer, we don’t like to swallow things as they are!!! After all, “platform limitations” is definitely not the story we want to tell our customers! In this specific case, we were looking to try out external monitoring tools that need a few agents to be installed into the cluster nodes. Eventually supporting a third-party BI tool took us roughly two months, in coordinating between the technical teams at the PaaS vendor and the BI vendor. This is simply not acceptable to the customer in terms of time or budget.

Case 2: “Yeah, it is a simple feature, but it is not in our ‘Business priority list’ of features. So, it may take us some more time to do it

Not just disappointing, this is alarming!!! Technical interoperability for the customer’s data should not be restricted for the sake of business priority. Unfortunately, the so called “business priority” often loses focus in retaining customers which reminds one of a “My way or the Highway” strategy! No customer wants his data being stuck in a specific platform. We need the flexibility to move it through multiple platforms, as business data has latent insights which could be extracted through different systems today or in the future.

To sum up, apart from traditional architectural considerations for selection between OnPremises or IaaS, PaaS or SaaS, we should be vigilant regarding these hidden factors during the selection of distributed platforms especially for IoT and Big Data applications, where large amounts of data are generated. The hidden factors are tricky in the sense that they may not be visible in the first look.

Some of the architectural considerations that help mitigate these hidden factors are given below.

1.     Create a proper migration plan – This may not be a short-term goal. But it becomes very important because as and when the data grows you may end up in a world of restrictions.

2.     Make sure you have enough control over the platform internals – Although you want to avoid administration overheads as much as possible, you still need good control of the platform for development, refactoring and analysis. Distributed system usage without platform control is painful in the long run. Telnet or SSH access to the cluster nodes, privilege to install custom tools and configuration level flexibility are few items to be verified in general.

3.     Third party integration flexibility – Most of the time, the system that we develop would be part of a pipeline and may need integration with customized systems like monitoring tools, custom logging methods etc. which make the integration hooks critical.

4.     Platform vendor’s willingness to provide functionality on demand – Platform vendors should be able to handle custom functionality requests on demand. We cannot wait indefinitely for the platform to support it in due course. Make sure that their quick and efficient response is covered in your Service License Agreement (SLA).

Distributed Platform as a Service is definitely growing rapidly, and customers will continue to invest heavily for the combo-advantages of reduced Capex cost, reduced time to market and reduced maintenance/administrative complexity. But I hope the quality and competitiveness of PaaS Solutions also matures fast for the benefit of investing customers, like our IoT and Big Data customers at Sequoia AT. Let’s hope a day will come soon when the platform vendors start advertising their respective platforms by throwing an open challenge “Hey, try out our PaaS solution and if you don’t like it, migrate to any other PaaS solution in 24 Hours or 1 Week!!!”

Link to Article on Linkedin

Industrial IOT (IIOT) – Hype or Reality

We have been hearing a lot about IIOT to be the real revolution happening or industry 4.0 or the next industrial revolution. However its not like a eureka moment and it’s not something set out in future. It’s been an evolution into it for past many years. Factories were already getting digital with the industrial internet, with Industrial IOT its just picked up pace. The tools, technology and ease of data access has accelerated the pace of this adoption.

Large industrial houses like GE or Siemens or ABB were always an IIOT company although they were not known for it. They had the ability to monitor and managed the expensive machinery health Since it was important for them to prevent downtime to customers . It also enabled them to learn in real time how machine was being used to improve their engineering. The thing that has changed is that this capability can be offered and implemented by any industrial plant of any size or revenue.

IIOT is a vast area which includes everything from sensors to data big data & AI. After ERP, IIOT will change it further to pick up on problems sooner and there by saving time and money . Imagine a small shop manufacturing pumps – They can now be connected real-time to their sales offices so they know which pumps are selling each day which in turn enables them to adjust the production to what’s needed more, to getting inventory only when they need based on this data, and the predictive maintenance systems enable them to know if there is any flaws in the manufacturing process and once the pumps are installed at customer premises they can collect the live data and alert customer of any possible problem they are foreseeing .

There are many companies operating in this space trying to address different parts of the puzzle . At SequoiaAT we have been fortunate to work with 2 companies in this space @opabydesign work in building condition monitoring and predictive maintenance. @Deepthoughts works on building energy monitoring solutions for factories to ensure that machines are running efficiently and at optimum energy consumption

We may not see or experience much change in daily life unless we are involved in the industry or factory and that’s our daily job. Supervisors expertise to be called upon to identify and fix a problem is going away as a decision will be made on actual data and not the experience of a floor supervisor. Today if you are a small shop, and say a machine starts making noise most likely your workers are depended on more experienced people to troubleshoot and tell what the problem could be with help of these cheap but effective smart devices .

Link to Article on Linkedin

Bringing home SAE J1939 Heavy-Duty Protocol Simulation

The J1939 standard for heavy-duty vehicles drafted by the SAE (Society of Automotive Engineers) in the mid-90s was driven originally by the “ECU trend” with the main objective of controlling exhaust gas emissions under increasingly tightening US and European regulations. Having gained wide acceptance ever since among diesel engine manufacturers, the SAE J1939 heavy-duty protocol has presently reached the stature of the de-facto standard for Truck and Bus manufacturers worldwide, for communication between various vehicle components and for diagnostics.

J1939 is a set of standards that includes a higher layer messaging protocol that works over CAN (Controller Area Network) protocol at the physical layer. The communication model supports both peer-to-peer and broadcast communication. The J1939 message format uses the Parameter Group Number (PGN) to label a group of related parameters, each of which may be represented by a Suspect Parameter Number (SPN). Continuously varying vehicle parameters (like Engine RPM etc.) are defined along with their valid range, offset, scaling etc. Besides, discrete (ON/OFF) parameters (like Brake Switch ON etc.) are defined separately. Commands to Enable/Disable specific vehicle functions (like Engine Fuel Actuator Control etc.) are defined.

Time based updates happen at 20 milliseconds (or lower) repetition rate, whereas the rate is significantly higher at higher Engine RPMs. Some periodic messages contain information that is of particular interest only when a specific state change occurs, and there is a defined range of repetition rates for these messages. Diagnostic messages (DMs) from various sub systems (like emission control etc.) are defined as per the Diagnostics Application Layer of the J1939 standard that includes services like periodic broadcasts of active DTCs (Diagnostic Trouble Codes), reading and clearing DTCs etc. Manufacturer specific parameter groups are supported that allow OEMs to define their proprietary message in addition to standard messages.

ECU design engineers of vehicle sub-systems at automotive OEMs, Tier-1 suppliers and R&D Service Companies routinely use J1939 Simulators for their product development, test and validation activities. In the early stages of development, a simulator comes handy for providing signals from other vehicle components exactly the same way as it would be in the real vehicle environment without the need for an actual vehicle in the lab. For instance a design engineer working on an ECU development program for Transmission Control would need signals from Engine Control system, Braking System etc. in order to validate his design functionality and performance. The ECU would get all these signals from the Simulator exactly as it would receive them in a vehicle environment, the physical connection provided by 2 CAN wires (CAN-HI and CAN-LO) and Ground (GND), taken out from the Simulator’s 16-pin OBD (or 9-pin D-Sub) connector using a custom wire harness to the mating connector of the ECU.

The J1939 simulator provides the design engineer with the ability to generate and vary individual parameters in order to check the response of the system under design/test. The required variations could be manually controlled using (rotary knob) potentiometers for continuously varying parameters. Some simulators automate the variation according to a pre-defined curve. A linear ramp that sweeps the full range (0-100%) of the given parameter, in increasing steps of 1%, is typical. Advanced simulators based on engine modeling data provide the ability to vary multiple parameters simultaneously in a specific relationship with reference to each other for better real-world simulation. A cost effective alternative to this would be to record multiple parameters of interest from the actual vehicle under standard test/driving conditions for the required duration, also known as the drive signature, and playing back the captured signature in the lab in the same time base, although with a lesser timing accuracy. Add on the simulation of actual vehicle hardware, like sensors, actuators etc. to create a fully Hardware-In-The-Loop (HIL) simulation and the full-extent of the simulation picture becomes complete.

Indian automotive R&D groups have traditionally banked on imported tools for J1939 simulation. Originating from USA, Canada, Germany etc. many of them come with pricey licenses although offering just an elementary 5-signal manual simulation. A few sophisticated ones with automatic ramp sweeps etc. are super-pricey, that even Indian R&D subsidiaries of multi-national OEMs have to contend with time-sharing the same simulator across multiple engineers/teams. It is in this context that a strong need is being felt for a high quality, cost effective J1939 Simulator that is indigenously designed and manufactured, that could provide many Indian customers the much-needed scalability for their R&D activities and reduce their dependence on imports.

Awareness on the availability of an indigenous product is the starting point however strict adherence to the standard is a hard requirement, including very strict timing considerations, in order to create a positive lean among automotive customers who always select and use only “proven technology”. Benchmarking data with reference to competing products could help customers get quantitative insights. Pilot trials could help them in familiarizing themselves with the indigenous product and to evaluate it against their experience with imported tools.

We at Deep Thought Systems design manufacture and supply J1939 simulators to Indian automotive customers, in addition to other offerings for CAN/J1939 logging, test/diagnostics, J1939 based displays and ECU manufacturing test automation. In our endeavors in bringing the above mentioned advantages to the Indian automotive R&D sector, we found that a customer’s need many a time is a highly customized simulator for their specific application. And thanks to our expertise in automotive protocols like CAN, OBD-II and J1939, and being fully in control of the hardware design, component sourcing and manufacturing as well as the embedded firmware and application development, we find ourselves well placed to deliver to these custom needs.

Post Scriptum:

A later industry development has been that all the major European heavy-duty OEMs came together in 2000 to co-develop the Fleet Management Standard (FMS) which is based on J1939 that incidentally opened up possibilities for manufacturer-agnostic telematics applications. The J1939 simulator, combined with suitable GPS simulation having the required levels of performance, offer telematics product designers a proven means to quickly test and validate their design well before going for in-vehicle tests.

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Alexa- What excites me to explore this latest from Jeff Bezos’s research hub

 Anu Pauly

Anu Pauly

Nowadays voice communication has become the easiest way to interact than the other mediums of communications. Since 1994, when Jeff Bezos founded Amazon, they have been the inventors from STEM to Prime to Web Services to Kindle and the latest addition of Echo, Echo Dot and Echo Show. Echo series connects to the voice-controlled intelligent personal assistant service Alexa, one among that best till date.  Alexa is named after the ancient library of Alexandria. Using Alexa you can call out your wishes and see them fulfilled—at least simple ones. For example to know the weather of any place, play music, do a Google search etc..

Alexa Enabled Devices available in the market are Amazon Echo, Echo Dot, Echo Show and a new addition announced The Echo Look. You can explore these amazing products in https://echosim.io and login to Amazon.

The Alexa Voice Service is currently only available for US, German and UK customers with an Amazon account.

The architecture of Alexa is, when the user asks something like “Alexa, tell me the weather of San Francisco”, the audio request will go to the Amazon voice Service(AVS) i.e Alexa .It converts speech to text. The keywords are “Weather” and “San Francisco”, processes it and returns as Voice to User. Alexa Skills have two parts Configuration i.e. data in Developer Portal and Hosted Service are responding to User requests The Hosted Services available are Amazon Lambda or an internet accessible HTTPS endpoint with a trusted certificate. You can build skills using Alexa Skills Kit(ASK). The Skills that are supporting here are Custom Skills, Flash Briefing Skill and Smart Home Skill.

About the architecture of Alexa Skills Kit(ASK), when the user speaks a phrase beginning with “Alexa” and the Echo hears it, the audio is sent to AVS for processing. An Alexa skill request is sent to your server(Lambda) for business logic processing. Then server responds with a JSON payload including text to speak. Finally, AVS sends your text back to the device for voice output.

The specialties of these device are the far field’s microphone and there’s no need of an activation, simply say the trigger words like “Alexa”(default),”Echo”, “Computer”. So that it can respond to Voice Commands from almost anywhere within Earshot. Microsoft’s Cortana , Google Assistant and Apple’s Siri provides the similar Services. However, if you get used to Alexa it feels much more natural and responsive than speaking to a phone-based voice assistant. Voice control frees you from being constantly tethered to your smartphone.

Manufacturers of automobiles, kitchen appliances, door locks, sprinklers, garage-door openers and many other recently connected products are working to bring to Alexa or a similar voice-driven service to their devices

Alexa is particularly useful for smart-home because it allows you to control your connected devices without having to take out your phone and launch an App.

Despite the success and growing interest in Alexa products and services, Amazon still faces scrutiny over the potential privacy implications of having an always-on, always-listening device in peoples’ homes, cars and other personal spaces.

I was excited to know about Echo, so tried my part to add Custom Skill in Alexa. I could build a sample Quiz where Alexa acts as a Quiz Master. It was fun, but more importantly, I am onto see how effectively this can be benefited for Connected Homes.

Ultimately, Alexa is using natural language processing system(voice)to interact, so no need for the user to change his accent. Be You and Enjoy Alexa!

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IoT in Construction Industry

Aju Kuriakose

Aju Kuriakose ( Linkedin Profile)

I absolutely love my job and what we do at SequoiaAT. I am fortunate to learn about many new industries and technologies as we work on helping Startups and Fortune 500 companies with new product ideas. As part of engaging with a recent customer, I got a learn a lot about the home construction business in the USA.

I was surprised to see how one of the oldest industries know to mankind- the home building was slow in adopting the latest technology trends. Although the commercial construction side has been fast to adapt to the new technologies to provide energy efficient building, the home construction side has been way behind on this

I think the reason for slow adoption is Homes are basic necessities of humans and probably the biggest investment an average person makes in his/her life. So given everything equal, people, in general, will spend more money on buying a bigger house for same dollar value than buying a high-tech house. Also, another reason could be that the necessity always exists. There is no urgency to reinvent or adopt technology at a fast pace.

As Smart buildings become popular and the norm, the home construction industry will have to change itself and adapt itself to the new norms. Sooner or later there will be a disruptive force in the industry. The scope of bringing IoT-enabled technologies are unlimited in every part of the supply chain right from selling to occupancy. As an example – Having an IoT beacon beam out to potential buyer passing by information about the house or lot.

Incorporating some of the smart sensors for occupancy, temperature etc. during constructions itself rather than retrofitting. It will save 100’s and 1000’s of dollars for the homeowners in long run for a fraction of the additional cost while setting it up. Devices like Powerwalls from Tesla or leak Detection systems from Flowlabs will get embedded into the home construction process. Smart asset tracking and people tracking will ensure that right tools are present at the right place at right time.

The backend construction cycle itself will get faster with the use of technology as the tools & equipment be used can be tracked better and maintained better leading to less downtime and loss of working hours. IoT enables devices will also ensure better safety at the worksite and can lead to better co-ordination.

Even for a consumer point of view, warranties can be tracked better as every single component used in the house being build can be tracked and managed to their warranty.  I think like commercial building owners advertise how energy efficient the building are, builders need to start paying more attention to  HERS index (Home Energy Efficiency Rating System)

As of now, I think many of these technology implementations are at a hobbyist level and need mass adoption. I think buyers need to start pushing on the HERS index rating to put pressure on homebuilders to use better technology to produce more energy efficient homes.

Home construction companies which adopt and embrace the new technologies will have better and faster turn around time and edged out the competition. Plumbers, electricians, painters etc. will have to learn new skills to incorporate technology tools into their work to stay competitive.

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Critical Success Factors for Medical Device Product Development

According to published market reports, by year 2021 medical device market is expected to grow to a staggering $340+ billion. The opportunities are expected to be more in general medical devices, cardiovascular and surgical & infection control segments. With such a tremendous market opportunities in the global market, it is imperative that medical device product developers to be aware of the stringent demands of design and development which emphasis on safety and compliance to established regulations and standards. Over the years, our experience with major medical product companies like Johnson & Johnson, Boston Scientific, Medtronics, Baxter, etc., we could see and experience various development approaches, challenges and stringent standards compliance needed by both client audit teams and independent audit teams. Some of the products developed included disposable colonoscope, automated sterilizers, blood glucose meters and a drug dispensing implantable device. This is an attempt to share our experience in essential elements in product design. Similar share on process elements will be posted soon.

Medical devices can be broadly classified into three market segments – Diagnostic, Therapeutic and Implantable. Based on Safety and Risk assessment the devices are classified into Class I, Class II or Class III device. Product designers and manufacturers, must demonstrate adequate controls and “compliance” to avoid being found guilty of deficiencies. It is important to understand that in this domain “intentions do not count but action alone”.

Product Development

Product development rigor depends on the product safety classification, history and whether it is a “first of its kind” product or “me too” product. Focus should be on characteristics of materials used, effective documentation from the proof of concept phase in case of first of its kind product. Manufacturing Process is important (especially material consistency and sterilization & hygiene). Software development needs to demonstrate complete verification and validation throughout the development life cycle.  Severity of device failure decides the development rigor (Level of Concern Analysis (LOCA). Proof of positive compliance needs to be recorded and submitted

The product life cycle phases are Concept à Design à Implement àManufacture à Disposal. This life cycle looks very much standard one but what differentiates is the focus you need to bring in each of these phases from product, process and compliance perspective. In concept phase inputs are to be considered from market, existing products, product category specific standards. In design phase DFX aspects should be planned and incorporated. Design rigor is brought in through processes like DFMEA (Design for Failure Mode Effect Analysis), Reliability Prediction, PFMEA ( Process Failure Mode Effect Analysis), System Hazard analysis, Software Hazard analysis, Requirement trace matrix, COTS (Commercial Off-the Shelf) products validation, test plans covering verification and validation with both positive and negative compliance.

User Interface design is another important aspect that needs to be practiced. This will contribute in improving the safety of medical devices and equipment by reducing the likelihood of user error. This can be accomplished by the systematic and careful design of the user interface, i.e., the hardware and software features that define the interaction between users and equipment.

Focus on Six early engagement areas will significantly contribute in developing a safe and reliable product. These are – PCB layout and fabrication, PCB assembly, Component engineering, Test engineering, System engineering and packaging and Product support.

Conclusion

Fundamental to designing and developing a medical product which is safe and effective is to integrate safety into product development. Objective should be to Remove or lower the risk at design phase, followed by Protecting for risks which cannot be removed at the design phase and failing which Inform the user about the residual risks through appropriate methods. The goal is to cancel all foreseeable life time of the apparatus – transportation, installation, usage, shutdown and disposal.

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