Winning Manufacturing Strategies

June 28, 2017

Telematics driven parts supply chain

Authors :

Srihari Sreedharan, Associate Consultant, MFGDCG, Infosys, Srihari.Sreedharan@infosys.com
Anand Sethuraman, Senior Consultant, MFGDCG, Infosys, Anand_Sethuraman@infosys.com
Barath Ashokkumar , Senior Consultant, MFGDCG, Infosys, Barath_AshokKumar@infosys.com

Introduction

A common occurrence we encounter in dealerships is the customer being informed by the dealer/service agent of a delay in procuring the spare part from the supplier or the OEM (Original Equipment Manufacturer).

In spite of being an area of high significance, OEMs have traditionally found it hard to forecast the demand of spare parts accurately. Factors like complexity, erratic demand, large SKUs have continually thwarted the accuracy of forecast. On most occasions, lack of proactive mechanisms to anticipate part failures lead to long waiting time for a customer.

With the influx of Telematics into the world of Automotive, accurate data on parts usage will be made available for the OEMs, suppliers and dealers thereby improving the forecasting mechanisms.

Spare parts market categories

The current spare parts market can be split into two major categories:

1. OE Spares - These are spare parts which are sold by the OEMs through their exclusive dealerships and are typically sold at a premium. These parts are procured from suppliers and certified by the OEM as Genuine Spares.

2. Independent After Market (IAM) - The parts sold through this channel are available at all spare parts stores. These are sold independently by the suppliers but not certified by the OEM as genuine spares. Hence they are cheaper than the OE spares.

Shortcomings of current forecasting techniques

Given the two different types of spare parts market, the next logical question would be, how demand is presently forecast. With the demand being need-driven, companies currently use any one of the traditional forecasting methods or a combination of a few to forecast their demand. 'Poor data quality costs around $600 B US dollars to US companies each year' highlights a statistic.

Albeit having some well-defined methods, forecasting is still a tough equation to crack owing to the number of assumptions made during the process. With each part having its own demand pattern, it becomes increasingly difficult to forecast, thereby impeding its accuracy. Owing to this, dealers maintain large SKUs (Stock Keeping Units) fearing the opportunity cost due to lost sales. This overstocking not only leads to over usage of real-estate space apart from hampering working capital of the dealer but also result in piling up of obsolete stock. On the flip side, maintaining very few parts that have a constant demand would often lead to stock-out situations and customer dissatisfaction.

A mere increase in serviceability levels from 95% to 97% would increase the inventory levels by two times. - Based on inventory level calculation.

Magnitude of 'cost of stock' is very high as it leads to loss of sale and customer good will. Striking a balance to maintain an optimum level of inventory and desired service level, is an area where every automotive player is looking for solutions.

123qwe.png

Figure 1: Advantages of connected cars over unconnected cars [4]

Telematics as an enabler

With the technological advancements making automobiles more connected than ever, vehicles fitted with electronic parts and sensors are able generate huge amount of data. Breakthrough inventions in areas like Cloud Storage, Connectivity speed, Big Data Analytics are providing the means to store and process the data in a quicker and efficient manner. Similarly, sensors and other devices required to capture data have also become more affordable making the process simpler and easier

The type of data that vehicles on road can now transmit, range from overall performance indicators to the health condition of parts in use. Availability of this 'in-vehicle' data can dynamically alter the way the parts forecasting is done. It will not only improve the accuracy of forecast, but also help in ensuring 3R's (Right parts at the Right place at the Right time), the most sought after attribute by the companies. One can never guarantee the 3R's with the traditional forecasting methods as they work on the premise, "Future can be predicted by looking into the past", which most often has an element of error associated with it.

123asd.png                                     Figure 2: Benefits of vehicle connectivity [2]

The processed data can help companies gain insights on multiple parameters such as driving pattern, condition of the parts, and performance of the vehicle. This can play a great role in enhancing the customer experience. Monitoring the health of the parts in the vehicle can help a customer proactively schedule an appointment for replacement of the part and prevent unprecedented breakdowns.

For instance, consider a typical case of a sedan being continually driven on a hilly terrain resulting in brake pads wearing out sooner than the prescribed mileage/time frame. Access to this data can help OEMs gain insights and proactively store more brake pads in dealer locations on hilly regions. The data will also enable them to factor in the local geographical conditions when fixing the durability of the part.

This real-time data can help stakeholders get an idea of the kind of parts that are required to be stocked at the dealer locations. Accurate usage information could aid OEMs to do a targeted distribution aiding in optimization of inventory levels at dealer locations. Metrics like Inventory

Turnover Ratio (Times the inventory has been turned over in a particular period) will also improve from an OEM perspective due to the accuracy of the forecast.

The availability of data will also improve the communication and collaboration between the Tier I suppliers and OEMs due to the increase in transparency across the supply chain right from the dealer to the supplier, significantly reducing the buffer at each end. Suppliers functioning upstream will be able to efficiently plan their production schedules since the actual number of parts that are running on road and the number that needs to be replaced are available at any point of time, thereby drastically reducing the bull-whip effect.

Another major functional area, where 'in-vehicle' data can help reap benefits for the OEMs, is logistics.  The availability of real time data will bring down logistics costs drastically. Knowing what kind of part is needed at which place will help in considerably reducing the inter dealer transactions and ordering costs. Only a small buffer would be required to cater to unforeseen incidents like accidents.

Warranty management is an additional space where 'in-vehicle' data can provide a sizable benefit. Mining of real time data for key insights like driver behavior, handling, maintenance, location and other details will help in OEMs handle the warranty campaigns efficiently. It will help OEMs approach concerns like NTF (No Trouble Found) & Invalid claims in an informed and better manner as it saves precious time and money spent on unfruitful negotiations. Researches show that access to 'In-Vehicle data' can save warranty claim costs by 25% and also bring down recall costs by 35%

Conclusion

In the tightly contested space of automobiles, where OEMs are trying hard leaving no stone unturned to occupy the TOMA (Top of Mind Awareness) of the customers, leveraging the 'in-vehicle' data captured through telematics in the parts supply chain space can reduce the lead time across the chain from dealer to supplier significantly. It will also aid in improving the satisfaction levels of the customers, providing the much needed competitive advantage

References

[1]      Warranty management & Predictive Analytics-Tech Mahindra,2016

[2]      Connected vehicles executive summary, CISCO IBSG Research & Automotive Econometrics, 2011.

[3]      How to choose the right forecasting technique, John C Chambers, Satinder K Mullick Donald D Smith, HBR

[4]      Connected vehicle big data opportunities, SAS White paper.

[5]      Gartner, TDWI & Terra Data Reports

December 23, 2016

Leveraging the Potential of Automotive Enterprise Data

Authors

Vijayaraghavan Suranarayanan, Principal Consultant, MFGDCG, Infosys Ltd., Vijayaraghavan_s05@infosys.com

Snigdha Goyal, Consultant, MFGDCG, Infosys Ltd., snigdha.goyal@infosys.com


Introduction

Automotive Original Equipment Manufacturers (OEMs) are increasingly looking up to big data analytics for solving many of their business challenges and staying ahead of the competition as the data available at their disposal is increasing at exponential rates. The data collected from the vehicle, along with other data owned by the OEM across various enterprise functions, and data collected from external sources can provide valuable insights about the customer and vehicle - two key elements that determine the fortunes of any automotive company. Automotive companies are trying to maximize the value delivered to a customer by leveraging the collected data for service & product offerings, customized customer experiences and additional revenue through a deeper understanding of customer's attitude and behavior. Analyzing customer behavior provides valuable insights into aspects such as brand loyalty, customer satisfaction, customer preferences, sales and aftersales behavior, price sensitivity and propensity to buy (accessories, warranty plans, new vehicle and subscriptions).

 

Leveraging Automotive Enterprise Data

Data analytics presents a plethora of business opportunities for Automotive OEMs. A typical Automotive OEM with fairly advanced Telematics capabilities and IT systems gathers a good repository of information that can feed in as inputs for analytics. The data available with an Automotive OEM can be broadly classified into three overlapping categories - vehicle data, customer data and external data as mentioned in the diagram below. The classification is based on the primary entity to which the data is attached. For example, a customer's social media activity is tracked and stored. Hence this can be considered as customer data. Similarly the ECU and sensor data is generated for the vehicle, so it can be considered as vehicle data. In some cases, there are more than one entities associated with the data. For example: Point of sale data may include incentives offered to the customer for the vehicle. This can be considered as both vehicle data as well as customer data. This overlapping circles in the diagram signifies that there is overlap in the classification of data by entity.

Vehicle data covers the complete lifecycle of a vehicle through stages such as Manufacturing, Sales and Aftersales till the end of a vehicle's life. Data that can be collected comprises of point of sale data, Factory master data, ECU and sensor data, part wear & tear and failures, warranty claims, recalls and service & repair data.

Similarly, customer data comprises of the data gathered in context of the current and the past vehicles owned by a customer such as vehicle ownership history, preferences and interest areas (music, sports, restaurants, fuel stations), call center touch points, dealership interactions, driving habits (speeds, braking, acceleration etc.) and social media usage.

OEMs also gather data from external sources to complement enterprise data. The external sources include, but not limited to Department of Motor Vehicles (DMV), Dealer Management Systems, Environmental data (e.g. weather), Internet of Things (IoT) devices and other vehicles.

Of all the data sources, Telematics data plays a major role by providing a wealth of valuable information from the Electronic Control Units (ECU) and Sensors, Navigation, driving behavior, telematics products usage, data from other devices, vehicles and cloud. View image to see the types of data available with an Automotive OEM.

There is a wide range and ever-expanding list of use cases of the Telematics data such as usage based insurance, driving assistance, predictive maintenance, personalized and location based marketing and several other new products and service offerings that are being talked about or implemented by various Automotive OEMs.

Outlined below are a couple of interesting analytics possibilities about how Telematics data can be leveraged to predict customer behavior in two important business areas - Sales and Service.

 

Predicting New Car Purchases

When would a customer be looking out for the next vehicle purchase? Can data analytics predict a customer's inclination for a new car purchase? These are some questions for which Automotive OEMs and dealers are looking for an answer.

predict_1.png

Figure 2: The Illustration shows a customer switching brands due to an unsatisfactory experience at a dealership for new vehicle purchase inquiry. The customer expresses anguish in social media and visits competitor dealers to look for alternate options. Using data analytics, this customer could have been retained in the same brand family.

With best-in-class technology and big-data processing capabilities, machine-learning models can be built to determine the propensity of a customer to buy the next vehicle based on various data points available. Age of the vehicle, wear and tear, miles driven and customers' past ownership history could be some of the influencing factors. The negative customer experiences that could be the tipping point for brand switching could be understood by analyzing call center call sentiments, dealer interactions or social media activity. OEMs could track the navigation patterns of the customer and determine if the customer is shopping around for a competitor brand in pursuit of the next car purchase. Applying human feedback to the machine learnt recommendations will help strengthen the prediction results.

 

Service Retention

The data from the ECUs and sensors give a good indication of the health of the vehicle and can be used to build intelligence around analyzing the patterns of individual customers in response to a failed part, and their subsequent visit to a repair center to get it repaired. Subsequently, the OEMs can send notification alerts or have the dealers reach out to the customers proactively for maintenance and repair services before the customers get these issues diagnosed and serviced elsewhere.

Service Retention.png

Figure 3: The Illustration shows a customer who typically goes to different repair centers depending on nature of the problem with his vehicle. In this case, the OEM or Dealer can reach out to the customer with targeted promotions for parts and services for which the customer would never visit an authorized dealership.


Competition and Collaboration

Other than the Automotive OEMs, the other Telematics ecosystem players are also be keen on gaining a share of the data and leverage it for building analytics use cases. Google's Android Auto and Apple's CarPlay are good examples where the ecosystem player has an opportunity to gather a lot of in-vehicle data. Another example is Verizon, who provides in-car connectivity to some Automotive OEMs, also has an aftermarket product 'Hum' that collects vehicle diagnostics data. There are other connected car startups like Zubie, who have forayed into multiple areas like insurance and onboard diagnostics and are collecting large volume of vehicle and customer data.

However, the ownership of the customer data and terms of conditions of usage are decided by contractual agreements and data privacy principles. Customer data cannot be collected, stored and leveraged in personally identifiable form unless it is communicated clearly to the customer and the customer provides consent for the same. OEMs while working with Telematics partners, will have to be careful in managing the contracts and data sharing agreements to ensure that they don't relinquish control of the data to their partners or do not invade customer's privacy.

With the increasing penetration of Apple's CarPlay and Google's Android Auto in the Car Infotainment space, the competition between Telematics ecosystem players and the Automotive OEMs will become more intense with respect to gathering in-vehicle data and leveraging it for analytics use cases.

Though Google and Apple have advanced technology capabilities, the availability of complete array of automotive enterprise data (mentioned in the diagram) provides a unique advantage for Automotive OEMs to dominate the data analytics space. However, as the competition is intense, agility and ability to adapt to changes in a marketplace are some of the key success factors in maintaining a competitive edge.

Automotive OEMs may be cautious in their approach towards Google and Apple, but are partnering with other technology and consulting companies to accelerate their journey towards data analytics. Recently, Toyota launched a new company Toyota Connected Inc., in partnership with Microsoft to significantly expand the company's capabilities in the field of data management and data services development. Ford partnered with IBM to add cloud and big data capabilities to its Smart Mobility initiative that aims to advance connectivity, mobility, and autonomous vehicles.

 

Key Challenges

The race to building cutting-edge analytics solutions is however not as easy as it may appear. Automotive OEMs will have to overcome several challenges in their journey with respect to acquiring data, storing data, and leveraging data for analytics.

Challenge: The customer has the freedom to download an application provided by a third-party telematics services provider and the OEM has to share the vehicle related data with the customers and these third party service providers based on customer's consent.

Cick on the link to view the architecture of a telematics platform that can be used by automotive OEMs to provide a seamless integration between the vehicle and the cloud based telematics services' applications. View image

ChallengeOne of the other challenges is the utilization of available data. Many a times, data is gathered without keeping the end goal in mind and a lot of data is not utilized for any meaningful analytics. This results in wastage of manpower and computing infrastructure. Michael Gorriz, Chief Information Officer, Daimler rightly said that "Big data analysis is an important area for Daimler, but generating large volumes of data is not a goal in itself. More important is to use the data to create business and customer value".

Also, due to the immense competition, OEMs have to innovate faster to keep pace with or to move ahead of competition. Agile execution of IT initiatives is one of the key success factors in order to achieve this. Slow decision making and execution of IT initiatives will result in losing relevance in a fast changing market.

 

Conclusion

Automotive OEMs are collecting huge volumes of data from the vehicle, ecosystem and various other sources. The real potential of the collected data can be realized only with a clear vision of the data analytics strategy. Identifying the right business opportunities that create value to all stakeholders, investment in the right analytics tools and resources, keeping pace with market trends, controlled cooperation with ecosystem players and technology companies, agile execution of initiatives and managing the challenges mentioned in this article are some of the key factors required to succeed in this highly competitive space.


November 8, 2016

Connected Cars: Forging New Partnerships in the Automotive - Supplier Landscape

Authors:

Deepthi K. Bhat, Lead Consultant, MFGDCG, Infosys Ltd., deepthi_bhat@infosys.com

Bharath Krishna Bellamkonda, Senior Consultant, MFGDCG, Infosys Ltd., bharath.bellamkonda@infosys.com

Snigdha Goyal, Consultant, MFGDCG, Infosys Ltd., snigdha.goyal@infosys.com

Kunal Kumar, Senior Associate Consultant, MFGDCG, Infosys Ltd., kunal.kumar12@infosys.com


The concept of connectivity in the automotive industry was pioneered by General Motors, when it introduced emergency assistance with OnStar in 1995. The concept was connectivity, which was previously limited to infotainment, has evolved to remote applications, safety and security, vehicle intelligence, eco driving, vehicle diagnostics and secondary services.

Ecosystem.png

Fig: Evolution of Connected Cars Features

To captivate the nextgen buyers, Automakers are now converting their vehicles into smart vehicles on wheels. Gartner predicts that 250 million vehicles will be connected with a 67% increase in the number of installed connectivity units by 2020. With an engineering legacy, automakers are now partnering with Technology Service Providers (TSPs) to reduce time-to market and increase the car maker's footprint. If they fail to do so, in the long term, automakers could end up as hardware suppliers to tech giants such as Apple and Google.


Factors Forging Partnerships

Automakers are investing in technologies related to vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) services, and fleet services to engage the millennial. Most of these services are realized by partnering with Technology Service Providers (TSPs). Our studies of the investments made by OEMs in Connected Car Services, suggest that the key influencers to forge partnerships are  - advanced driver assistance system (ADAS), semi-autonomous driving (SAD), remote applications, single mobile platform that manages the entire digital automotive experience, intuitive and safe access to infotainment, urban mobility and secondary services (usage-based insurance, toll collection). While a majority of these shifts demand collaboration with technology companies, trends such as urban mobility and secondary services require partnerships with fleet service providers, city and government administrators that provide infrastructure services, and research partnerships with universities.

A few examples of such partnerships: General Motors' partnership with Shanghai OnStar and Didi Chuxing - China's largest car-hailing app - to support expansion plans in China; Toyota's partnership with City of Grenoble, Grenoble-Alpes Métropole, Cité lib and the EDF Group (an electric utility company in France); BMW's investments in MyCityWay and Parkmobile. From an infotainment perspective, multiple automakers have partnered with Google, Apple, and MirrorLink to develop a single mobile platform. Until 2016, 35 auto brands have tied up with Android Auto, 41 with Apple CarPlay, and 12 with MirrorLink.

An unlikely avenue that compels automakers to form robust alliances across industries is cyber security. Ransomware designed by professional attackers, could be the most serious form of threat. Consequently, automakers are addressing these issues by opening up information sharing platforms, with security agencies, hackers and other OEMs. Automotive Information Sharing and Analysis Center (Auto-ISAC), German Cyber Security Organization (DCSO), and GM Vulnerability Disclosure Program with HackerOne are some notable collaborations. Auto-ISAC is a collaboration of Automotive OEMs that promotes transparency in sharing cybersecurity threats and countermeasures. DCSO is a holistic effort to address cybersecurity across industries in Germany.


Constraints Around Partnerships

Some of the key challenges faced by the automakers in forming strategic partnerships today are:

  1. Partner explosion due to regional complexities: Enabling secondary services such as car sharing, usage based insurance, etc. will require OEMs to spend considerable effort in developing local alliances. Based on the area covered, multiple partners for a single region, and service, is a possibility.
  2. Long lead times to enable services: Scouting for the right regional partner and arriving at consensus on liabilities, could entail significant lead times. Unless tackled swiftly, OEMs could lose their first mover advantage and run the risk of becoming market followers than trend setters.
  3.  Inability to pass on the cost to the customer: While the consumer desires a world of functionalities, his willingness to pay for the same is not proportionate. Automakers have to absorb the cost of enabling digital features. E.g.: The total price of Mercedes E-Class increased by €1,654 between its 2010 and 2015 digital packages while the cost of adding connectivity options was €7,0002. OEMs cannot lose sight of indirect costs incurred due to strong vendor management programs and legal teams to enable these services.
  4. Strategic Failures: OEMs are investing millions in R&D towards services like urban mobility. Inability to predict the pioneering services with geo-specific variants and converting investments into viable products and services could lead  to heavy losses.
  5. Brand Management: Partnering with third parties for services with possible undetected vulnerabilities in the products, such as safety, will ultimately still be the responsibility of the OEM in case of recalls & lawsuits.


Trends

With burgeoning telematics services and complex TSP ecosystems, following are some of the trends we may expect to see in the near future:

  1. Bundling Telematics Packages: Given most of the telematics revenue is expected to flow from the customers of the passenger car segment, competitive pricing is key. If manufacturer installed options are offered at premium prices, the customer could very well chose a third party add-on solution, available at a much cheaper rate. Eg: Navigation devices are available in the market for €180, as compared to a €600 embedded option offered by the manufacturer2.
  2. Traditional technological providers reinventing themselves: TomTom, is a classic case of an organization that transformed itself from a portable navigation device manufacturer into a supplier of embedded telematics equipment. Proliferation of smartphones, that left portable navigation-only devices obsolete, forced the supplier to foray into the telematics business for survival. Today, major OEMs like BMW, Daimler, GM, Volkswagen, Toyota and Volvo use the platforms and offerings from TomTom in one way or the other.
  3. Cyber security: Besides a collaborative approach, automakers could engage with independent security validation services that review application code and provide unbiased views on the security features developed and closed out with their partners.
  4. Consolidation of Telematics Service Providers: Today, most TSPs specialize in services that pertain to one or two areas of Connected Car Services like infotainment or vehicle diagnostics. Automakers will look at enabling services across the spectrum. Instead of direct partnerships with multiple TSPs across service lines, they will look to minimize the overhead of supplier management. TSPs that provide a consolidation of services and in turn manage the tier 2 and 3 suppliers, will be the go-to partners in the future.


Conclusion

Global penetration of automotive telematics is expected to grow, from the current 48% to 62% by 2020, in the area of Vehicle Diagnostics. Safety & Security, is expected to capture more than 60% of the telematics services3. The evolution of partnerships is observed the most, in the areas of Urban Mobility and Infotainment.

With mobility services touted to be a profitable source of income for automakers in the next 5-10 years, it remains to be seen how the automakers will pool together and convert their R&D investments, partnerships across ecosystem players, regulations and understanding the consumers' needs (and services they are willing to pay for) and stay ahead of their competitors and/or afford to retain their spot. Investment in enabling these services and prudence are inevitable.

In the short term, connected cars services will act more as a product differentiation strategy rather than a source of revenue. In the long term, it will help open up new digital revenue streams through service offerings such as urban mobility, infotainment, and concierge services.


References

"Gartner Says By 2020, a Quarter Billion Connected Vehicles Will Enable New In-Vehicle Services and Automated Driving Capabilities", by www.gartner.com

"Automotive Telematics Market: Asia Pacific Industry Analysis and Opportunity Assessment 2014 - 2020", by www.futuremarketinsights.com

"Connected Car Study 2015: Racing ahead with autonomous cars and digital innovation", by www.strategyand.pwc.com

Continue reading "Connected Cars: Forging New Partnerships in the Automotive - Supplier Landscape" »

October 14, 2015

Future of eCommerce in India and its significance to a Common Indian Customer

Having noticed a tremendous innovation and growth in the Digital Transformation space, I wonder what it would mean to be a normal Indian consumer, who is now experiencing these changes. In this blog, I attempt to understand and bring forth the perspective of their experience on these wonderful E-commerce sites.

Continue reading "Future of eCommerce in India and its significance to a Common Indian Customer" »

September 15, 2015

The + Service opportunity for Industrial Manufacturing

Industrial manufacturing is typically a low-volume high-value long-term play. The potential high value of each sales transaction is counterbalanced by a generally protracted sales gestation period. And post commissioning, most of these capital intensive solutions have impressively enduring lifecycles

Continue reading "The + Service opportunity for Industrial Manufacturing" »

May 6, 2015

A subdued future for IOT

 

In case you haven't heard, the world is going to collapse soon! Well, not really but scientists and experts from UK have predicted that, at the current rate of data consumption, the internet will collapse in about 8 years. Wow! That's as good as the end of the world for the digital dreams we all had.

Which brings me to my favorite subject - Internet of Things (IOT). This premonition about the 'capacity crunch' of the internet will spell doomsday for companies betting on IOT enabled products and services since they will rely heavily on the internet. Real-time sensor-data transfer over the internet is the backbone of the connected world and one that will bring immense transformation to the way we use products and services. Gartner predicts 20 billion devices to be connected to the internet by 2020. This figure will only increase exponentially beyond 2020. All this internet activity due to IOT will only accelerate the downhill spiral towards the internet capacity crunch. If there is a capacity crunch in the offing, what happens to all the IOT use cases? With no regulations or regulatory bodies, how does one optimize usage of available internet capacity? With much to lose, I think it is time to introspect and determine what could possibly be a more practical choice for customers to get the benefits of IOT while still doing their bit to delay the doomsday.

I foresee a subdued future for IOT rather than the enthusiastic hurrah we hear from most analysts. Let me explain what I mean by 'subdued'. I believe that the theory of a connected world will remain just that - a theory. (Well, at least in the short term until we are able to figure out what and how to handle the entire IOT ecosystem and that too in an unregulated arena.) Gartner may be right about the number of devices being connected by 2020 but when it comes to transmission of data (and here's where the bandwidth crunch comes into play), it may not be practical to have all the connected devices to send data at all times. In fact, the rate and type of data to be transmitted will be controlled by the biggest equalizer in business - the humble customer or end user.

I think it will be futile and in fact amateurish for companies to just put up a few sensors on their products and start relaying the data over the internet. Not every customer would be ready to pay for this service especially if you are unable to show her the value of doing this activity in real time 24x7. I predict a bouquet of services to be offered by corporations to its customers to choose and determine which option best suits their (customer's) needs. Let's take an example of a smart refrigerator. Not all customers would be able to afford their refrigerator monitored for its health 24x7 since that would entail paying for a higher internet plan. Some may opt for an option wherein once the refrigerator starts giving trouble, the customer will be alerted on their smartphone and they will then have the ability to trigger a health check from their phone app. This app will finally push the logs (findings in software code) from the smart refrigerator to the service company over the internet for the technicians to analyze and revert with the best solution. The solution could either be an over-the-air software update or a field technician visit to check and rectify the problem at site. In any case, it will mean that the service company will have data upfront to analyze and decide before any visit.

The higher end customers may go in for predictive maintenance type of service packages which will help prevent failures but for those who cannot afford such premium services, they could at least go for these intermediate solutions. So how does this help in capacity crunch? Voila! - Optimized transfer of data over the internet from these connected devices. These assets will be part of IOT and hence connected; they will support customers to control when to send data and hence control costs and lastly, customers will be in better control of their data - thus addressing the data privacy concerns of many.

Internet doomsday or not, customers will challenge the IOT companies to come out with innovative options that will make the technology economically feasible to all. And it's upon us to make that happen. It will be disastrous for all players to thrust connected devices without providing options on how to optimize internet bandwidth. What do you think is going to happen in the future of IOT?

April 6, 2015

Records Management: Disaster Recovery Plan for Offsite records storage

In my previous blog "Significance of Records Management and Types of Retention Policies"

www.infosysblogs.com/thought-floor/2013/09/significance_of_records_manage.html, we talked about a well-designed Retention Management solution. We saw how it provides a cost effective method to manage large volume of records and help to adhere to compliance standards.

Continue reading "Records Management: Disaster Recovery Plan for Offsite records storage" »

April 2, 2015

Comparing the Big 4s of Social Media

Social Media revolution has enhanced the way we communicate with our acquaintances and also helped improve the efficiency of conducting business. There is not a single day when we don't hear news about Social Media or use them. Overall it enables individuals to receive update from friends, share videos and Photos. For business, it helps them to build and maintain new relationships.

Continue reading "Comparing the Big 4s of Social Media" »

March 31, 2015

The Digital Transformational Journey for the Manufacturing Industry

 

Manufacturing Industry had to go through a lot of challenges to cater the digital disruption is addition to the tradition challenges listed below.

·         Reaching out to the new market segments

·         Reducing the Operational cost and increasing efficiency 

·         Reducing the Time to Market                            

·         Ensuring that the logistical operations run smoothly

Continue reading "The Digital Transformational Journey for the Manufacturing Industry" »

February 13, 2015

POV on Architecture for Internet of Things

1.0 Introduction:

This article embodies the architectural thoughts on Internet of Things for Architects and developers. The aim of this paper is to provide a base architecture that covers challenges and main requirements of IOT projects and systems - devices, server side, cloud based services, third party integration that interact with and manage the devices.

1.1  What is Internet of Things?

The Internet of Things (IoT) is a scenario in which objects, animals or people are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. IoT has evolved from the convergence of wireless technologies, micro-electromechanical systems (MEMS) and the Internet.

 

A thing, in the Internet of Things, can be a person with a heart monitor implant, a farm animal with a biochip transponder, an automobile that has built-in sensors to alert the driver when tire pressure is low -- or any other natural or man-made object that can be assigned an IP address and provided with the ability to transfer data over a network. So far, the Internet of Things has been most closely associated with machine-to-machine (M2M) communication in manufacturing and power, oil and gas utilities. Products built with M2M communication capabilities are often referred to as being smart. ( smart label, smart meter, smart grid sensor)

 

1.2  Devices

The simplest devices have embedded controllers - they have no operating system
Devices with 32-bit system that can support OS - such as Linux
Devices with 32 bit/64 bit computer platforms such as a wearable watch that can connect to internet and support 2 way communication
Devices that communicates to gateways; these gateways perform filtering, aggregation, event processing

The way devices communicate with gateways/internet could be based on:

Ethernet, WiFi using TCP/IP or UDP, MQTT, http, CoAP

UART, SPI/ I2C

Near Field Communication(NFC)

Zigbee and mesh networks in RF, blue tooth

Modbus

Low Power Bluetooth technology

SRF


2.0 Challenges

We have to address the obstacles to the connection to the devices - Firewalls, Network Address Translation (NAT) and other obstacles on the way.

There could be issues in connectivity of devices due to internet connectivity, battery life, RF interferences, simply being switched off, physical security/damage etc.,

There is plethora of protocols, vendors in this space. Inter-operability among these and derive the required data from these could be a challenge.


3.0 Key Requirements:

·        Device management - remote provisioning and upgrade of firmware/software.

·        Device security is mandatory - only the authorized personnel should have access to the information from the devices. Also, lock and isolation of impaired/hijacked devices should be supported.

·        Ability to process live stream of data and apply configurable complex event processing/rules on the incoming data to respond real time/near real time.

·        Support for time series data and transformation of data to the granularity required for reporting.

·        Leverage existing open/marketplace API's, technologies - should have a loosely coupled architecture, where we can plug/play/replace these components.

·        Multi modal communication API's - support for tablets, mobiles, web applications and other third party integration.

·        We need an architecture that scales well (horizontal) with addition of devices; should have high availability and fault tolerance features. Should support cloud hosting.

 

4.0 Architecture:

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4.1 Device Layer:

Sensors, Actuators, Bar code reader, RFID readers, wearable, smart meters, GPS locators, mobile phones, google glass, biometric sensors, drones are examples of devices in this layer. They communicate in various protocols covered in Section 1.2. Gateways can act as protocol translators, data aggregators, data cache (where connectivity is intermittent).

 

4.2 Data Ingest/Processing Layer:

The data from the devices is accessed over various protocols as mentioned above and protocols with lowest overhead over payload - MQTT and CoAP are clear winners on this account.

We can have an implementation of Agent Hub running in the device/gateway layer, which would collect the data from devices and send it over to a Central Registry (which is the case with Bosch M2M platform) in the ingest layer. 

We need a filter, adapter, transformation are part of data ingestion; Complex event processing (CEP), Business process Modeling (BPM), Business Rules Modeling (BRM) are in the Processing layer. A pub/sub model is best for handling data at this layer. Choices could be ActiveMQ, RabbitMQ or cloud bases offerings such as SQS. CEP is available in many flavors - open source tools such as WS02, ESPER; enterprise tools from Oracle etc; also, Storm/Spark from Hadoop world. Data in flight Analytics using R or any other similar tool can be done in this layer. Volume/Variety will decide the selection of tools in this layer.

4.3 Data Storage and Access Layer

SQL and NoSQL data bases are candidates for storing data. Depending on the volume HDFS can be used as well.

Recommended data access to the consuming applications is over REST API. This layer of abstraction enables access across different data sources.

4.4 Applications

Employee health and Safety, Remote Monitoring, Track and Trace, Traceability, Predictive Maintenance, Risk/Fraud Analytics, Digital Farming, Industry 4.0, Connected Vehicle Technology, Smart Home/Factory/Warehouse/City are some of the applications in this space.

4.5 QoS/Monitoring

he Quality of Service is across all the layers - it should support non-real time, soft real time, hard real time depending on the application requirement. Architecture should support measuring the latency, data loss, ability to handle duplicate data, late arriving data, identify error in data. Instrumentation should be provided in all the services in the system that is capable of reporting the health, resource utilization, efficiency etc.,

 4.6 Security

Security risks associated with using inherent internet and risks that are associated with IoT devices should be addressed. Best practices such as encryption, Identity and access management with OAuth/OAuth2 (tokens rather than username/password) are suggested. XACML based Attribute/Policy based Access control are appropriate.   

5.0 Conclusion:

This article covers the overview architecture of internet of things. We will elaborate on the individual layers of the architecture in the coming articles.

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