Quality Assurance and IOT

Been involved in the testing and utilizing of many IOT devices for the smarthome, and in the process found many bugs in process and software,     So quality assurance is a big deal to hope to effectively test and deliver robust systems.   If your IOT device is gathering data from sensors, a common approach, consider the implications for embedded downstream ML, analytics and decision making.

The relationship between QA and the success of IoT devices

Posted by Hemanth Kumar Yamjala on February 1, 2021 in IOT Central

In an increasingly tech-driven world, the Internet of Things holds a special place. It helps to connect devices and establish communication among them through the use of embedded software. According to Statista, the global revenue projection for IoT devices in 2021 will be worth 520 billion USD. This exemplifies how the Internet of Things is slowly but steadily taking the digital world by storm and is capable of adding economic value to diverse markets. At the core of such devices are the sensors with embedded software that help to automate processes, connect domains, and deliver superior user experiences. The terms like smart homes and smart cities are no longer in the realm of fiction but a reality where data mined from myriad sensors are processed to perform specific activities for delivering great user experiences.

The Internet of Things (IoT) is a network of connected devices through sensors or embedded technologies that interact with the external and internal environment to arrive at intelligent decisions. The IoT ecosystem comprises three core components:

Things: The real-world physical objects or devices containing sensors and embedded software to interact or communicate with the external environment.

Communication: The networking component allowing communication between IoT devices and the external environment comprises protocols. 4G for LAN, Wi-Fi for LAN, and Zigbee, BLE, and ANT+ for PAN.

Computing: It is executed on a computer or mobile device at two levels – to take intelligent decisions within the ecosystem and to create a vital link for data analysis. By analyzing mined data, the computing component makes intelligent decisions possible.

A real-life example related to the three components is the car’s navigation system. Here, the ‘thing’ is the actual hardware present in the console, which ‘communicates’ with satellite readings to ‘compute’ and deliver data for the driver to take notice.

Since the IoT ecosystem can have real-time implications for individuals, enterprises, and entities, IoT device testing should be accorded top priority. The critical role of the Internet of Things QA testing is based on validating the software and hardware components and checking if the transmitted data leads to real-time intelligence. Let us understand why it is important to apply QA to the IoT ecosystem?  ... " 

Meaning of Quality Assurance

This CW article led me to the meaning of QA ... " ... Long undervalued, quality assurance is in the limelight -- and QA pros in demand -- following the disaster of the HealthCare.gov website rollout. ... "  Quality assurance here is more about development.   Our own experiments combined the two elements.  Build a key system/process so that when it does fail, even rarely, you can readily bring experts or other resources to keep things running.  QA is a combination of design and resources.

FTC to Investigate the Internet of Things

In GigaOM: It should be regulated. But I have my doubts that governments will do a good job.  Think too of the implication of governments knowing the location, status and interaction of all tagged things. Has to be a stronger privacy assurance.  When a thing, a medical device, communicates with your Doctor, who could possibly be listening?

Crowdsourcing Grocery Shopping

Called Instacart:      Personal Shopping remains a narrow market.   With crowdsourcing come quality assurance issues.   " .... In contrast to the high overhead of Webvan, which had its own refrigerated warehouses and a fleet of trucks, Instacart is built on a crowdsourcing model. Its 10 full-time employees, mostly engineers, work from a small office in San Francisco’s South Park neighborhood. Its app sends customer orders to about 200 independent Bay Area personal shoppers, who receive commissions based on the number of items and orders they deliver in their own vehicles. The app features detailed maps of local supermarkets and can direct the personal shoppers to specific aisles. Founder Apoorva Mehta says Instacart’s “secret sauce” is its fulfillment software, which allows the online retailer to combine orders placed at different times and fill them from different stores—supplementing frozen food from Trader Joe’s with fresh fruit from Whole Foods (WFM) and cereal from Costco (COST). Customers assemble their orders with lengthy drop-down menus on Instacart’s website or app. ... " 

Cheesecake Factory Restaurant Analytics

The Cheesecake Factory delivers an exceptional brand experience with IBM Big Data analytics http://goo.gl/Mfn5c #IBMPWLC

I had yet to see a restaurant application of this type, so it was fun to see the linked to video.  I have eaten at the Cheesecake Factory a number of times, and when in a business I always look for indications of data gathering and process applications. It is always interesting to see how this can be linked to midsize business needs.

Cheesecake Factory is a global company that serves 80 million guests a year, with over 200 menu items, hardly a small company.  How do they deliver a unique brand experience that meets customer expectations?  In  my own visits I was singularly impressed by their process, though I could not see how they used data to achieve it.  Clearly they had dound out how to deliver quality consistently. This short video and provided a look at the data back of the house.   A chef myself, I wanted to see how big data contributed to the experience.



As every chef knows, its all about the quality assurance of what goes into the food.   They also seek to make the experience as consistent as possible, regardless of the location of the restaurant.  IBM and partner N2N Global  have put together a strategy that provides " ... ERP, Tracebility, Quality and Food Safety, and Business Intelligence Software integrated in a single solution for the Food Supply Chain. ... " 

This is by its nature a big data problem. Multiple sources of supplier information about key ingredients that is being updated constantly. Volatile data because it needs to address such questions as traceability and food safety.  Usage information in each region to assure the highest quality ingredients to meet restaurant specifications.  Feedback from the users of the ingredients to assure continued quality.   Consistent  analysis of costs to assure profits.  That's lots of data, and lots of places where that data can be used to streamline the process. Analytics.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.

Global Food Supply Chain Improvement and Protection

I just got a chance to interview N2N Global.  A business intelligence and analytics provider for food supply chains.   Aimed at Food Safety, Quality Assurance and supply and product traceability systems. Their CEO Ernesto Nardone is a former IBMer who worked in the area of using expertise-based systems that led to some of IBM's recent AI applications like Watson.

N2N is using IBM's Cognos to do Business intelligence and related basic analytics to utilize the huge amounts of data, millions of records per month, for the Food Coop Cherry Central.   Cherry Central has a space on Facebook which succinctly describes the work done.   See also this press release for more detail, which emphasizes how this relates to consumer food safety.

In particular I was interested in how this data can be connected to the decision process.  They are doing this now by looking at outlier analysis, reporting and alerting.  A likely good next step is to include process models that specify set of rules based on the knowledge of food experts, and also including quantitative analytical processes that can find patterns, predictively forecast future situations and bring together this intelligence to make next steps safer and more efficient.  Bring on the tool set to make this happen.  That leads to smarter commerce.

Cherry Central and N2N won IBM's Engine of the Week for midsize businesses.    A part of the Midmarket initiative. " ..... This honor is given to midsize businesses that have transformed themselves using insight and IBM technology.... " .  Their transformation has started with the automating the large amount of paper that was once needed to run any complex business, large or small.  It will be instructive to continue to follow their effort as food knowledge is included to make the enterprise truly intelligent.

Software Quality in the Global Supply Chain

In ECommerce Times:  Part of a conversation I have been having recently on supply chain design.  How is the right data brought together in the quantities required to support the analytics required?   " ... Successful processes create value for both parties involved. For a company purchasing software components, implementing quality assurance methods can improve and support the brand by ensuring that externally sourced code is held to a high standard. For a supplier, it's an objective way to represent the quality of the product and strengthen the relationship with the customer ... "