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Why Your Business Needs Non-Stop Software Security

software security

Have you ever lost 30 minutes of creative works on your computer? Or has it suddenly occurred to you that you have a great piece of data that will augment a business proposal, only to discover that the data is missing? Oh – how frustrating!

Data loss occurs for various reasons

  • 78 percent – Hardware or system malfunction
  • 11 percent – Human error
  • 7 percent – Software corruption or program malfunction
  • 2 percent – Computer viruses
  • 1 percent – Natural disasters
  • 1 percent – Other acts.

Impact of critical data loss across global enterprises

Meanwhile, research reveals that global enterprises lose a whopping sum of 1.7 Trillion dollars due to data loss and downtime. And this excludes disruption of business activities, the loss of productivity, the diminished customers’ loyalty, the break of investor’s confidence, the cost of time spent on reconfiguration, and lots more.

While it may be difficult to establish a precise impact of data loss and downtime on organizations, it’s obvious that it would, sure, have a radical negative effect.

With a seamless increase in web adoption and constant acceptance of new technologies, both small and large scale businesses have been able to share important data as regards their products and services — using the web-as-a-service, Waas.

Hackers can compromise corporate networks

Meanwhile, hackers are seriously looking for ways to compromise the corporate network of several industries. As a matter of fact, the Verizon Data Breach Report reveals that 15.4 percent of reported incidents were related to malware and web application attacks.

Also, many of the most fatal breaches that covered the media in the past few years were caused by web-application and software security vulnerabilities. A very good example is the Equifax breach.

Simply put, “business websites possess the greatest threat to organizational security.�

Watch your data loss due to website and software patches

A sizable number of business sectors have experienced (or will experience) data loss due to website and software patches. This has reduced the efficiency and productivity of these organizations to the barest minimum. Little wonder why 70 percent of firms that experience data loss run out of business within one year of the attack. (DTI)

You may not know when the next attack could occur, but taking proper precautions can hamper or completely abolish a hacker’s attempt at gaining access to your business website.

Why your business website needs software security programs

1. Monitoring and detection

How satisfying will it be to have effective and efficient protection of your business website against the worst threat ever?

Using a software security program means your business web is on the watch, and any single vulnerability will be detected on the spot.

Software security companies provide website security scanners that check your website at predetermined intervals to detect any malicious action. You can rest assured that you’ll receive an alert as well as the next line of action when this happens.

Not only does website security monitoring protect you and your customers, but it protects your website’s rankings by checking a variety of different blacklists, and notifying you if you have been placed on one.

2. Performance optimization

Do you know that Google, Bing, and other search engines, use site speed as a ranking factor?

We live in a world where nobody is ready to wait for anything. We have become accustomed to business websites and apps working instantly and perfectly. As a matter of fact, a study reveals that 47 percent of customers abandon business websites that take more than 3 seconds to load!

Performance optimization is a major reason why your business website needs software security programs. Besides SEO, a site performance typically revolves around reducing the overall size of web pages. This includes the size of the files and perhaps, more importantly, the number of them.

3. Fast disaster or data recovery

In an age where data is king, the idea that data can be lost so easily should be enough to encourage businesses to take steps to protect it.

The U.S National Cyber Security Alliance found that 60 percent of companies are unable to sustain their businesses over six months after a data breach.

According to the Ponemon Institute, the average price for small businesses to clean up after their businesses have been hacked stands at $690,000; and, for mid-sized businesses, it’s over $1 million.

Recent events have proven that nobody is safe from the threat of data breach — not large corporations, small businesses, startups, government agencies or even presidential candidates.

When a crisis occurs, there would be one of the two scenarios:

  1. You run a licensed app/piece of software and the vendor is responsible enough to issue an update/patch when issues are reported.
  1. You run a custom software delivered by your software development company and you ask for the software to be enhanced. That is going to take just as little time but chances are your custom software will ever be hacked is drastically lower. Just because the hacker would need to spend even more time looking for vulnerabilities than the AQ department of your software developer.

Even if your website is secure, a misconfiguration or simple mistake can lead to data loss. Only a sure backup plan can save you if your custom files are overwritten or tampered with.

A website security provider can offer secure remote storage, automatic backup scheduling, and an easy recovery process without disturbing your workflow. Decent software companies offer a fast and easy way to recover all the files you need in a very short time.

4. Regular software update

A software update, also known as a service pack is a periodically released update to software from a manufacturer, consisting of requested enhancements and fixes for known bugs. A software update is mainly to present security vulnerabilities in their existing items.

You may think that you do not have anything to protect on your business website but the reality is that security software gives protection for your data. Data is valuable for the sustenance of your business. Top software security programs keep your data secure by providing regular updates to keep you safe from malicious attempts.

Summing It Up:

Since 60 percent of businesses that are affected by a breach in business websites or data will shut down in 6 months, cybersecurity experts, thereby, recommend that you have an effective software security program to save yourself and your business from this calamity.

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Connected Devices connected security HVAC Internet of Things IoT security

Addressing Security Challenges in an IoT Dominated World

security

Adding connectivity with a degree of intelligence to household appliances gives rise to the Internet of Things (IoT). Integration of these inter-connected appliances, with our daily routine, inside our personal spaces, is resulting in smart homes, and the adoption is already exponential. Here is how we are addressing security challenges in an IoT dominated world.

Many industries are deploying the IoT concept, such as security and surveillance systems, home appliances, manufacturing, automotive, and recently we also experience numerous innovations in the HVAC industry (cielowigledotcom – HVAC tech). All players’ goal is to provide connectivity plus automation, resulting in comfort and even energy savings.

Smart homes promise an automated living experience, with in-built convenience and an efficient style of living. As per IDC projections in 2015, there will be 50 billion connected devices by 2020, with a market worth 1.7 trillion USD. This widescale acceptance of IoT is a fascinating part of the future. It bodes well for the times to come for the smart home industry. But with all good things, there is a catch. Security of data is the most significant risk to such large scale integrations. Moreover, preventing any backdoor entries into a secure home should also be an emphasis on IoT security.

Smart home devices’ mass use provides a larger pool for potential hackers and data attackers to target, resulting in a significant disruption of service, financial loss, and physical loss instead of promised convenience and energy savings.

Erosion of confidence in smart home appliances through security risks is a stark reality for the IoT industry. It would consequently lead to a slowdown in the adoption of smart home products by consumers.

IoT Vulnerabilities

Wi-Fi connected devices create a great volume of sensitive data, creating an inherent risk of data and identity theft, device manipulation, and server/network manipulation, and providing many avenues for hackers to exploit.

As per Open Web Application Security Project (OWASP), IoT vulnerabilities include inherent insecurities in the web interface, mobile interface, cloud interface, network services, and firmware. The vulnerabilities also include insufficiencies in authentication/authorization and security configuration. The lack of transport encryption, privacy concerns, and poor physical security also adds up to the list of vulnerabilities.

Limited memory and computational power of microcontrollers is another challenge that is unique to IoT. Both these components are essential to convert dumb appliances into intelligent connected devices. Implementation of security at the device level is a big problem for IoT solution providers. They have to keep in view the balance that needs to be maintained between the security and marketability of the end product.

Often, resource constraints within the design of the product do not allow sufficient computing resources, which are necessary to implement strong security. Consequently, many devices are unable to provide advanced security features. As a case example, temperature and humidity sensors cannot handle advanced encryption protocols and various security features.

Even over the air (OTA) updates are not utilized, with many IoT devices used in a “set and forget� mode. High-end manufacturers are the exception to this, though. They can provide regular FOTA updates and a robust security mechanism all the way from the cloud protocols to on-device safeguards. Other manufacturers are not so forthcoming, prioritizing low-cost development and a faster timeline for conception to sale.

Strategy to Mitigate IoT Vulnerabilities

An all-encompassing strategy is to mitigate any potential vulnerabilities from design conception to end product. Post-sale software updates are a critical part of aftersale support. Without being hampered by cost restrictions, a security-centric approach needs to be adopted. The strategy must include proven security practices, prioritization of security measures, and transparency across the whole eco-system.

Another major issue that needs to be addressed in the amalgamation of legacy assets with modern technology. The security challenges of today were not kept in mind when older generation devices were made. Outright replacing the legacy structure with new-generation devices is a very cost-prohibitive venture. This is why smart home providers are more focused on retrofitting already installed equipment with plug-and-play devices and sensors.

But the cross-link between a legacy device and smart sensor will inevitably leave a little gap in the proverbial door and can be exploited by those with malicious intent.

Time restrictions are also a cause for concern. Many smart solution providers only cater to updates for a few years, after which their after-sale support becomes only rudimentary. With devices running around for a much larger time period than support provision, this can be a security lapse. Achieving security at par with the current standards can be challenging without assistance from manufacturers.

Industry Acceptance

A major component of security protocols and networking is industry-wide acceptance through well-established standards and procedures. Although multiple independent security frameworks operate in somewhat isolated bubbles, a single, comprehensive, industry-wide standard needs the hour. Major manufacturers and service providers utilize their own internal protocols.

To develop these protocols, a large number of resources have been put in. But smaller companies are at a disadvantage. They have to resort to making do with third-party frameworks, which are often not up to the mark. Moreover, they can also be incompatible with other major players in the industry. Due to this, not only is security an issue but also inter-operability.

Putting IoT Security Strategy Vehicle into Action

The IoT solution providers have to involve security issues at all stages of the IoT cycle. Emphasis should be on cybersecurity. Security begins at the design stage with a special focus on threat modeling, secure component selection, component adaptability to future security measures, and finally, resilience testing. The FOTA functionality is a must for remote updates, failure patching, and data protection in case of security breaches.

The options of standalone operations in case of connectivity problems can also give greater confidence to users. The manufacturer must also educate the users for setting stronger user preferences through user configurations.

The users on their part can reduce the risk of security breaches by using strong passwords for device accounts and Wi-Fi networks, use of stronger encryption method when setting up Wi-Fi networks such as WPA2, disabling the remote access to IoT devices when not needed, and disabling features that are not currently in use like location information.

Privacy is an Essential Part of Security

Privacy issues have lately been at the forefront of the discussion on networking. IoT has the potential to provide unprecedented amounts of personal information. Such information may land in the hands of information abusers. OEMs would need to provide privacy policies on how they handle such data. They should also adopt best practices to avoid reputational damages and adherence to regulatory requirements.

IoT is here to stay. The sooner this realization comes in –the better it is for both the consumers and smart solution providers.

A robust framework is needed by the industry to ensure that consumer confidence in IoT is not hampered in any way. Rather, the focus should solely be on providing the utmost in convenience and comfort to the world.

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All You Need to Know About Industrial IoT

industrial IoT

The term IIot, or Industrial Internet of Things is used to refer to the industrial applications of the Internet of Things. We are talking about using the technology in anything from the machines in a factory to engines inside a car – these are all filled with advanced sensors equipped with wireless technology. These can collect and share data enabling extensive use of digital intelligence across various industries. Here is all you need to know about industrial IoT.

Industrial IoT Use Cases

Applications of Industrial Internet of Things

Process Automation

One of the best use cases of the industrial Internet of Things has been the process of automation across many industries.

With the help of smart sensor networks that can connect with each other through cloud computing systems, industries have been able to automate a number of their crucial processes and achieved a higher level of productivity and efficiency.

It has provided better control of the process and has significantly decreased the number of people required to get the job done.

Restaurants have been using process automation to get rid of food wastage. With the incessant developments in IoT technology, the evolution of traditional industries will become inevitable.

Predictive Maintenance

To be able to run effective predictive maintenance, you will require the processing of large amounts of data and will have to run sophisticated algorithms on it. This cannot be implemented within SCADA.

Therefore, an IoT-based solution, that can store terabytes of data and can still run the required machine learning algorithms, was introduced on several computers to keep a tab on the progress and have prior knowledge of industrial equipment failing.

A robust IoT-based predictive maintenance ecosystem has become essential for modern industries. The architecture consists of field gateways, cloud gateway, streaming data processor, a data lake, and machine learning algorithms.

Asset Tracking

Asset management and tracking have become much easier and efficient as an IoT-based digital asset tracking can now connect different components of the business chain and create an integrated strategic system.

We are talking about connecting multiple stakeholders, processes, workforce, and assets to a single digital IoT-driven system that provides a unified view of a process now backed by effective data analytics.

Industrial IoT can be added to your traditional solutions to make them more intelligent and get automated workflows, real-time alerts, dynamic edge control of assets, cross-domain analytics, real-time visibility and more.

Fleet Management

IoT-enabled solutions have revolutionized fleet management by making the process more environment-friendly. An IoT solution can help monitor the carbon emissions and monitor the service condition of the fleet.

Industries can build sensors-equipped fleets that can send automated signals and warning alerts like system failure, low battery, engine temperature or maintenance, and more. IoT-based solutions can also regulate driver behavior which can result in improved fuel efficiency.

The fleet manager can keep a tab on all such data and get actionable insights. IoT solutions allow managers to implement changes and make data-backed decisions.

Technologies in Industrial IoT

Kubernetes, k8s

1.   Front-End Edge Devices

The sensor data is what industries need to get important insights into their processes. This makes the hardware containing the sensors a crucial component of the IIoT system.

Many front-end devices and control devices are installed to capture critical process-related data and analyze it in real-time. Therefore, the devices must be reliable and of very high-quality so that the stream of data captured is consistent and accurate as well.

Some of the traditional industries already have devices installed that collect data for them. It will be easier for them to develop their process and make it IIoT-enabled.

However, if your data collection process isn’t there in the first place, you will have an opportunity to install a modern set of tools for your industry. It’s quite a win-win situation for you because your process has to evolve some or the other day. So, why not now?

2.   Connectivity Technology

Industrial IoT solutions rely heavily on wireless technologies to transmit and receive commands from the cloud. You have got Wi-Fi, Bluetooth, mesh networks, cellular networks, LPWAN technology, and what not to choose from.

Before going forward with technology, you should pen down your requirements. Different connectivity technologies have different range and capacity. It’s not just about wireless connectivity. Many of the industries have established IoT devices and connected them through wired systems as well.

If your setup allows for a wired connection, you should go for it as it will save a lot of money and provide even better reliability.

3.   Industrial IoT Platforms for Data Analytics

Once your setup is complete, you can focus on the data analytics part for which you will software that can analyze the collected and transmitted data. The software can be trained or programmed to make decisions for the processes.

The software is called the industrial IoT platform and it helps connect the hardware, access points, and data networks and also the end-user applications. All the data and command management happens with the help of real-time task management and data visualization.

The IoT platforms act as the middleman between the data and the processes or applications. One of the most common issues you will face is that you won’t get an off-the-shelf software solution for your process.

You might have to build the solution for yourself or buy the whole end-to-end software solution and the hardware and align your industry setup accordingly.

Wrapping Up

There you have it. We have discussed what you need to know to understand IIoT in depth. Let us know if this piece of content provided you with great value in the comment section. We’d love to hear from you. Also, since you are here, don’t forget to follow us on social media, we bring all the latest news and updates from the world of technology, startups and more.

Further Reading

What can we expect for IoT in 2020?

Is it time to implement IoT in the warehouse?

Top 5 areas where companies want IoT solutions?

Will companies embrace digital transformation?

Demystifying the 8 core myths that surround the Internet of Things

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The Future of IoT Devices: What it Means for Connectivity

iot devices connectivity

A shift from the cloud to the edge might signal a real autonomous revolution in IoT connectivity. While previously, we witnessed how cloud computing allowed for centralization and collaboration — edge devices are all about abilities to work offline, autonomously, without sending data to the cloud for processing and storage. Here is the future of IoT devices and what it means for connectivity.

Does edge connectivity mean we’ll outgrow Cloud-based connectivity and that we are heading towards the era where edge computing takes central place? Good question.

When we say IoT, what do we mean?

When the Internet of Things term was first introduced around 20 years ago, it alluded to the Internet, which was a big thing back then.

The concept of miniature sensors sending and receiving the data from the cloud over WiFi was huge and breathtaking. When talking about the Internet of Things today, we mean a remotely controllable ecosystem of devices connected to the cloud and to each other with some kind of connectivity.

Most importantly, these devices must be able to perform some actions.

In terms of smart homes, we talk about smart speakers/voice assistants like Alexa or Google Echo, that can issue commands to switch on the lights, tune the conditioner or order a pizza at the nearest Domino or Pizza Hut.

The connected-concept can be rigged up to smart systems controlling commercial real estate across a variety of scenarios. When talking about the Industry 5.0 factories and other industrial installations like wind farms, the IoT means an ecosystem of devices capable of communicating with each other and able to perform some actions based on the commands received.

However, as the technology evolves, the meaning of the terms like IoT and connectivity broadens, and we must take into account this updated image of what connectivity is today — and what it will become in the future.

Why IoT is not enough anymore

The concept of IoT as an independent development entity centered at the gathering, sending, and receiving data has overstayed its welcome. In short, the IoT, in its original meaning, is long dead.

Such systems must provide much more business value to be feasible nowadays. They must enable the users to analyze the data gathered and perform meaningful actions based on the results of this analysis.

The focus of the IoT and connectivity has shifted from the brilliance of myriads of sensors to the value of data they gather. The data, not the sensors, is king. There are surely more sophisticated sensors to come, but their main value is the data they can gather — and the actions we can perform based on this data.

Of course, we only need a smart kettle to be simply switched on when we are close to home so that we can get a cup of tea or coffee faster.

But an autonomous car must be able to react to the changes in the road situation around it, and a smart factory must be able to adjust complex working scenarios should something go awry.

Therefore, the IoT alone as a concept of Digitally Connected Assets, or DCAs, is not viable. It cannot exist in a vacuum, as such systems must be able to process the data quickly and make use of it either through analytics or through issuing some commands.

Performing the task in the cloud means too large latency — so we need something faster. “Faster” is where the edge computing concept comes into play.

Edge computing — the next stage of the IoT evolution

The edge computing term refers to the concept of local computational nodes that form the hearts of the sensor networks in some locations. These sensor networks can be a server node on a factory or in an agricultural complex, an aforementioned Google or Amazon smart home system.

The system can also be the smart utility control system for commercial real estate like malls or office buildings.

In short, edge computing provides a Local Area Network connection for sensors, enabling lightning-fast data transmission. It is also connected to the cloud to enable centralized data gathering and analysis, storage of historical data, and training of AI/ML models on this data.

But most importantly, edge computing nodes provide sufficient computing capacity to host Artificial Intelligence / Machine Learning algorithms locally, which allows these models to issue the needed commands based on the data received from the sensors.

Let’s imagine the fully-automated Industry 5.0 factory equipped by various sensors (movement, temperature, humidity, etc.), a fleet of robots, and multiple actuators.

The robots perform the production operations while the sensors monitor the situation — and one sensor signals the drastic overheating in one of the conveyor belt engines.

The local edge computing node receives the signal, and the AI/ML algorithm running it enacts one of the response scenarios. The scenario can shut down the engine, apply the coolant if possible, disconnect the engine from the conveyor belt (if there are backup engines – start them).

To minimize the production disruption — or reroute the flow of production to other conveyors. All of the functions are done within milliseconds, preventing fire and saving the manufacturer millions in potential damage.

To make operations possible, the edge computing nodes must have three key abilities:

  • To control the processes in the physical world. Edge computing nodes must be able to gather the data, process it, and enact some response actions.
  • To work offline. Deep underground mines or sea installations far from the shore can have issues communicating to the cloud, so their systems must be able to operate autonomously.
  • Zero-second response time. With automated production or utility operations, a delay in several seconds can results in huge financial losses, so the response scenarios must be enacted and executed immediately.

The future of IoT: cyber-physical, contextual and autonomous objects

As we can see, the meaning and the value of the IoT have shifted from the ecosystem of interconnected devices for gathering data to the ecosystem of devices able to gather the data, process it, and act based on this data. Therefore, we can define three main categories of existing and future IoT devices:

  • Cyber-physical objects.

    The sensors that collect physical signals and transform them into digital data. Think of smart wearables that track our vitals, digital printers, many machine-to-machine and telematic equipment, various smart home systems like thermostats, etc.

    All the consumer devices that can perform only a single function like switching the light on/off or rolling the blinds up/down also belong to this group.

  • Contextual objects.

    Simple cyber-physical DCAs just provide the data or execute single commands, but more complex systems allow understanding the context in which these sensors and actuators operate and make better decisions.

As an example, let’s imagine an agricultural complex, where DCAs control the irrigation systems or the location and operations of a fleet of automated machines.

By supplementing this with an edge computing node, the farmer can consolidate this data to a single dashboard and augment it with weather forecasts and other crucial information, which will help get much more value of the data and control all the systems effortlessly.

  • Autonomous objects: the highest level of the “gather-process-reactâ€� chain, these systems combine the sensor networks, edge computing nodes, and the AI/ML algorithms to form autonomous objects that take the responsibility from humans to machines. An example is the factory incident we mentioned earlier.

Summing up: call it as you wish — connectivity will not die

We must operate in the real world and use the tools available to us. Basic gateway devices provide ample capacities for data gathering, storing, and processing within an edge computing node.

These nodes enable the ML model in it to take action. Nevertheless, they cannot provide sufficient computing resources for training a model like this, as it requires processing mounds of historical data over hundreds of computational cycles, which can be done only in cloud data centers.

Connectivity is still crucial for connecting edge computing nodes to the cloud, gathering statistical data, training new AI algorithms, and updating the existing ones. It is an integrated ecosystem, where every component plays its role.

What are we going to call this new and exciting ecosystem?

IoT 2.0? Cyber-physical edge computing-enabled objects? The terms itself matters little, while we understand what stands behind it. These objects will have the ability to connect the physical and digital worlds, gather the data with sensors, process it in context with other input, and take actions based on this analysis.

While this ecosystem works and is feasible, it matters little what we call it.

Most importantly, connectivity is still crucial for connecting edge computing nodes to the cloud, so connectivity will never die.

What do you think of the future of IoT and the importance of connectivity? Please let us know in the comments below.

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AI cloud computing Connected Devices Exception management Internet of Things IoT Issue tracking Logistics technology Machine Learning Tech transport

Machine Learning and Exception Management – A Logistics Tech Game-Changer

Exception management with machine learning

There has been a lot of talk about machine learning in logistics management. The idea is simple: optimize, infer, implement and repeat. Here is: machine learning and exception management — a logistics tech game-changer.

What is included in the different pillars of logistics management?

A system optimizes the different pillars of logistics management that include order planning; vendor performance management; fleet capacity optimization (management); dispatch management; in-transit shipment tracking; and delivery management.

Next, the system infers the points or bottlenecks within these pillars (logistical processes) which can be fixed, improved, or enhanced. These inferences or analytics are then ‘implemented’ back into the logistics set-up. The learning mechanics start back from optimization. Over-time the system evolves and improves all the connected logistics management processes. This is machine learning in logistics management.

What is exception management in logistics?

A logistics exception (issue) is a deviation from planned or expected process execution. Here are a few examples.

  • Shipment loads aren’t mapped properly to available fleet options (creating capacity-mismatches and loading/dispatch delays).
  • In-transit shipments are detained at a spot for more than two hours (or are violating service level agreements with speeding or harsh braking).
  • Consignees didn’t receive all the SKUs (stock-keeping units) as per the initial purchase order.

Every transportation management system (TMS) involves some or many human touchpoints. A person supervises these system or process interactions (touchpoints). This can be anything from checking the shipment assignment schedule and ensuring that the handlers are following the planned loading patterns. Similarly, many other touchpoints work to ensure that the gap between plans and ‘actuals’ is minimal.

The goal of exception management is to minimize this gap between planned and on-ground results. Overall, the machine-learning aspect of exception management induces accountability and efficiency within the company’s and logistics network’s culture. This can be with the supervisors, warehouses, freight forwarders, logistics service providers, consignees (distribution points), etc.

 

6-stages of machine-learning enabled exception management system.

The 6 stages are Discovery, Analysis, Assignment, Resolution, Records, and Escalation.

Discovery:

It detects and reports issues or anomalies within the processes. This can be through temperature sensors (cold-chain logistics), real-time movement tracking, order journey tracking (in-scan and out-scan of each SKU), etc.

Analysis:

It analyses and processes the issue or exception as per protocols (or learnings). It categorizes and pushes ahead all exceptions – either to an assignment or to an escalation.

Assignment:

It matches the exception with the right person or department (best-suited to resolve the exception on time).

Resolution:

It tracks the speed and effectiveness of the person’s (assignee) resolution. It moves the ‘resolution’ through multiple criteria and validations before satisfactory ‘completion’.

Records:

It records and analyses each exception right from discovery to resolution. The system processes these records to throw-up insights or best-practices for future applications.

Escalation:

This is an important aspect of dynamic exception management. The system constantly tracks each issue within the system.

  • If at the analysis or resolution stage, the supervisor (or system) deems the issue – critical or complicated, then it’s escalated through special ‘analysis’ and resolution. It mostly includes people with different skill-sets or authority.
  • If the system detects that an issue hasn’t been resolved in its time-frame, it’s again escalated.

Through these 6-stages, the system constantly weeds-out inefficiencies from within itself. It helps propagate a more transparent, accountable, agile, and responsive culture. Furthermore, it helps reduce errors and delays, which, in turn, improves profit margins. A few new-age TMS start-ups, like Fretron, are trying to capture market share using this 6-stage exception management.

Real-world applications of escalation management in logistics

Let’s consider a real-life use-case for an exception management system (EMS) – a fast-growing retailer in India focusing on Tier-2 and Tier-3 cities.

Their biggest challenge was an unorganized logistics (vendor/freight forwarder) network and weak city infrastructure. Even though the retailer had opted-in for total logistics automation, they still weren’t able to implement it to the full extent. The client was looking for a tech-enabled process and culture change.

Let’s take vendor performance management as an example.

  • The EMS helped cut down discrepancies in billing and settlements. A single synchronized TMS was able to track each order (at the SKU level) as it moved through crates, pallets, trucks, cross-dockings, and final delivery. The out-scan could automatically highlight all the missing items.
  • The EMS would process the information and mark the exact point of deviation where the item went missing. This helped with issue resolution and also to plug these operational gaps. It cut down invoice-level disputes and hastened the settlements.
  • The EMS enabled fast and error-free invoicing which incentivized the carriers and freight forwarders to work in a more organized fashion. Through an iterative learning process, the system improved upon itself. It brought a higher degree of transparency and accountability within the logistics ranks (in the company).
  • On the back of machine learning-enabled EMS, the company was able to deliver on-time value (better shelf choices) for its end consumers.

Conclusion: Exception management, in logistics, is a game-changer

EMS successfully bridges the gap between tech-induced efficiency and on-ground employee efficiencies. It’s especially effective in unorganized or traditional markets that are riddled with such ‘exceptions.’

If machine-learning backed EMS is used in the right manner, many mid-level companies can scale fast and improve their outlook within the next five years. At this time of COVID-19, scaling faster may be the only option to save your company.

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Why IoT Needs an Open Ecosystem to Succeed

iot open ecosystem

Imagine if the internet had been built as a closed ecosystem controlled by a small set of organizations. It would look very different from the internet we know and rely on today. Perhaps this alternate version would run on a pay-per-use model, or lack tools and services that have been developed over the years by independent contributors and scrappy startups. Here is why IoT needs an open ecosystem to succeed.

The Open Internet

Instead, of a closed internet — we mostly enjoy an open internet. This is in part due to its origins: the internet was built to be fundamentally open, and this is what has allowed it to grow, change, and be adopted as quickly as it has been. In fact, the trend of an open approach propelling innovation is one that we see repeatedly for emerging technologies.

When it comes to the Internet of Things (IoT), we’re at the precipice of a similar innovation boom as witnessed with the internet.

IoT is slated for explosive growth: by 2021, Gartner expects that 25 billion connected things will be in use, enabling our smart homes, factories, vehicles, and more.

As more and more IoT devices come online, edge computing will become a necessity. Edge computing enables data to be processed and analyzed in real-time for business-critical use cases, such as self-driving cars, safety and security, and industrial automation.

As with the internet, we need an open, consistent infrastructure foundation for IoT and edge computing in order for these technologies to reach their full potential. While the challenges of building an open IoT are different than those we faced with building an open internet, this is an important problem for our industry to solve now, before we witness further fragmentation and vendor lock-in.

Where we are today with IoT

We’re currently in what I like to call the “AOL stage� of IoT—the phase of getting devices connected at scale, and working through the balance of proprietary vs. open approaches.

Back in the 1990s, America Online opened up access to the internet to the masses with an easy-to-use CD; by popping it in, anyone could easily sign up and get connected. However, the tradeoff for this simplicity was getting locked into the AOL ecosystem as the conduit for communication and search.

Over time, users became savvier, realizing they could connect to the internet directly through their ISPs and access more powerful search capabilities (Google, for example). As more people came online through their medium of choice, innovation picked up speed, giving birth to the internet boom and the ecosystem we know today.

IoT is inherently heterogeneous and diverse, made up of a wide variety of technologies and domain-specific use cases.

To date, the market has created a dizzying landscape of proprietary IoT platforms to connect people and operations, each with wildly different methods for data collection, security, and management. It’s like having many different “AOLs� trying to connect devices to the internet—needless to say, this fragmentation has resulted in unnecessary complications.

Companies beginning their IoT journeys are locked in with the vendor they start with, and will be subject to additional costs or integration issues when they look to scale deployments and take on new use cases. Simply put, IoT’s diversity has become a hindrance to its own growth. 

To avoid going down this path, we must build an open ecosystem as our foundation for IoT and edge computing. It’s only when open standards are set that we can scale the commercialization of offerings and services, and focus on realizing ROI.

Open ecosystems facilitate scale

What would an open ecosystem for IoT look like? When creating an ecosystem, there’s a spectrum of approaches you can take, ranging from closed to open philosophies. Closed ecosystems are based on closely governed relationships, proprietary designs, and, in the case of software, proprietary APIs.

The tight control of closed ecosystems sometimes referred to as “walled gardens,� can provide great customer experience, but come with a premium cost and less choice. Apple is a widely cited example of this approach. 

There are open approaches that offer APIs and tools that you can openly program.

The open approach tools enable an ecosystem of products and services where the value is derived from the sum of its parts.

Open-source software like Android is an example; it’s a key driver of a truly open, vendor-neutral ecosystem because of how it empowers developers. Having an open standard like Android’s operating system for developers to build upon not only promotes further innovation but also bolsters a network effect. 

To fully grasp the business trade-offs of closed vs. open ecosystems, let’s compare Android and Apple’s iOS. While Apple provides a curated experience, Android device makers have less control over the overall experience through deep software/hardware integration, and therefore need to find other differentiators.

Nevertheless, openness facilitates choice and scale—Android has over 70 percent of the global mobile OS market share. Even with Android’s openness, providers like Samsung have still been able to carve out market share by investing in innovation and a broader device ecosystem strategy.

An open future for the IoT

The IoT can have as great of an impact as the internet has had, but generating hundreds of closed, siloed ecosystems dictated by vendor choice is not the path to scale. A bright future for IoT is dependent upon our ability to come together as an industry to build an open ecosystem as our foundation.

Across hardware, operating systems, connectivity, applications, and cloud, we must bridge key elements and unify, rather than reinvent, standards in order to empower developers to focus on value creation.

Commercial offerings built on top of that open foundation may very well take a more “closed� approach; however, starting development with an open foundation will always provide the most scalability, flexibility, and transparency to maximize options for the long term.

Open-source collaboration is an excellent accelerator for this open foundation. The Linux Foundation’s LF Edge and Kubernetes IoT Edge Working Group, and the Eclipse Foundation’s IoT and Edge Native Working Groups are just a few of the initiatives exploring architectures and building frameworks to unite industry efforts and enable IoT and edge computing ecosystems to scale.

As they say, the whole can be greater than the sum of its parts, and I look forward to seeing the immense potential of becoming a reality when we have a common foundation to innovate on.

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5 Technologies a Trucking Business Must Be Aware Of

trucking business

In long-ago-days, the trucking businesses and fleets may have refrained from adopting new technologies because of the inherent resistance to change. However, that attitude has changed drastically — especially recently. Today, more and more truckers are opening up to the use of technology as an integral part of their work. Here are five technologies a trucking business must be aware of.

Are you among the truckers who are using the best technologies?

Here are the top 5 technologies for truckers to be aware of. Know more and apply these to your advantage.

Electronic Logging Devices (ELD)

With the deadline for the ELD mandate 2019, 16th December, fast approaching, all truckers are now quite aware of this technology and are working hard to adapt to it. The ELD consists of an electronic logbook to track a driver’s ROD (Record of Duty) status, among other functionalities.

The advanced technology of the ELDs ensures less chance of tampering besides reducing the costs of fleet management and ensuring 100% compliance. At this point, it’s one of the most important technologies for trucking businesses to be aware of.

However, before you invest in an affordable ELD for owners and operators, make sure that it is in compliance with the FMCSA regulations.

Trailer Tracking Systems

Many trucking companies are increasingly adopting this technology to manage their assets and avert losses, especially in high-theft areas. Besides installing a GPS transponder in your vehicles, you can also take advantage of wireless technology to feed location and other data directly into the fleet management system.

Be it Blackberry’s radar tracking system or the Asset Manager by MiX Telematics, the trailer tracking technology is here to make asset management hassle-free for the trucking businesses.

Some ELD solutions also include elements of Fleet Management that could work like a charm for smaller fleets too.

Collision Avoidance Technology

No matter whose fault it is, the consequences of accidents are always severe, causing loss of lives and goods. Hence, trucking businesses should invest in effective collision-avoidance systems to ensure safety on the road.

This technology aims to inform the driver about any obstacle on the road ahead, as well as prevent possible crashes by taking emergency actions.

You can opt for a range of efficient collision-avoidance systems like the OnGuard system by Meritor WABCO, Wingman Fusion by Bendix Commercial Vehicle System, and Drivewyze.

Platooning Technology

Truck Platooning is recently gaining a lot of attention in the fleet industry. The process involves installing advanced driving support systems in the trucks and using the same to communicate with each other and move in the form of a platoon.

This initiative to group vehicles, being driven by smart technologies, not only improves communication and streamlines the transport process but also reduces the chance of accidents with the help of advanced braking and accelerating systems.

Truck Platooning is also looking to adopt automated driving technologies, and according to the biggies in the transport sector, this will eventually help the industry realize its long-time dream of self-driving vehicles. Thus, it could also resolve the issues caused by the shortage of truckers.

Temperature Tracking Software

One of the most interesting trucking technologies that have gained prominence in recent years is temperature tracking and record-keeping for food and beverage transport.

The Food Safety Modernization Act has mandated that all carriers and suppliers should follow a set of important regulations and proper sanitary requisites while transporting food and related products.

It has become necessary to maintain appropriate temperature and moisture conditions even inside the trucks to ensure safe delivery and maximum compliance to the regulations. The temperature tracking technology thus helps the fleet management businesses to track and record temperature conditions inside the vehicles and take corrective actions whenever deemed necessary.

Most of the temperature tracking software in the market aspires not only to fulfill proper temperature requirements in the refrigeration units but also to send the information to the fleet management system tied to its GPS location.

The supply chain and trucking industry are going through a massive change, most of which is caused by their changed attitude towards adopting improved trucking technologies to better the system.  Be it installing ELDs in their vehicles to comply with the ELD mandate 2019 or taking the help of advanced collision-avoidance technology, both business owners, and truckers are leaving no stone unturned to grow and prosper.

As always, some regions inspire others to follow suit. For instance, owners and owner-operators of mid-sized and small fleets are at the forefront of investing only in the best ELD for trucks in California that are in compliance with the FMCSA standards.

If you have a trucking business, or even if you are a trucker yourself, staying updated with these technologies will help you drive into a better future. That’s a safe bet.

Image Credit: Quintin Gellar; Pexels

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