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Cloud cloud computing edge computing Fog computing fog vs. edge IoT

Fog Computing and Its Role in the Internet of Things

Fog computing refers to a decentralized computing structure. The resources, including the data and applications, get placed in logical locations between the data source and the cloud. One of the advantages of fog computing is to keep many users connected to the internet at the same time. In essence, it offers the same network and services that cloud-based solutions provide, but with the added security of a decentralized network.

Difference Between Cloud Computing and Fog Computing

Cloud Computing

Cloud computing refers to the provision of computing and storage resources geographically distributed. Computing can occur over a variety of platforms, including public cloud and private cloud.

The cloud-computing platforms offer the opportunity to share and mix the workloads among the users over a scalable system. Cloud computing is essentially the ability to store and regain data from an off-site location.

Cloud computing is one of the main reasons conventional phones got “smart.” Phones don’t have sufficient, built-in space to store the data necessary to access apps and services. All the data is transmitted from and to the cloud to provide the services we need. Still, cloud computing technology has a challenge – the bandwidth constraint.

Fog Computing

Fog computing will be dominating the industry in the near future. The domination of Fog will be driven by a need to gather data closer to the source of the data (the user device). Devices are not able to perform the necessary processing in the cloud and the devices are physically constrained (low power and small size).

The ability to process the data locally is more important than in the past because fog computing increases the data’s security. With the evolution of the Internet of Things, more and more devices are being added to the network. Each device is wirelessly connected for data transmission and reception.

Fog computing is about how efficiently data is stored and accessed. Fog computing refers to the networking of the edge computing nodes dispersed in a network so that they can be geographically distributed but still provide an organized communication between those nodes.

The use of fog computing involves a complex process of interconnected edge devices. The edge devices include sensors, storage systems, and networking infrastructure that work together to capture and distribute data.

However, the flexibility of fog computing and its ability to gather and process data from both the centralized cloud and the edge devices of a network make it one of the most useful ways of dealing with the information overload we face today.

fog computing                                                                                                                                                                   Image Credit: nikhomk panumas; pexels

 

Are Fog Computing and Edge Computing the Same Thing?

Fog computing is also referred to as “edge computing.”  Edge computing is designed to solve issues by storing data closer to the “ground.” In other words, edge stores data in storage devices and local computers, rather than running all the data through a centralized DC in the cloud.

In essence, fog computing is responsible for allowing fast response time, reducing network latency and traffic, and supporting backbone bandwidth savings in order to achieve better service quality (QoS). It is also intended to transmit relevant data to the cloud.

IDC estimates that about 45 percent of the world’s data will be moved closer to the network edge by the end of 2025. Fog computing is claimed to be the only technology that will be able to withstand artificial intelligence, 5G, and IoT in the coming years.

Another IDC study predicts that edge devices will generate 10 percent of the world’s data even in 2020. Edge devices will fuel the need for more effective solutions for fog computing, resulting in reduced latency.

Edge Computing

Edge computing is, basically, a subset of fog computing. It refers to the data being processed close to where it emerged. Fog computing allows for more effective data processing, thereby reducing the possibility of data latency.

Consider fog computing as the way to process the data from where it is generated to where it is stored. Edge computing refers only to the processing of the data close to where it is generated. Fog computing encapsulates the edge processing and the network connections required to transfer the data from the edge to its end.

With edge computing, IoT devices are connected to devices such as programmable automation controllers. The automation controllers perform data processing, communication, and other tasks. With fog computing, the data is transferred from endpoints to a gateway. Then the data is transferred to sources for processing and return transmission. The geographically distributed infrastructure is aligned with cloud services to enable data analytics with minimal latency.

Both fog and edge computing help to turn data into actionable insights more quickly so that users can make quicker and more informed decisions. Then, fog and edge allow companies to use bandwidth more effectively while enhancing security and addressing privacy concerns. Since fog nodes can be installed anywhere there’s a network connection; fog computing is growing in popularity in industrial IoT applications.

The Role of Fog Computing in IoT

When a device or application generates or collects huge amounts of information, data storage becomes increasingly complex and expensive. When handling this data, network bandwidth also becomes expensive, requiring large data centers to store and share the information.

Fog computing has emerged as an alternative to the traditional method of handling data. Fog computing gathers and distributes resources and services of computing, storage, and network connectivity. It significantly reduces energy consumption, minimizes space and time complexity, and maximizes this data’s utility and performance.

The “Smart City”

Let’s take a smart city as an example. Data centers are not built to handle the demands of smart city applications. The ever-increasing amount of data transmitted, stored, and accessed from all IoT devices in a city will require a new kind of infrastructure to handle this volume. It is these applications that need fog computing to deliver the full value that IoT will bring to them.

Utilities

Water utilities, hospitals, law enforcement, transportation, and emergency management applications in smart cities need the latest data and technology to deliver information and services to support their operations.

Information about water leakages, carbon emissions, potholes, or damage can be used to update billing information, improve operations, save lives, and increase efficiencies. The benefits of capturing and analyzing this data can be directly applied to smart city applications.

Fog computing doesn’t move you from one place to another. Instead, fog is a method for deploying Internet of Things networks where they provide the best return on investment.

Benefits of Using Fog Computing

Fog computing can be used in applications that deal with large volumes of data, network transactions, and fast processing. The benefits of using fog computing include real-time, hybrid, and autonomous data centers that improve operational efficiency and security. Additionally, fog computing can help ensure your systems stay available and optimized without the need to invest in power, data center security, and reliability.

Fog computing reduces overhead costs by concentrating on computing resources across many nodes. The location of the fog nodes is chosen based on their availability, efficiency, and use. It also reduces the load on the data centers of organizations. The reduction in data traffic is another major advantage of fog computing.

Many companies are using fog computing to deploy software applications distributed in many places. Companies deploy many systems over a network to achieve better efficiency and reachability.

Fundamentally, fog computing gives organizations more flexibility to process data wherever it is most necessary to do so.  For some applications, data processing should be as quick as possible, for instance, in manufacturing, where connected machines should respond to an accident as soon as possible.

Fog computing can also provide companies with an easy way to know what their customers or employees are up to in real-time. With the implementation of fog computing, companies can expect to take on new opportunities and increase their profit with IoT technology. But more than that, this technology has the potential to save a lot of money for governments, companies, and even individual users.

Bottom Line

As cloud technologies continue to penetrate into the enterprise environment, fog computing usage will also continue to increase. Cloud computing distributes computing workloads through an elastic computing infrastructure, enabling the real-time processing of data in the cloud.

Edge computing is a major focus area of the IoT fog computing segment. Edge computing is the technology of computing resources deployed at the edge of the network, outside of the cloud. It allows computing resources at the edge of the network to be accessed, analyzed, and then sent back to the network’s edge. This allows for real-time processing of data.

Fog computing solutions will enable companies to implement real-time computing in the Internet of Things. As a result, the IoT fog computing market will be a major contributor to the cloud computing market.

Image Credit: riccardo bertolo; pexels

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AI AR/VR Artifical intelligence benefits of HR Blockchain Chatbots cloud computing data security robot process automation

6 Futuristic HR Technology Trends Amid COVID-19 Crisis

HR tech trends

Businesses are quickly adapting to the COVID-19 reality where HR technology has made the greatest influence to do so. COVID-19 has accelerated the evaluation, training, and implementation of HR tech trends in businesses of all sizes.

The HR team is actively involved in adopting innovative technologies at all levels like – recruitment process, employee engagement practices, and management processes. An emphasis has been laid on mobile connectivity and visual communication as employees started working remotely. The traditional management approach may not address the new challenges and complexities to keep the employee cycle moving.

Also, HR professionals are turning toward more strategic roles. They are catering to business needs like structuring organizations, strengthening the workforce, and managing talent. So, the repetitive tasks are handled through tech tools paving the way to use their potential to spot trends, decision-making, and becoming the true business partners.

In the survey, we can find how HR projects are delivered on their expected business value as said by employers.

Let’s deep dive into the technical aspects of the HR domain and how best HR leaders are cruising through this storm in the ocean. Here is a quick primer on some of the popular HR tech trends that are developing at an unimaginable speed.

Popular HR Tech Trends Amid COVID-19 Crisis

There is a quick shift in HR functionalities amid the COVID-19 crisis. Here are the HR tech trends that are raising the bar and helping the team to make informed and innovative decisions for the organization.

Artificial Intelligence in HR

AI in HR is on the limelight to lessen the administrative burden on the HR team. Artificial Intelligence can be used for various purposes in HR functionalities like screening, recruiting, online and offline training of employees, managing leaves, detecting anomalies, resolving queries, reviewing performance, absenteeism, exit metrics, and initiating retirements. AI use cases include digital coaching, development planning, recognition, and wellness as per ISG reports, 2019.

In brief, AI can streamline redundant and time-heavy tasks. It can quicken the tasks like surfing hundreds of resumes and cover letters, compiling and analyzing survey data, and many more tasks. Also, it removes human bias or error while evaluating candidates. However, one has to take care that there is no built-in bias while programming the algorithms, as this will continue the issue and may not get noticed upfront.

It is essential to train the systems with the right data and algorithms that are easy and transparent to understand. AI-enabled workplace still requires human skills and that’s where leadership in HR shines.

Robotic Process Automation (RPA) in HR

RPA includes robotic skills like natural language processing (NPL), machine learning, chatbots, and Artificial Intelligence (AI). It helps the HR team to increase productivity as it can speed up communications. Many of the modern HR systems have chatbots that can provide answers to employee inquiries. 50 percent of companies will have HR chatbots by 2022, Chatbot News Daily reports.

RPA has a wide range of applications in HR processes, Deloitte reports. RPA can contribute in many aspects like strategic processes, talent management, operation, and total rewards.

  • The strategic processes include workforce planning management, also, employee satisfaction, organization design, establishment, and implementation of HR policies and programs.
  • Similarly, talent management processes involve recruitment, onboarding, employee development, employee training, performance, competency, global employment, career graph, and succession planning.
  • Likewise, operation management involves data administration, management of payrolls, reports, employee health, employee separation, labor, and employee relations.
  • And, total rewards include salary compensation and other related employee benefits.

As per Deloitte studies, RPA tools are best suitable for processes with repeatable and predictable interactions with improved efficiency and effectiveness of services.

Employee engagement tools in HR

Employers today are concerned with the employees’ financial well-being and health. As a solution, they are providing financial and employee wellness apps like budgeting apps, fitness trackers, wearable apps for health, and more. They are given access to many other apps and platforms for child care too.

Moreover, there are apps provided by healthcare providers that maintain the privacy of health data. Given the pandemic situation and raising remote work culture, there are self-service employee experience portals that facilitate employees to handle HR functions all by themselves. Likewise, remote tools like Zoom and Microsoft Teams are also used in maximum while engaging with employees, interviewing, hiring, and recruitment at the remote. This paves the way for the HR team to focus on people more than processes.

Cloud-computing in HR

Cloud computing streamlines the recruitment process and is capable of transforming the whole HR functions. To mention a few trends are omnichannel models, the Internet of Things, employee wellness, learning culture, agile workforce, and data security.

  • Cloud computing ensures streamlined functions and benefits organizations that have implemented it.
  • IoT acts as the perfect tool through greater connectivity. It can be used to transform data into information at a faster rate. And, storage of this humongous data will not be a hurdle due to the cloud.
  • Cloud communication is much better and fills the missing links in the communication facilitating managers to review, communicate, or provide feedback and all through a single platform.
  • As firms encourage e-learning and online training for employees to upgrade their skills, cloud computing enables employees to meet industrial requirements in a comfort zone.
  • Cloud computing connects the workforce from various geographical locations and profiles easily and gives instant communication facilities.
  • Cloud computing is more reliable for data security as the security measures protect the data to the core.

Augmented Reality and Virtual Reality in HR

Virtual Reality (VR) and Augmented Reality (AR) can be used as a tool in HR toolkit. They help in the recruiting and onboarding process by setting up a simulated environment to test candidates’ specific skills, share a virtual tour of the office, create a personalized work-space environment, improve efficiency, save costs and make engaging recruitment process that helps in branding, training employees in new techniques.

It enables the HR professionals and supervisors to identify key areas of improvement, understand elements of concern for accomplishing goals by scanning people’s faces through sentiment analysis. 49% of Gen Z employees in Singapore believed that VR would revolutionize their work, while 45 % in the US confirmed the same. AR and VR have the potential to elevate a team collaboration levels. Though it is not implemented in an appreciable strength, it is more likely to be the top trend in the near future.

Blockchain in HR

Blockchain technology is poised to manage HR capabilities in different ways. The HR industry is envisioning the use cases of blockchain in their arena vowing to its characteristic features like immutability, transparency, trust, security, and decentralization. A few of the use cases could be –

With its security capabilities, blockchain can handle sensitive employee data like their pay, healthcare, banking, performance records, and expense reimbursement. Blockchain will prevent internal and external hacks of sensitive records as there will be authorized persons only.

It is difficult to determine the employees’ work and education history with the current facilities. With blockchain, the HR team can improve recruiting processes, verify the qualifications of the prospect, and make background checks. All records of the candidate will be present in a block that will get accessed through authorization.

Further, blockchain eliminates time lags in payroll systems even when the company goes global. The blockchain ledger helps to track invoices, facilitate distribution, billing, and reporting of all kinds of transactions. Payroll processing will occur in a timely fashion. It also assists in automating taxes, reimbursement system, mitigate audit risks, and give better access to benefits and packages.

To conclude

HR technology is helping the industries to sustain amid the prolonged lockdown led by the COVID-19 crisis. In addition to these technology implementations, an increased focus toward the people aspect will drive the HR domain toward new work habits and its success.

Image Credit: pexels; pixabay

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cloud computing Connected Devices connectivity Future Tech Industrial Internet of Things IoT Tech

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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