SaaS or Software as a Service is making a buzz in almost every industry, and logistics is no exception. Businesses across the globe prefer SaaS-based logistics software instead of investing in developing on-premises software that might take several months or even a year to build. To understand why this shift is happening- let’s first understand what are the problems the logistics industry is facing? And how SaaS-based applications can be a perfect solution for them.
SaaS is a mechanism of transferring software to everyone (individual or business) over the internet. In this nature of software licensing and release model, the license of the software can be availed periodically. The software is centrally introduced in the cloud, and the individual, availing SaaS access through any compatible browser.
Present industry challenges:
Transportation and logistics industries are constantly struggling with challenges- like complex competition, changing customer expectations, and leveraging digitization to succeed. Lack of ‘digital culture and training is thus the biggest challenge for these companies. Attracting the right skills is one issue, but developing the right strategy is even more crucial.
Besides, an increasingly competitive environment is another dominant factor in the mix. Some of the sector’s customers are springing logistics services of their own, and different competitors to the business are shaping out more profitable aspects of the value chain by exploiting digital technology or brand-new ‘sharing’ business models. They don’t have asset-heavy balance sheets or cumbersome existing arrangements weighing them down.
There is no other industry where so many experts ascribe vast influence on data and analytics in the next five years than transportation and logistics. There are vast possibilities to enhance performance and serve customers better, and LSPs who are part of a digitally integrated value chain can benefit from significantly increased forecasting to scale up capacity or down and plan routes. Adding machine learning and artificial intelligence techniques to data analytics can deliver truly dynamic routing.
One of the most promising ways for transportation companies to improve their workflows and find new ideas for business growth is using a custom SaaS system. These systems guarantee more agile data processing and greater automation of operations than out-of-the-box solutions. They also allow companies to build only the features they need.
From streamlining processes to increasing visibility in the supply chain, their pertinence has grown in quality. According to Statista, 87% of logistics and supply chain firms are tapping into cloud-based solutions to facilitate transportation management and gain an edge in an ever-changing marketplace.
Businesses can optimize SaaS solutions to automate the logistics workflow and free employees from performing routine tasks connected with paperwork.
Logistics is a sphere of multitasking and multiprocessing. Robust SaaS solutions are essential for tracking details and conducting analysis, which helps businesses detect issues and optimize their work. They are designed to simplify and manage complicated logistics problems by taking an integrated, end-to-end approach. The functionality enables optimal route planning, reductions in fuel costs, and significant improvements in asset utilization
Logistics companies gain real-time visibility from SaaS solutions into logistics processes and data, assisting businesses to keep their operations under command. SaaS logistics solutions support companies to reach a more predictable supply chain, while companies can associate in the cloud to cooperate efficiently.
SaaS solutions empower operators to access high-level route planning abilities on demand, encouraging them to avoid the high capital cost investment and the in-house IT staffing required for superior systems integration. We can directly scale them up or down according to business needs without infrastructure changes as they are delivered as a service.
Inovar optimized SaaS solution for logistics
Inovar joined hands with fleet operators to manage their logistics operations and apply technology for making their workflow automated. With an AI-powered SaaS solution, we gave fleet operators ways to predict demand, optimize vehicle usage, and give them real-time tracking of their vehicles from both fleet operators and consumers.
Our first aim was to bring all stakeholders Markets, Warehouses, Traders, Ports, and Truckers under a single virtual platform and allow them to trade on-demand within an open marketplace.
Inovar brought Microsoft Azure IoT and machine learning services within a customized application that could build a connected platform for fleet operators. The solution could not only gather data efficiently but generate intelligence as it was used. Another remarkable feature was analytics and event hubs that captured data used in a Databricks platform to generate insights. Using these insights, system booking, scheduling, predictive maintenance cycles became 70% easier than before.
Finally, the system went live with 1000+ vehicles, and the logistics client started to reap ROI from multiple avenues. Within 3 months, the efficiency of their operations improved by 30% with better security, high scalability, and faster availability from the end-user.
Summing up, we can say that cloud logistics automation is beneficial to all participants in the supply chain. It allows you to easily solve complex problems, so its popularity will only grow every year. As a logistics software development partner, we solve many standard and custom needs of the transportation industry from Business as well as Technology perspectives. After many years spent in the logistics domain, our dedicated development team learned specific details and gained experience that can be used to effectively help you with any of your logistics software that you need to develop.
InovarTech is ready to improve your automation experience. With our brilliant forms and streamlined workflows, you will be fit to tackle process technologies and know-how to adapt them to your institution. Reach out to us today to get started. You can take comfort because we know process automation and how to implement them effectively. Let us deliver high-volume processes to your doorstep.
Business process automation now becomes the new norm at the workplace to cope with a fast-paced economy and growing challenges for modern business. While a few organizations may very well consider automating their IT tasks, RPA likewise means to help them rehashing the way they work together, uplifting their consumer loyalty and fortifying employee work values. The necessity of bringing automation has given a boost to Robotic Process Automation (RPA) solutions. But AI (Artificial Intelligence) comes into the center stage as entrepreneurs craving more assistance from robotic process automation. Near future, it would be hard to imagine the RPA solution without AI integration.
According to Forester’s 2019 predictions, the combination of RPA and AI is becoming the new strategic
investment in the corporate world. AI and RPA are emerging automation technologies with enormous potential. The combination helps businesses in achieving tremendous growth in such a competitive market.
You may curious why a business should combine these two technologies?
The reason is simple- RPA has no capability of cognitive awareness, and AI brings it in the customized solution. This combination of AI and RPA adds up to intelligent process automation (IPA). In interest to machine learning algorithms and RPA, IPA also incorporates process management software, natural language processing, creation, and cognitive agents, or “bots.” According to McKinsey, IPA can –
- Add up to20 to 30% improvement in proficiency,
- Reduce the processing time by 50 to 60 percent!
Make an improvement in ROI in triple-digit percentage. TMR (Transparency Market Research) has predicted RPA will be prevalent in the corporate world that its market to reach up to $16 billion by 2024. On the other hand, AI focused on imparting intelligence in the machines and connected devices through speech recognition, decision making, and predictive analysis.
As AI is gaining ground swiftly across the world, it can be integrated with other emerging technologies like IoT and Blockchain to realize the futuristic concepts of smart cities and secure transactions sequentially. McKinsey foresees AI to deliver around $3.5 to $5.8 trillion in value to the world per annum.
How AI enhances RPA services
AI and RPA combination enables modern enterprises to leverage the benefits of both these technologies in accomplishing incomparable tasks with higher efficiency. It is like something beyond the range of a customized RPA solution because it develops to perform the same and repetitive type of work.
At the point of a substantial change is anything but a reasonable alternative for companies, RPA can go about as a facilitator to enable organizations to grow and include value. It also permits them to appear with strategic frameworks around investment they’ve efficiently made into their custom frameworks. The assimilation into AI is the following stage of this granular, more responsive type of change, with more business exercises either completely or partially automated by progressively refined means. It is known as cognitive RPA or CRPA.
RPA alone imitates human action through different AI capabilities, machine vision, speech recognition, and pattern identification capacities and can deal with organized, semi-organized, and unstructured data. However, infused with machine learning gives robots a chance to figure out how to process and enhance tasks that ensure probabilistic conduct.
AI integrated Robotic Process Automation solutions bring the capability of gathering and sharing valuable information with different systems for better decisions. Customized RPA solution can utilize data fetched by AI and performs the complex task with ease. Adding up, AI, commonly with cognitive technologies and ML (Machine Learning), enables robotic process automation by preceding a human response in the workflow.
The humble bot
Organizations aren’t looking for just a bot. They have been looking for a platform with orchestration and integration where, they could readily adopt modern technologies and combine them with intelligence. Now, most organizations are strategizing to automate most of the steps of a business process and then applying a different level of intelligence. Hence, when picking vendors for their bots, the enterprises keep their eye towards that future.
Just like, Germany’s ZF Group, an automotive supplier has started applying intelligence to its business processes just over a year ago and created a bot to answer the most repetitive questions. According to the company’s IT manager around their corporate communication, they have a lot of repetitive work. Furthermore, they receive a lot of emails with repetitive questions coming into their inboxes. Automated integrations and orchestration were their first goal, and they also wanted a platform with built-in checks and balances. Finally, they choose a bot platform that offers a management, governance, and language support layer underneath the bots, called stateful network for AI process (SNAP), which will stop a bot if it demonstrates anomalous behavior.
Real-time Decision support system
Decision points are another way to add intelligent decision points into a traditionally automated business process. Nevertheless, cognitive technologies and machine learning direct out to be even more severe. In general, when AI technologies are integrated with RPA stages will automate the emotional and judgment-based process.
Just like American Fidelity Assurance, an insurance policy provider turned to UiPath, an enterprise RPA vendor, and AI platform Data Robot, to add intelligence to its processes. A system that can manage and execute real-time decisions through AI is the best option for automating complex decision processes. Applying high-level analytics and machine learning those systems to become intelligent and continually adjust and discover from new, contextual data. They set up a real-time decision-making system in form of RPA that helps to transfer highly complex problems in real-time applying AI-based analytical decision-making.
For investment firms, real-time decision-making can analyze thousands of business inputs, constraints, and options and arbitrate between multiple internal targets and trading decisions. It can apply an analytical constraint-based optimization approach to models across thousands of possible actions – whether it be specific content, an offer, a price, a sort order, or a recommendation – and determine the best outcomes for the organization. sometimes it qualifies those choices upon certain criteria from profitability, risk, or revenue perspective.
Sometimes, the traditional approaches to RPA may hit a decision point that is too complex for simple automation. Considering the facts, organizations are also looking at using AI for process mining to automate process discovery rather than have business analysts figure out what happens in the company.
RPA-Robotic Process Automation is transforming the system many organizations function. Process Mining enables companies to holistically experience their methods and recognize process advancement possibilities to boost efficiency and overcome costs. Its 360° view of processes enables hyper-automation—standardizing and accelerating enterprise automation using technologies, such as machine learning (ML) and artificial intelligence (AI).
The conventional way to business process management involves business analysts talking to managers and employees, carrying audits, then creating charts that illustrate the organization’s various business processes.
Sumeet Vij, director of the strategic innovation group at Booz Allen Hamilton, has mentioned how clients are presenting process workflow automation and how things happen, bottlenecks are different. When it comes to process mining, machine learning helps people experience a picture of how things are actually happening. New tools will update the processes and spot anomalous behavior in real-time with the evolvement of business.
Sometimes, a business process may be viewed as charts, such as Visio diagrams, and managers can drill down into the process, down to the level of individual transactions.
Business process analytics
Some organizations have sufficient business process data that they can instantly view the overall picture of what’s happening and make analyses and forecasts. Organizations are taking the exhaust from the RPA process and trying to capture that, learn from it, and make those processes smarter.
Seann Gardiner, senior VP of business development at Data-Robot (an AI platform provider), mentions if a company has a strong focus on process-level automation and can manage that data, then it might be ready. But strong leadership is also mandatory to make the process successful. Business leaders who believe in automation and an AI-first mentality can make the organizational changes needed.
The use-cases of intelligent automation will likewise keep developing as new AI procedures and solutions enter the marketplace, drastically changing the future working environment. It can perform even complex and unique business actions like humans as an advanced solution. However, there is no point in believing that this combination will replace humans as they remain the controller of the RPA +AI combination by building the rules and manage the operation. The combination of RPA and AI had aimed to bring a digital transformation in the companies and adding more value over the period.
For organizations to use this innovation completely, they initially need to see how these solutions can change their procedures and apply strict governance to settle on beyond any doubt their decision-making is paramount. If organizations attempt to actualize these connected yet granular solutions over their companies, it will enable them to drive a double speed change and pursue new heights in Business and IT maturity level.