The first well-explained development methodology, known as Waterfall, was established in 1970. However, businesses later realised that a more flexible approach was needed. As a result, the Agile Manifesto, established in 2001, brought about a significant shift towards the adoption of more agile methodologies, resulting in an ongoing agile transformation across industries. This manifesto formed the basis for further methodologies development like Kanban, Scrum, Lean and SAFe, which provide businesses with the flexibility and responsiveness required to navigate today's competitive landscape.
Companies that use agile methodology fully or partially are 1.5 times more likely to perform better financially than their competitors.
It is interesting that supporters of each Agile methodology were struggling to confirm the advantages of each relatively popular methodology. Given the highly dynamic nature of market evolution, it became clear that no methodology "silver bullet" exists for product development. Thus, it's essential to select the appropriate methodology for each particular project, and sometimes it may require combining different established approaches.
Similarly, roles like Project Manager, Scrum Master, and Agile Delivery Lead/Coach are becoming more common, but it can be confusing to know which one your organisation needs. Here, we will briefly overview those popular roles and compare their abilities to handle frequently occurring requests.
During the early stages of software development, all companies and customers had a shared desire to see each project and business idea implemented according to the stated product vision (scope), schedule, and budget. These were the three key elements crucial to the business' success, and it was necessary to have a person who could ensure the project's realisation and maintain the agreed-upon success criteria.
This individual served as the project manager (PM), overseeing the entire project lifecycle from initiation, planning, execution, controlling and until completion. The PM's responsibilities include managing resources, budgets, timelines, risks, and stakeholders. They are like superheroes who understand every process and make the correct decisions necessary for the success of any project delivery.
Over time, it has become evident that markets have grown increasingly volatile. Clients were keen to see results at the early stages and have the possibility to influence the process as much as possible. However, with contracts that strictly defined Scope, Schedule and Budget, it was hard and sometimes frustrating. Fighting around changes and the way of introducing them could lead to the deterioration of relations, especially when we talk about long-lasting endeavours like product development.
Publishing the Agile Manifesto gave a start for different product development approaches. One of the most popular is Scrum, which introduces the role of Scrum Master (SM). Scrum masters ensure that all team members and stakeholders have a clear understanding of the project's progress, objectives, and challenges. But unlike a Project Manager, Scrum Master is just a team leader and doesn't dictate what, how and when to do to the team. Instead, their main goal is establishing Scrum principles defined in the Scrum Guide.
Challenges associated with the Scrum Master role often relate to their strong narrowing down to Scrum rules and their servant role. What does it mean?
Agile Delivery Lead or Coach (ADL) should cover all gaps related to PM and SM competencies. This is possible only by having a broad classical PM practice and experience in different Agile frameworks, including scaled Agile frameworks. Agile principles and experience must have a dominant position but be supplemented by standard project management skills. The advantages of having an ADL with such skills to solve special requests are shown in the table below.
Customer needs | Project Manager | Scrum Master | Agile Delivery Lead / Coach | Comments |
---|---|---|---|---|
Fixed price project | + | - | + | Management of project constraints: Scope, Schedule and Budget. Sometimes, it could be a trial project with a new vendor just to understand reliability. |
Scrum set-up, team training, progress tracking, risk management | +/- | + | + | The most popular Agile framework. Here, our goal is not only to work with your team but also to provide assistance in Scrum training of different personnel. |
Scaling Agile (program / product level) | - | +/- | + | It is the next step after establishing a robust Agile culture on the team level. Also, it could be an urgent need based on Merger and Acquisition process. |
Complex Agile delivery (Scrum + Kanban, Lean, Hybrid, FP, KPIs) | - | - | + | Very flexible but responsible delivery based on frequent market and client requirements changes. |
Agile transformation strategy | - | - | + | Here, we mean to conduct Agile coaching and mentorship at different organisational levels. It includes environment screening, building an agile transformation strategy and leading the Change management board. |
Objectives and key results (OKRs) | - | - | + | OKRs – a set of challenging, ambitious business goals with measurable results. To gain these important business outcomes, it is necessary to build a common aligned environment and be flexible in choosing the needed implementation approach. |
Lean portfolio management (LPM) | - | - | + | In contrast to traditional Project Portfolio Management, LPM allows the selection, prioritisation, and management of multiple projects to achieve a strategic goal based on market changes and client needs. |
The evolution of project management methodologies has been primarily driven by the need for businesses to adapt to a rapidly changing market landscape. The shift from traditional Waterfall methods to Agile frameworks like Scrum and Kanban reflects a deeper understanding of the importance of flexibility, iterative development, and customer collaboration in delivering successful products and services.
However, it is not enough to simply find the optimal approach in a particular situation. One must also consider the clients, market, available resources, and future plans. This means that the choice made today may not be optimal tomorrow and should be corrected based on new information. A person with broad delivery experience, who is flexible and ready to make non-standard decisions can handle new challenges with ease.
In this new paradigm, the roles of Project Manager, Scrum Master, and Agile Delivery Lead each play critical functions. However, the Agile Delivery Lead (Coach) stands out as the right person who is able to cope with current challenges and at the same time, able to think about the future.
The relationship between a Scrum Master and a Project Manager is not based on hierarchical superiority but rather on different roles and responsibilities within project management.
As we mentioned in this article, there are distinct differences between the two roles. Both Scrum Master and Project Manager can be crucial for project success, and it's not uncommon for them to work on the same project simultaneously.
The oil and gas sector is undergoing rapid and significant transformation. As tensions between Russia and Western countries continue escalating, many European countries and oil and gas companies are scrambling to find alternative energy sources, including diversifying through LNG (liquified natural gas terminals) terminals.
Environmental considerations also play an increasingly important role in oil and gas production. The implementation of green policies regulating global energy transformation has pushed major polluting industries to measure and record air emissions more accurately and continuously.
The need for sustainable practices is not limited to governmental regulations; environmental and social activists are also urging industry leaders to prioritise green energy solutions. This has resulted in a significant shift in traditional practices within the oil and gas industry, with a greater emphasis on innovation, technology-powered energy solutions and environmental responsibility.
The oil and gas industry has been leveraging technologies for quite some time. With the demand for energy constantly increasing, digital solutions have become essential to addressing the current challenges of this industry. Let's look at the current and future technology trends that could help the oil and gas sector stay ahead of the curve.
The oil and gas industry has been collecting data for decades, so it is no surprise that companies in the sector are adept at using big data, advanced analytics and artificial intelligence. Big data refers to the large amounts of information companies can collect from their operations—everything from buying supplies and drilling data to production figures.
With the use of advanced analytics companies turn this raw data into actionable insights in real-time so executives and managers can decide how best to operate their business. Advanced analytics powered by artificial intelligence also allows companies to identify patterns in their operations to predict future outcomes and make better decision-making processes for allocating resources.
The oil and gas industry can greatly benefit from data analysis, as it has the potential to reveal essential details that may otherwise be overlooked. In this sector, even a small mistake can have major consequences.
One example of how this trend is being applied in the oil and gas industry is through analytics tools like appygas, which helps users reduce distractions, collect data from all sources in a high-quality format, and focus on what matters most to make more informed decisions.
This particular tool is most useful in the heavily regulated European gas market, where operators are required to file regular reports on their operations. The appygas software consolidates gas market data from over 50 reliable sources, both historical and real-time, providing users with the ability to quickly identify trading opportunities and calculate the cost of gas transportation based on an aggregated data analysis. The platform also includes easy-to-use dashboards and can be fully customised according to the needs of the user's portfolio.
If you would like to test the tool, you can sign up for a 14-day trial here.
Many oil and gas companies are turning to cloud solutions for data storage, analysis, and management due to their many benefits. The decentralised nature of the cloud makes it much easier for those in the gas and oil industry to perform complex analyses and make decisions. The ability to store data virtually without limit is an excellent benefit for drilling companies, who can access their data when needed rather than being limited by the space available in their physical servers. If more storage is required, the cloud expands accordingly. Moreover, cloud solutions can assist oil and gas companies in enhancing their sustainability efforts.
Oil and gas companies have to deal with many factors that can be difficult to control and monitor, such as weather, moisture levels, vibrations, and equipment conditions. The use of Industrial Internet of Things (IIoT) technology can assist these companies in monitoring and controlling these factors in real-time and making necessary adjustments to avoid potential problems.
For example, imagine that an oil company is using IIoT-enabled sensors to monitor the temperature of its pipelines. If the sensors detect a sudden temperature drop, it could indicate that the pipeline is leaking. The company would then be able to take immediate action to fix the problem before it causes any severe damage.
Moreover, IIoT technology can also help oil and gas companies reduce their overall costs. By closely monitoring their assets, they can avoid costly downtime associated with maintenance problems or equipment replacement. In the long run, this can lead to significant savings for these companies.
Digital twins technology allows for a replica or model of an object, system or process to be created and used for various purposes such as monitoring, analysis and decision-making. The software allows oil and gas companies to simulate their assets, understand how they will behave under different conditions, predict problems before they occur, mitigate risks and remotely monitor oil and gas rigs.
Digital twins can help oil and gas companies maximise production by running various scenarios, considering constraints such as water, gas and infrastructure capacity. By combining advanced digital twin software with IoT and machine learning capabilities, businesses can collect data from multiple sources and monitor energy usage, operational conditions and key performance indicators.
The oil and gas industry is currently facing several challenges, including the need to find new sources of oil and gas and the need to lower emissions. While innovative technologies are not a panacea, they can assist the industry in addressing these difficulties.
For example, data analytics can help determine optimal routes for transportation and commodities prices, while IIoT can aid in monitoring pipelines to avert leaks. Despite future challenges that the oil and gas industry will face, technological advances will enable it to carry on its operations successfully.
The oil and gas industry is experiencing a significant transformation due to the integration of advanced digital technologies like high-performance computing (HPC), machine learning, digital twins, IoT sensors, and AI-driven environmental monitoring systems. These technologies are being implemented across various sectors of the industry, including exploration, production, transportation, and refining.
Advanced technologies like high-performance computing and machine learning are improving seismic imaging and reservoir mapping, while digital twins and automated drilling systems optimise operations. In midstream and downstream, IoT sensors and predictive maintenance ensure asset integrity, while AI-driven process control and digital plant twins optimize refining processes. Environmental and safety efforts benefit from AI-driven emissions monitoring and drone surveillance.
Technologies are bringing significant improvements to operations within the oil and gas industry. For example, leveraging technologies can help prevent and reduce downtime, enhance the quality of production data, minimise manual work, etc.
As we navigate the merging of the world with AI and ML, it becomes clear that a surge in devices equipped with AI and/or ML is on the horizon. The rise of AI-enhanced products is already in progress, as seen through Samsung's introduction of Galaxy AI, as well as Apple's claim to integrate generative AI features into the upcoming iPhone 16.
The automotive sector has also embraced the shift, exemplified by Volkswagen's preparations to introduce vehicles incorporating ChatGPT into its IDA voice assistant.
Modern consumers want everything at their fingertips—information, products, and services. The time available to capture and retain their attention decreases. Businesses that can't keep up risk falling behind.
That's where intelligent automation comes in as a powerful ally. It's like having a tireless assistant that streamlines processes, eliminates manual roadblocks, and speeds things up. Methodologies like DevOps and MLOps showcase the magic of automation. In 2022, DevOps emerged as the predominant software development methodology globally.
The concept of AI is no longer a distant future—it's already here, and its impact is palpable. The question arises: what does this mean for businesses? In brief, understanding and leveraging automation, especially through specialised MLOps services and DevOps services, are crucial for businesses looking to thrive in this tech-driven landscape. Let's explore this topic further for a more in-depth understanding.
MLOps, short for Machine Learning Operations, refers to practices that streamline the end-to-end lifecycle of machine learning models. Drawing inspiration from DevOps principles, MLOps serves as a bridge between the intricate phases of model development, deployment, and monitoring.
To understand MLOps comprehensively and its potential benefits, one must understand how machine learning projects evolve through model development. Initiating any machine learning process involves defining a foundational set of practices, including data sources to be used, the place of storage of models, monitoring and addressing issues, and more. After deciding on the basic set of practices, creating a machine learning pipeline can begin.
A typical ML data pipeline consists of the following stages:
MLOps enables seamless cooperation between data scientists, DevOps engineers, and other professionals involved in ML production . Its core purpose lies in enhancing collaboration, accelerating model development, and implementing continuous monitoring practices. MLOps methodology can help companies navigate the dynamic landscape of machine learning, ensuring efficient and high-quality AI & ML solutions.
MLOps adoption can speed up machine learning development and model integration by implementing continuous integration and continuous delivery (CI/CD) pipelines. Process automation eliminates the need for manual interventions and fosters iterations. Thus, it enhances team agility and flexibility in testing and deploying machine learning models.
With the help of automated model validation, monitoring, retraining, and re-evaluation, MLOps can assist in delivering machine learning solutions that perform consistently well. MLOps engineers specialise in optimising infrastructure and configuring workflows to identify and promptly resolve any bottlenecks that may arise proactively.
With MLOps services, businesses can enhance the effectiveness of their machine learning initiatives, ensuring that investments in machine learning projects result in heightened business value and profitability. Organisations can maximise ROI by streamlining resource utilisation, automating model management processes, refining machine learning workflows, enhancing ML model accuracy, and reducing time-to-market for solutions.
Within the world of software development, MLOps and DevOps strive to streamline and improve operations. Essentially, both MLOps and DevOps foster automation and stress the significance of monitoring and feedback for the optimal performance of models and applications.
In addition, MLOps tools and platforms often seamlessly integrate with some DevOps toolchains, like Jenkins, Terraform or Helm , ensuring seamless integration of MLOps into broader DevOps workflows.
Despite some shared principles, MLOps and DevOps still differ significantly. Primarily centered around traditional software development, DevOps focuses on collaboration and communication between development and operations teams. Its core objective is to streamline and automate the various stages of software application development, including building, testing, and deployment.
In contrast, MLOps extends these principles to the domain of machine learning. It addresses the unique challenges posed by ML models, incorporating version control, reproducibility, and lifecycle management. Let's take a closer view at some of the differences.
DevOps | MLOps | |
---|---|---|
Versioning | Version control primarily focuses on tracking changes to code and aspects associated with the software application. The workflow typically involves building, testing, and deploying the application, with a relatively straightforward tracking process around code changes. | MLOps introduces a more complex landscape for version control. In machine learning, the process resembles an experiment in nature, where various elements, such as different datasets and algorithms, are applied. This complexity adds a layer of challenge, as there are numerous factors to track. |
Testing | In DevOps, testing primarily centres around the traditional software development life cycle, emphasising unit tests, integration tests, and end-to-end tests to ensure the software application's functionality, reliability, and performance. | Testing in MLOps extends beyond conventional code validation and encompasses evaluating model performance on diverse datasets. It involves testing different algorithms, validating data, assessing the model's accuracy, and validating predictions against real-world scenarios. |
Monitoring | In the DevOps domain, monitoring typically revolves around the software application's performance and health throughout its development lifecycle. The emphasis is on ensuring the seamless functioning of the application within the software development and delivery pipeline. | Monitoring in MLOps is crucial for understanding the dynamic nature of machine learning experiments, where models evolve based on continuous learning and adaptation to new data. As real-world data undergoes constant changes, it can lead to model degradation. MLOps addresses this challenge by implementing procedures that support continuous monitoring and model retraining. |
Understanding these distinctions is paramount for organisations navigating the intersection of software development and machine learning. By adopting the principles that align with their specific needs, businesses can enhance collaboration, accelerate development cycles, and ensure the robust deployment of both software applications and machine learning models.
In the ever-evolving landscape of technology, the intersection of AI, ML, and software development is reshaping how businesses operate. As we explored the realms of MLOps and DevOps, it is evident that these methodologies play pivotal roles in meeting the demands of a tech-driven future.
The comparison between MLOps and DevOps reveals shared principles but distinct focuses. While DevOps centres around traditional software development, MLOps extends these principles to address the unique challenges ML models pose. The complexities of versioning, testing, and monitoring in the MLOps domain showcase the methodology's adaptability to the intricate nature of machine learning experiments.
In conclusion, navigating the convergence of software development and machine learning requires a nuanced understanding of MLOps and DevOps. By embracing the principles that align with specific organisational needs, businesses can thrive in the dynamic, tech-driven landscape and ensure the seamless deployment of both software applications and machine learning models.
Integrating MLOps and DevOps into existing software development processes is a nuanced task. Organisations often initiate the integration by aligning the principles of both methodologies with their specific needs. It involves identifying points in the software development lifecycle where collaboration between data scientists, DevOps engineers, and IT professionals can be optimised. Practical implementation may include the adoption of continuous integration and continuous delivery (CI/CD) pipelines and automation of model validation, monitoring, and retraining processes. Successful integration strategies might also involve overcoming cultural resistance, fostering cross-functional collaboration, and addressing team skill gaps.
Despite the highlighted benefits, adopting MLOps and DevOps can present challenges. Cultural resistance within organisations, particularly in sectors unfamiliar with these methodologies, may impede seamless integration. Skill gaps may arise, requiring training initiatives to ensure teams are proficient in the tools and practices associated with MLOps and DevOps. Unforeseen complications during the implementation phase, such as compatibility issues or the need for significant infrastructure changes, could pose additional challenges. A thorough understanding of these potential obstacles is essential for businesses considering the adoption of these methodologies.
Since its launch, Copilot has sparked various discussions, ranging from enthusiasm and optimism to scepticism and caution about its potential impact on programming and the future role of developers. Its influence on different aspects of software development - from speed to code quality and learning - continues to be a subject of analysis and discussion.
GitHub Copilot users demonstrated an acceleration in task completion, achieving a 55% faster rate than developers who did not use the tool.
Intrigued by the bold claims regarding the speed boost attributed to Copilot, we embarked on a journey to verify its effectiveness, particularly in the realm of AI in software development. In our pursuit of truth, we conducted our own testing of Copilot's use on real projects. To ensure optimal results, we took the following approach:
The key objective for the team was to conduct a GitHub Copilot review and assess its impact on coding productivity, identify its key influences, and find the most effective ways of using it. The testing period lasted three months to mitigate the potential bias influenced by a learning curve. Let’s dive into the outcomes.
ELEKS team has recently conducted an in-depth investigation into GitHub Copilot, aiming to assess its impact on developers' tasks, completion duration, and the quality standards of the recommendations it provides. The findings of this investigation can be reviewed here: ELEKS’ GitHub Copilot Investigation – Exploring the Potential of AI in Software Development.
This study's key focus was to explore how the use of Copilot affects different types of projects. We tested and analysed the effectiveness of Copilot in monolithic applications and microservices in both backend and frontend applications to understand where this tool is most effective.
In broad terms, we can assert that the impact of Copilot on development speed is highly variable and depends on many factors. The following are key dependencies that emerge regarding the effective utilisation of Copilot:
Depending on the type of project and code structure, Copilot's impact on development speed varies: in frontend monolithic applications, we got approximately 20-25% development speed improvement; in backend monolithic applications - about 10-15% improvement, and in backend microservices - an average of 5-7%.
The verdict? Copilot thrives in projects with a large codebase, where it can support developers with existing templates and solutions. However, its prowess dwindles in the microservice realm, characterised by a small codebase. It indicates Copilot's ineffectiveness in projects that are just starting and do not yet contain enough developed solutions.
Testing Copilot on projects with different tech stacks showed a significant dependence on the quality of Copilot's suggestions based on the popularity of the technology.
We believe this is because Copilot was trained on public GitHub repositories and had more training material for technologies that were more popular among developers.
Copilot tends to generate higher-quality suggestions with proper and logical naming of variables and methods. Leading us to believe that quality naming helps Copilot better understand the context of the code, providing more accurate and useful suggestions.
Meanwhile, when the naming of variables and methods is unclear or ambiguous, Copilot has less information to analyse, which decreases the quality of its contribution to the development process. Thus, high-quality naming in code not only simplifies the work of programmers but also enhances the effectiveness of artificial intelligence tools.
Despite its effectiveness in certain aspects of development, we also found that Copilot has limitations, especially when generating code that implements new business logic.
Copilot writes only the code according to the prompt, not complete solutions. Copilot is most effectively used for clear and template tasks. The time spent on a detailed description of business logic can outweigh the time needed to implement this business logic without using Copilot.
While effective in templated tasks, it struggles with the intricacies of new ideas or creative programming. The message is clear: Copilot is a developer's trusted companion in routine, but the realm of innovation demands the touch of human creativity.
One of the most interesting characteristics of Copilot is its ability to adapt to the specifics of a particular project. Over time, Copilot "learns" the coding style and specific features of the project, leading to an improvement in the quality of its suggestions.
Initially, Copilot may provide generic or less precise solutions. However, as the tool accumulates more exposure and interaction within the project, the accuracy and relevance of its suggestions significantly improve. This is especially noticeable in projects with an established coding style and a large amount of existing code for Copilot to "train" on. This adaptability makes Copilot not only a tool for increasing efficiency but also a powerful aid in maintaining code consistency within a project.
Developers have also highlighted Copilot's positive influence on code complexity, noting a shift towards more readable and maintainable solutions, especially among those accustomed to crafting convoluted and intricate code structures.
Copilot doesn't stop at coding; it has also mastered the art of automated testing. The tool offers templates and recommendations for potential test scenarios, allowing developers to save time and resources.
Copilot's ability to generate unique test cases that may not be obvious to developers is particularly valuable. It expands the testing coverage, improving the software product's examination depth.
Interestingly, the quality of tests created with Copilot is directly related to the quality and structure of the tested code. Our developers noted that the clarity of variable names, methods, and the overall structure of the code significantly affect the quality and accuracy of Copilot's test generation. Therefore, the effectiveness of utilising Copilot for writing unit tests depends on the tool itself and the quality of the tested code.
Overall, Copilot has proven to be a useful tool in the process of writing Unit Tests, enhancing not only the speed but also the quality of the final product.
GitHub Copilot increases the coding speed and improves the overall nature of a developer's work. According to developers' feedback, Copilot allows them to shift focus from routine, time-consuming work to more creative and challenging tasks.
Additionally, Copilot can be an effective alternative to searching the Internet or documentation, reducing the time spent switching between different windows and allowing developers to concentrate on their current tasks. This feature is handy when needing to quickly find answers to questions without being distracted from the main work.
Copilot positively impacts the comfort and satisfaction of a developer. It streamlines getting answers to different questions and helps when there is no opportunity to turn to senior colleagues or find a solution on the Internet.
Interestingly, we found a correlation between the soft skills of developers and their satisfaction with using Copilot: developers with less developed communication skills often are less satisfied with its performance, possibly due to difficulties in precisely formulating prompts.
GitHub Copilot is a powerful tool that substantially enhances development productivity in specific scenarios, particularly during unit test composition and when navigating extensive codebases built on popular technologies. However, its efficacy faces constraints in tasks demanding innovative approaches or the creation of novel concepts.
Contrary to the claim suggesting a 55% boost in productivity, the actual outcome fell short. On average, teams experienced a moderate 10-15% improvement in productivity related to generating new code. However, it's essential to highlight various advantages attributed to Copilot utilisation. Overall, developers appraise Copilot as an invaluable tool that contributes significantly to development speed and fostering satisfaction among developers.
We recommend that teams and developers consider Copilot and approach it with an understanding of its potential limitations. The key to effectively using Copilot lies in understanding that it is an auxiliary tool, not a replacement for human intellect and creativity. It can enhance productivity and job satisfaction, reduce the time spent on routine aspects of development, and allow developers to focus on more complex and creative tasks.
GitHub Copilot has limitations in generating innovative ideas and creatively approaching programming tasks. Its effectiveness diminishes when dealing with smaller codebases, which highlights Copilot's inefficiency in projects at their initial stages, lacking a sufficient number of established solutions.
Additionally, the tool may not consistently deliver accurate results, particularly when employed with less commonly used programming languages.
Yes, GitHub Copilot can be utilised for code review purposes by employing Copilot Chat. It is essential to provide explicit instructions detailing the specific aspects and criteria for evaluation during the review process.
ChatGPT and GitHub Copilot serve distinct purposes, making a direct comparison challenging. The effectiveness of each tool depends on your specific needs and use case. GitHub Copilot might be more suitable if you require assistance with coding tasks. However, if you need help with general conversation or writing tasks, ChatGPT would be a better choice. It's better to evaluate both tools based on one's requirements and preferences to determine which suits your needs more effectively.
GitHub Copilot serves as a valuable tool for enhancing developer productivity; however, it should be emphasized that it does not serve as a substitute for the role of a developer.
In manufacturing terms, the involvement of numerous third-party suppliers, each responsible for providing goods on time and in the right quantities, means accurate and efficient SCM processes are essential. With increased scale and the expectation of super-rapid deliveries, manufacturing warehouse operations have become vastly more complex and so have supply chains; comprising manufacturers themselves, suppliers, logistics partners and retailers. And so, the role of ERP consulting and implementation in supply chain management has become ever more important.
Today's businesses often partner with logistics software development vendors to build and implement custom ERP (Enterprise Resource Planning) solutions. With ERP software, enterprises can gain a real-time view of their business processes from a single easy-to-use dashboard, while automating many supply chain management steps to optimise their operational efficiency.
The supply chains comprise a multifaceted, interdependent set of operations involving demand analysis, purchasing materials, manufacturing and selling the product to clients. Such intricate structure makes it complicated for the companies to manage SCM effectively. ERP in supply management is used to address various aspects across the finance, logistics, sales, manufacturing, and distribution sectors.
There are a plethora of use cases for ERP in supply chain management, but we’ve given a breakdown of a few of the benefits, plus what to look for when considering other supply chain management ERP solutions, below.
ERP system gives businesses a single view of their operations and can automate a chunk of their day-to-day processes, including demand planning. In doing so, demand can be created upon the receipt of orders, which not only ensures the leanest and most efficient use of raw materials and resources at any given time, but it also allows for more accurate job and delivery planning based on real-time data analysis. ERP is especially beneficial for procurement in custom manufacturing scenarios, where there isn’t a consistent supply of materials needed for the production process and ordering can be more complicated—often with longer lead times. With ERP for supply chain management in place, other key tasks like transportation of raw materials can be automated for improved efficiency.
Keeping paperwork up to date and accurate can be a laborious and time-consuming task, but it’s nevertheless an essential part of business operations. ERP software solutions for business can automate things like invoicing, so that invoice documents can be automatically sent out to customers with no manual intervention required. ERP systems can also handle import and export documents to facilitate international shipments, archiving data while mitigating human error and providing a better all-round service to customers.
Since a supply chain is made of multiple links, optimising the interaction between them can vastly improve the whole chain and ensure smooth deliveries and happy end customers. Integrating ERP software with supply chain management helps streamline communication between various vendors in the chain by connecting them through one centralised ERP system. This optimises efficiency by reducing bottlenecks in workflow and automating inventories so that materials are always in stock. ERPs also allow businesses to gain an overview of vendor performance, and to compare prospective vendors so that they know they’re making competitive decisions.
ERP solutions help supply chains maintain a consistent flow of goods and meet their expected service levels, which benefits the end customer and builds consumer satisfaction. ERP software gives businesses a comprehensive real-time overview of end-to-end supply chain operations, which allows them to spot potential pain points and make the necessary adjustments to improve services. This improved level of operational visibility also means that companies can make and keep service promises based on facts, rather than pre-defined best-practice SLAs.
Selecting the right ERP system for your supply chain is critical to success. Firstly, organisations must assess their specific business requirements and ensure the ERP system aligns with their goals. It is crucial to evaluate scalability, flexibility, and customisation options to accommodate future growth and the changing needs of an agile supply chain.
However, just as enterprises come in different shapes and sizes, there’s no one-size-fits-all ERP solution. To get the most from integrating ERP systems within supply chain management, you should choose custom ERP modules and solutions tailored specifically to your business's needs.
A custom logistics software development partner will work with you to analyse your requirements, get invested in your vision, and find the best-fit custom ERP solution for your business needs, which may occasionally mean challenging your original ideas.
A good ERP software development company will be able to work flexibly with you, depending on your specific requirements, either offering end-to-end solution development or bringing onboard a smart development team to augment your existing in-house resources with enhanced skills.
When selecting an ERP software development partner, it pays to check the vendor’s credentials, see how many similar projects they’ve completed and spend some time vetting their domain and technology expertise.
Successful ERP implementation starts with thorough preparation. Engage key stakeholders, define project objectives, and establish a dedicated implementation team. Conduct a comprehensive analysis of existing processes and data to identify potential gaps and customisation needs.
The ERP implementation process typically involves system design, data migration, configuration, testing, training, and system go-live stages. It is crucial to follow a structured approach, involve subject matter experts, and conduct thorough testing to ensure system stability and accuracy of data.
Successful ERP implementation requires a robust change management strategy. Ensure stakeholders understand the benefits of ERP and how it aligns with the organisation's goals. Provide adequate training and education to the employees using the ERP system. Define realistic timelines, manage expectations, and foster an environment of continuous improvement and knowledge sharing.
Engage key users throughout the implementation process to gather valuable feedback, identify areas for optimisation, and ensure smooth user adoption. Regularly monitor system performance, provide ongoing support, and offer training opportunities to maximise the benefits of the ERP system.
As logistics and manufacturing operations become more complex, businesses are turning to ERP systems more often. ERP in supply chain management streamlines processes like demand planning, procurement, and inventory management, providing real-time insights through a single dashboard.
Key benefits ERP brings to the table include
Successful ERP implementation involves thorough preparation, stakeholder engagement, clear project objectives, and ongoing support for maximum benefits. Choosing the right ERP system is vital. However, there is no perfect fit for each company, thus, the best option is to go for tailored ERP solutions.
Enterprise resource planning (ERP) in the supply chain helps to streamline the supply chain management operations. With ERP, companies can consolidate all supply chain operations in a single dashboard, have better visibility into end-to-end operations, receive cross-platform reports, automate processes, and more.
ERP stands for Enterprise Resource Planning. In logistics, ERP systems are used to oversee inventory, track orders, and manage customer service.
CRM stands for Customer Relationship Management, — a software system used to manage interactions with customers and potential customers. It helps companies streamline their sales processes, improve customer satisfaction, and increase revenue. ERP stands for Enterprise Resource Planning, — a software system used to manage business operations such as finance, accounting, inventory management, human resources, and supply chain management.
ERP management refers to the process of overseeing and coordinating all aspects of an organisation's ERP system. It includes managing data, integrating different modules, ensuring system security and reliability, and providing training and support to users.
Let's first start with defining what the metaverse really is. This term refers to a virtual world or universe where individuals can engage with each other in real time. It is a shared space that combines virtual realities, offering users an array of experiences and diverse means of interaction. The vision for the metaverse is to create a shared online world accessible to anyone worldwide through various devices – enabling people to live, work, play and socialise.
Dzianis Aviaryanau, a Middle Experience Designer at ELEKS, has been following the metaverse's development closely and observed that it has failed in many ways. So, let's shift our focus to the factors contributing to these failures.
One significant challenge in the metaverse is the prevalence of AI. While AI technology has been crucial in metaverse development, it has simultaneously neglected the importance of good experience design. It has led to developers focusing too much on technical aspects of the metaverse - creating complex algorithms and designing intricate environments - rather than user experience, resulting in frustrating and confusing user flows.
Let's face the truth: it's not a novel issue. Meta - former Facebook - has had troubles with the interfaces and overly relied on its users' habits rather than easy-to-use user interfaces.
The thing is that users don't care much about technical aspects of the metaverse; their main priority is the intuitive and smooth experience that allows them to interact with others and engage meaningfully with the environment. Unfortunately, many metaverse experiences are too complex and challenging to navigate, with too many options and features that overwhelm users. The lack of intuitive navigation and utilisation of the metaverse can lead to user frustration and confusion.
Another problem with metaspaces is that some developers create disjointed experiences without clear tasks and goals. It can have users wander aimlessly, confused and unsure what to do and how to engage with the metaverse. In this case, the strategic product design can have a significant impact. Developers can enhance user engagement and satisfaction by prioritising simplicity and user-centric design.
In addition, some metaverse experiences are troubled with tech issues, including lagging, crashing and slow loading time, which can hinder engagement and even drive the users away completely.
As we indicated above, technical issues are among the main metaverse problems. However, the reason behind the rise of such matters is the sheer amount of computing power that the metaverse requires.
The digital world provided by the metaverse is constantly transforming and evolving in real-time, creating large amounts of data. It requires significant processing capacity, which can overload the hardware. For instance, some metaspaces need high-end PCs or specialised equipment inaccessible to the broader audience.
Moreover, modern VR equipment doesn't provide a seamless user experience. It's either too heavy and wired but powerful enough or lightweight and wireless but laggy and weak. Nowadays, devices usually fall short of satisfying the wide-ranging and diverse needs of the target audience.
And one shortcoming of the metaverse is that some of its algorithms are ineffective or way too complex. For example, some user behaviour-tracking algorithms failed to obtain accurate results. Consequently, user engagement and satisfaction can drop due to irrelevant recommendations or a struggle to find relevant information.
So, with all these issues, is the metaverse doomed to fail? Not necessarily. While the experiment with user experience design for the metaverse must be improved, this technology still has potential.
Check our article from the R&D team, where we describe our own meta art gallery prototype, share our findings and possible use cases: Exploring the Potential of Metaverse With Our Meta Art Gallery Prototype
For example, AI could be used to improve the user experience in the metaverse by anticipating user behaviour and providing relevant recommendations. By analysing user data and patterns, AI could help designers create metaverse environments tailored to reach specific goals and fully satisfy their users' needs.
Some of the potential disadvantages that have been discussed regarding the metaverse include:
The metaverse may not solve major issues, but it can aid in preventing social isolation. It holds the potential to diversify leisure activities by introducing new forms of entertainment and media consumption. Moreover, the metaverse opens new e-commerce opportunities, enabling businesses to connect with customers innovatively and generate additional revenue streams.
There are some people who believe that the metaverse is destined to fail. In 2022, it was reported by DappRadar that the virtual world Decentraland only had 38 "active daily" users, leading to doubts about its potential success. However, we cannot be so absolute in our judgement. As we stated in our article, while the user experience design for the metaverse needs improvement, there is still potential for it to succeed in the future.
Fintech allows to digitise traditional banks’ financial operations, allowing users to open bank accounts or invest in financial products online. Moreover, with AI and machine learning technologies, financial institutions can now analyse larger amounts of data and provide more personalised services to customers.
This surge in fintech apps usage has been driven by growing demand for contactless payment apps and other digital banking tools necessitated by the pandemic. But it’s more than a passing phase; it reflects a profound shift in financial services industry.
To keep up with the pace of modern times, companies in the financial and banking sectors must incorporate scalability and flexibility into their operations. It will allow them to effectively adjust to a swiftly changing market landscape and evolving regulations. Let’s review top fintech trends that help enterprises to do so.
Digital banking is a popular and preferred method of money management for many—and has been since before the pandemic struck. But digital-only fintech solutions and services that negate the need to stand in lengthy queues at physical banking locations have gained real ground since.
Digital banking encompasses services like P2P transfers, cryptocurrency sales, digital wallets, contactless payments with free transfers, and international remittances. And big innovations in this area will continue to make it easier for people to take care of all their banking needs; anytime, anywhere.
As of 2022, 78% of Americans preferred conducting their banking activities through mobile applications or websites.
Arguably the most important aspect of digital-only banking, however, is its potential to reach a wider demographic than has ever been possible before. According to a recent World Bank report, there are still up to 1.7 billion people without access to the global banking system. So the significance of opening up access to essential financial services cannot be underplayed.
A report by the International Data Corporation (IDC) indicates that investment in public cloud infrastructure and services will have doubled between 2019 and 2023—with the banking sector accounting for roughly a third of spending.
Driving the move toward a cloud-based model is the growing prominence of open banking, which is gaining ground thanks to its ability to provide greater transparency. Agile fintech disruptors favour the cloud because it allows them to foster collaborative partnerships with developers. And large banks are now getting on board with the public cloud model because it supports a range of Platform-as-a-Service (PaaS) options.
Furthermore, operational challenges brought about by the pandemic mean that financial institutions and banks have little choice but to rely more heavily on cloud-based solutions from here on out.
RPA has been adopted by a variety of sectors and, as a fintech solution, has the ability to streamline operations, reduce the human burden of repetitive tasks and greatly improve banking efficiencies. This will speed up and reduce the cost of many of the time-consuming back-end processes involved in running a financial institution, such as account maintenance, new customer onboarding and credit processing.
Moreover, automation can be harnessed to build a strategy for service excellence that sets financial institutions apart from the competition, including hyper-personalisation, true data integration and the ability to act on real-time insights to intuitively know what customers want.
If you would like to get more insights into the capabilities of RPA as well as its security vulnerabilities, check out this whitepaper: Top 10 Security Risks in Robotic Process Automation
Financial consumers are feeling increasingly time-pinched as they try to juggle home and work commitments with managing their personal finances. With AI and machine learning technologies, innovative fintech startups and businesses can automate financial decision-making and save their customers valuable time.
AI and ML are also enabling fintech firms to harness Big Data, to find meaningful patterns in customer behaviour that can lead to smarter financial decision-making. With this new intelligence, they can effectively tailor their products and services to match consumer desire.
Just as personalisation is becoming the expected standard across many other industries, like retail and healthcare, so to will it become the norm within banking. Other fintech applications of AI and machine learning include chatbots, trading algorithms, policy-making, fraud prevention, risk management and compliance.
Tying in with data science services, AI and machine learning, customer intelligence tools can be used conjunctively to harvest valuable insights from widely dispersed and oftentimes raw customer data. Fintech companies can use customer intelligence platforms to gather and analyse customer information including basic details, brand interactions, and customer survey data.
Furthermore, powerful linguistic analysis, language identification and pattern matcher annotators can garner a wealth of voice-indicative data from telephone conversations between customers and customer service call centres—most of which is unstructured and untapped. This technology can help brands understand a customer’s intention when they call, their behaviour, and their perception of the brand/product/service they’re calling about. In doing so, it will enable companies to better adapt their services to meet customer expectations.
There is a rise expected in the global 'Estimated Cost of Cybercrime' from 2023 to 2028, with an estimated overall surge of $5.7 trillion. After experiencing compound annual growth rate for eleven years, this indicator is predicted to reach its highest point at $13.82 trillion by 2028.
The proliferation of IoT technologies across all industries, not least the financial sector, has created a wealth of new opportunities for cybercriminals to exploit.
Given the nature of the information held by financial institutions, it’s unsurprising that cybersecurity represents one of the biggest focuses for the sector moving forward. In fact, the financial industry is one of the top three targets for cybercrime, accounting for around 10% of all annual attacks.
According to a recent Deloitte report, up to 64% of banking businesses are expected to plough investment into combating cybercrime in 2021 and beyond.
With the aforementioned rise in cybercrime, the financial technology innovators are having to think of new and infallible ways to protect their customers’ sensitive financial data. Passwords are coming under increased pressure from evermore advanced criminal technologies, and this is why biometric security measures are the next logical step in safeguarding financial security.
Many are already familiar with things like fingerprint ID, but an increasing number of banks, including HSBC and First Direct, are looking to the trends in fintech like face and voice recognition to keep their customers safe.
This not only has the benefit of being far more secure than a password but it’s also much easier for the customer. Instead of having to remember endless combinations of letters and digits, and answer multiple questions to access things like telephone banking, they can gain access to their accounts simply by using their biometrics. It also benefits the banks by making authentication quicker and more efficient, and enabling them to remove certain human touchpoints.
The technology can be applied at cashpoints too, removing the need for a traditional PIN.
The finance and banking sectors are experiencing a transformative wave driven by innovative fintech trends. Financial institutions need to flex their models to support remote operations while adopting the latest fintech trends to innovate their offerings and enable tailored, on-demand banking services for the masses.
The integration of AI and machine learning technologies has paved the way for autonomous finance, allowing for automated decision-making and personalised services. The importance of customer intelligence tools in gathering and analysing dispersed customer data cannot be overstated, providing valuable insights to adapt services to meet customer expectations.
However, amidst these advancements, the financial sector faces growing challenges in cybersecurity, with an expected surge in the global cost of cybercrime. Thus, biometric security systems, including face and voice recognition, represent a crucial frontier in safeguarding financial data.
As we've covered in our article the fintech industry is dynamic, with several trends shaping its landscape. Here's brief overview of the key trends in the financial sector:
The most commonly used fintech service can vary depending on geographical regions and user preferences. However, when considering a worldwide perspective, digital payment services and mobile wallets are extensively embraced.
The future of finance and banking will continue to be transformed by ongoing developments such as biometric verification, financial solutions enhanced by artificial intelligence, applications for neobanking, and the integration of finance into various platforms.
We are entering a new digital age, redefining customers' relationships with products and services in the insurance industry. Their behaviour changes and fuels the need for new customer-oriented products, namely faster and more efficient personalised insurance services that provide instant access to data through digital channels. And this is where the insurance software comes in handy.
Insurance companies have rapidly transformed the entire sector using technology as a key competitive advantage. Incumbent companies are also taking quick steps to integrate into this new environment. Therefore, we will consider the most relevant innovative technologies and analyse their impact on basic insurance practices, such as underwriting, insurance pricing, claims processing and fraud detection.
Let’s look at how innovative technologies are driving the industry's transformation. We’ll focus on three key pillars: IoT (Internet of Things), data management, and digital twins.
By leveraging IoT, data management, and digital twin technology, digital solutions can potentially transform the insurance sector. It can lead to more precise risk assessment, elevated customer experiences, and increased operational efficiency.
The IoT devices can have a dual impact on the insurance industry. On the positive side, IoT solutions can help insurers reduce risk and enhance personalisation by providing real-time data on customer behaviour, like driving patterns or health and fitness habits. Meanwhile, there are concerns about data breaches and cyber attacks, which could compromise sensitive customer information.
Let’s take a closer look at the positive and negative sides that IoT can have on the insurance industry, as outlined in Deloitte's research.
Advantages | Disadvantages |
---|---|
Data provided by IoT devices can enable insurers to make better risk assessments and lead to reduced number of claims. | With the decrease of the potential risk levels, the need for insurance may also decrease. |
Besides benefiting from managing risks, insurers can cooperate with manufacturers to proactively minimise potential dangers. | Certain risk pools may become smaller, leading to a potential increase in insurance costs for those seeking coverage. |
The potential for a significant decrease in the number of losses. | Opting for product liability coverage may be more financially beneficial than switching insurance products due to human error. |
Offering microinsurance packages and establishing dynamic pricing that can adapt to changing demand. | Privacy and security issues need to be addressed. |
Given that insurance primarily relies on data, any innovation like IoT that enhances data accessibility serves as an efficiency booster for insurance companies. Let’s explore the potential impact of IoT on insurance in detail.
1. Enhanced customer behaviour insights
The traditional methods of determining insurance premiums, such as solely age and car model, are no longer applied. With IoT, insurers can access data like speed and brake usage, preferred routes, and even distractions caused by cell phones. It helps to make more informed decisions about pricing without causing any undue stress.
By having access to a vast amount of data, insurance companies can provide better risk assessment and underwriting processes. It results in happier customers and reasonable premiums.
2. Streamlined claims processing
Efficient claim processing is a crucial aspect of the insurance industry, as it has a direct impact on customer satisfaction and the financial success of insurance companies.
Every year, American consumers lose at least $308.6 billion due to insurance fraud.
Let's continue with car insurance as an example. In the past, submitting an initial notice of loss (FNOL) could be challenging, especially if accidents caused delays in informing the insurance company. This not only increased fraudulent claims but also left customers feeling dissatisfied. However, with IoT, airbags can immediately alert insurers and speed up the FNOL process - leading to happier customers and reducing fraud.
3. Personalised insurance practices
Insurance companies can offer personalised policies without categorising customers into risk groups. It has led to the introduction of pay-as-you-go (PAYG) plans that calculate premiums based on actual usage, resulting in reduced costs if you use your vehicle less often. Such a system offers greater fairness and flexibility to policyholders.
4. Transformation of the insurance landscape
While traditional insurance used to cover risks like house fires, smart homes equipped with telematics can now detect gas leaks and take preventive measures.
This transformation presents both opportunities and obstacles. IoT devices allow for early detection and prevention of potential risks. Meanwhile, some insurers may need to reconsider their strategies to maintain profitability.
5. Data privacy and security concerns
In some cases, IoT devices may collect data that could be attractive to other industries or represent entirely private information to the customer. It raises concerns about data misuse and the possibility of intrusive sales tactics that could negatively impact individuals' privacy and well-being.
Data management is crucial in helping insurers understand and leverage the information and actionable insights gathered from IoT devices.
Below are key areas that help insurers to make the most out of IoT data management:
The digital twin is the technology that allows the creation of a digital representation of any physical object. With its help, companies can extract "virtual data". The integration of digital twins as a source of information holds great potential for the insurance industry in years to come.
In the past, insurance companies have used historical data to perform risk assessments. But now, thanks to digital twin technology, they can predict and evaluate any potential risks with ease.
The combination of digital twins and IoT encourages insurers to switch to a new approach focusing on preventing or minimising damages rather than simply providing compensation. To gain a better understanding, let's consider an example:
Imagine a smart factory that uses digital twins and IoT technology. The factory’s complex machinery, each equipped with IoT sensors and represented by a digital twin.
Digital twins constantly combine and analyse data that are represented by physical assets, such as temperature, vibration, etc. When a machine has abnormal behavior, special sensors detect it and transfer data to its digital twin. The twin then simulates the potential consequences - how likely the device is to break down and how much fixing or replacing will cost.
Based on those predictions, digital twins can notify the maintenance team when to fix machines so they don't break unexpectedly. It helps keep everything running smoothly and saves time by only fixing things that need it instead of adhering to a set maintenance schedule.
In addition, digital twins can use all the gathered data to identify trends and patterns, which can be later used to improve production processes, save energy, and produce less waste.
To sum up, digital twins can help to improve the following insurance processes:
Home insurance policies can benefit from various insurance tools and IoT data collection methods to assess risks, tailor coverage, and offer personalised services for customers. Here are some examples:
IoT sensors and smart home devices
With IoT sensors like smart thermostats, water leak detectors, or smoke detectors installed, homeowners can have real-time data on temperature, humidity levels, water leaks and security at the touch of a button. Not only does this provide convenience, but it also ensures that insurers are kept in the loop with any potential issues.
IoT devices offer features such as smart locks, video doorbells and motion sensors that ensure protection against burglaries or break-ins. And that's not all; IoT technology can help older people who need extra assistance in their homes. Fall detection sensors, medication reminders and emergency call buttons can work together seamlessly to ensure peace of mind for both seniors living alone and their families.
Video monitoring and security systems
Video monitoring systems and IoT devices provide real-time monitoring and alerts to homeowners and insurers. It not only enhances security measures, but this technology is a game-changer for insurers operating in disaster-prone areas, enabling them to respond to potential threats and emergencies immediately.
Life insurance policies can be enhanced by using advanced insurance tools and IoT data, which help to evaluate risks accurately, better customise coverage plans according to individual needs, and provide highly tailored services. Allow us to illustrate some examples:
Wearable devices and health apps
Wearable devices like smartwatches and fitness trackers can collect valuable data, including health indicators, activity levels, and sleep patterns. These devices can assess risks accurately, allowing for more personalised coverage options in life insurance policies.
By partnering with health apps or platforms tracking lifestyle choices such as nutrition intake or exercise habits in real-time, insurers can offer personalised coverage.
Digital health records integration
By collaborating with healthcare institutions, insurers can gain a wide view of an individual's medical history and provide tailored coverage options based on accurate risk assessment. With EHR integration, pre-existing conditions and ongoing treatments are considered for more personalised coverage plans.
Auto insurance policies can also benefit from digital solutions. As we mentioned above, IoT devices can help assess driving behaviour. Similar to the benefits it brings for home and life insurance, this technology can customise coverage and provide personalised services. Here's more:
Telematics devices
With sensors embedded in vehicles, insurers can gather real-time data on speed, acceleration, braking and mileage - allowing them to tailor coverage based on actual risk factors.
On the other hand, customers can be rewarded with lower premiums and additional coverage options by practising safe driving habits.
GPS tracking
GPS-enabled devices can be used in auto insurance policies to track vehicles and aid in stolen vehicle recovery. These devices can assist in locating stolen vehicles and recovering them quickly, reducing insurance losses.
Driver assistance systems
Advanced driver-assistance systems (ADAS) in vehicles, such as lane departure warning, collision avoidance and adaptive cruise control, have enhanced road safety for all drivers. Insurance companies can consider the presence of ADAS features when assessing risks and pricing premiums.
It is of utmost importance that insurers must prioritise data privacy and security while gathering IoT data, regardless of which insurance policy is being considered. Insurers must acquire appropriate consent, adhere strictly to data protection regulations, and guarantee open communication with policyholders regarding the use of their data and measures taken to safeguard their privacy.
Digital insurance solutions are reshaping traditional practices. The statistics echo this statement, with 65% investing in robotic process automation (Deloitte) and 44% prioritising digital tools (McKinsey).
The digital transformation we all encounter redefines customer relationships across industries. Insurers are adapting to this shift, integrating innovative technologies to enhance practices like underwriting, claims processing, and fraud detection.
IoT, data analytics, and digital twins emerge as technological pillars driving change. These innovations streamline processes and foster personalised customer engagement, loss prevention, and risk mitigation. But despite the obvious benefits, concerns regarding privacy and security rise. So, the insurers should pay special attention to safeguarding the sensitive information.
The fusion of technology and insurance is not just a temporary trend—it's a fundamental shift. Embracing digital solutions is no longer an option but a necessity for insurers to thrive in this ever-evolving landscape.
Digital solutions in insurance are the technologies that are aimed at streamlining traditional processes and improving customer experience. These may include a variety of online tools, platforms, and technologies such as automation, customer engagement tools, data analytics software and AI technology or IoT devices.
Digital insurance solutions benefit insurance companies by reducing administrative costs, improving efficiency, enhancing customer experience, and allowing for better data analytics and risk assessment.
For the customers, digital insurance solutions bring streamlined claim processing and 24/7 accessibility of their information and services and more custom-tailored policies.
Insurers are encouraged to adopt digital solutions for the following three reasons:
Managing software projects comes with challenges and risks that can disrupt the workflow. We want to share our methods and important parts of software release, specifically about timely delivery.
We've been working on software development for more than 30 years at ELEKS. We know how crucial it is for businesses to get their projects done on time. We don't claim to have invented the wheel, but with our experience, we can confidently say we understand how to get things done on time.
Our process of making sure project is delivered on time and meet our corporate standard involves these steps:
We are deeply invested in our clients' priorities. We understand that changes can happen at any moment, and we must be able to respond accordingly. Agile works well because it helps teams work together and change as needed. By splitting the work into smaller parts, we can deliver working software within shorter cycles. This way, we can find and fix problems early, so there aren't considerable delays or expensive mistakes later in the project.
Moreover, agile methodologies encourage transparency and open communication among project stakeholders. By embracing a culture of continuous feedback and teamwork, teams can quickly adjust and prioritise tasks based on changing requirements and feedback. It ensures everyone shares a common vision and purpose for the project goal. As a result, software solutions are delivered on time and within budget.
Having well-elaborated Non-Disclosure Agreements (NDA), Master Service Agreements (MSA), and Statements of Work (SOW) is essential. These make sure everyone has a clear understanding of their legal obligations.
Legal document templates help us to streamline the delivery process and save time. With our experience in dealing with documents like SOWs and NDAs, we know what areas must be included in legal documents to ensure that all potential situations are covered, and we're ready for any hiccups that might come up during the software project implementation.
We've got specific templates for each service we provide which saves us time during onboarding of new clients or extending cooperation with existing ones, making this part of service delivery as smooth as we can to reduce the time needed for negotiations. By taking such a proactive approach, we minimise potential delays.
By utilizing our established risk management approach and best practices database, our teams can identify and mitigate potential risks efficiently, allowing us to deliver quality software solutions without unexpected surprises.
We consider possible problems that could slow down our work, including factors outside our control, like customer needs and market requirements. It helps us to stay ready for any issues that may come up. We talk openly with the customer about possible problems. This way, we can deal with issues as they arise and ensure the customer knows what's going on.
Besides how we manage and deal with risks, we also make use of our best practice database. This collection has all the ways and lessons we've learned from past projects to use for new projects. It prevents us from making the same mistakes again and helps us make software faster without compromising on quality.
We guarantee to deliver the software on time through careful planning and roadmapping. But we always start by defining clear goals and requirements - because we are firmly convinced a project without them is doomed to fail.
We don't simply wait for clients to hand us the thoroughly prepared list of polished requirements; we actively identify and create them based on the customer's needs. Then we confirm these with the client so that there's a thorough understanding of all demands before moving forward.
Our release plan and roadmap provide a clear outline of key milestones, expected features, and deadlines for each development phase. We maintain focus by communicating actively with stakeholders and refining requirements. These approaches ensure that the team stays on track, and necessary adjustments are made along the way so that everyone involved remains satisfied with the progress.
Our roadmap is our guiding star, helping us navigate the complex development landscape, refine our strategies, and optimize our efforts to achieve the best possible results for our clients. However, creating a roadmap not only guides our actions, but also gives us a clear view of our final destination.
Our company’s monitoring and control approach ensures that the software is delivered on time. By collecting and analyzing data, such as how fast work progresses or how quickly bugs are fixed, we can identify patterns and solve problems swiftly.
We use standardized systems across the company, like JIRA, AzureDevOps, and Trello, together with reporting dashboards to keep everything organized. These tools help teams track progress, manage tasks, and find obstacles. This approach ensures that we keep everyone in the loop, helps to stay on track with our goals and make changes when needed.
The data produced by project management tools is used to compile reports which demonstrate the project's status in its entirety - this allows stakeholders to see what has already been achieved, what steps have been taken and highlight any challenges we face.
We promote open communication and collaboration among the development team and stakeholders to foster a culture of transparency. Our emphasis on client communication ensures a clear understanding of their needs. Conducting regular stand-up meetings, sprint reviews, and retrospectives helps identify any potential roadblocks and address issues proactively. By involving the entire team in client communication, we ensure unbiased decision-making and provide the customer with a comprehensive perspective.
From the technical standpoint, we employ CI/CD pipelines to maximize the efficiency of delivery process. CI/CD is not just a methodology; it's a mindset that accelerates development, minimizes risks, and ensures engineering excellence throughout the software development lifecycle.
CI/CD approach automates the entire software delivery process, from building to deployment. With automated testing frameworks and comprehensive test suites in place, we can detect bugs and issues as soon as they arise. By running tests as part of our build pipeline, developers ensure that their software is stable and reliable.
An essential aspect of our team's workflow is implementing version control and frequent, incremental code changes in a shared repository. It allows us to identify integration problems early on, before they become major headaches down the line. This collaborative process significantly reduces the chances of late-stage defects, enhances team synergy, and keeps development on track.
By applying the practices outlined above, we're taking every possible precaution to anticipate and mitigate any unforeseen circumstances to guarantee timely software delivery. A combination of agile practices, solid documentation, sound risk management tactics, clear communication, and technical excellence creates a supportive environment for successful software deliveries.
In addition, we believe in continuous improvement. By reflecting on our processes and making enhancements, our teams consistently refine their practices, which leads to increased competence and proficiency over time.
These techniques embody ELEKS' value-based principles, reaffirming our commitment to delivering quality software projects to clients on time, with a guarantee of excellence.
The process of delivering software successfully requires a well-structured and efficient approach. It includes careful planning, clear and effective communication, agile methodologies, performance monitoring using key metrics, automated testing, and continuous improvement practices. By prioritising these elements, we can successfully deliver top-quality software products that meet customer requirements and provide maximum value.
Preparing a software product for the market involves using a software delivery model. There are various ways to approach this process, which may go by different names such as software delivery lifecycle, software delivery pipeline, or software delivery process. Agile, Waterfall, and DevOps are among the most popular software delivery models.
It's a team who work together to develop, deliver and maintain software products or solutions. This team is responsible for taking a software project from its initial idea to its final implementation, ensuring that the end product meets the needs and expectations of all stakeholders involved. The team's main objective is to ensure the software is delivered on time, within budget, and to the highest quality standards.
In software engineering, delivery refers to the process of releasing software to users or clients, making it available for installation, implementation, or use by customers or stakeholders.
The breadth of knowledge and understanding that ELEKS has within its walls allows us to leverage that expertise to make superior deliverables for our customers. When you work with ELEKS, you are working with the top 1% of the aptitude and engineering excellence of the whole country.
Right from the start, we really liked ELEKS’ commitment and engagement. They came to us with their best people to try to understand our context, our business idea, and developed the first prototype with us. They were very professional and very customer oriented. I think, without ELEKS it probably would not have been possible to have such a successful product in such a short period of time.
ELEKS has been involved in the development of a number of our consumer-facing websites and mobile applications that allow our customers to easily track their shipments, get the information they need as well as stay in touch with us. We’ve appreciated the level of ELEKS’ expertise, responsiveness and attention to details.