Category: Blogs

Introduction

It’s been over a year since the concept of Genenerative AI took the world by storm. Yet, even today, the excitement around the technology and the (seemingly impossible) things it can achieve is still at its peak. Of the many applications of Generative AI, its use in and impact on digital factories is likely to be extensive. From intelligent assistance to widespread autonomy – GenAI is poised to play a huge role in carving the factory of the future.

Let’s learn more about the possibly transformative role of GenAI in digital factories.

While GenAI delivers several benefits for manufacturers, its long-term success depends on how well it is implemented. Read on as we showcase GenAI’s role in digital factories and how manufacturers can successfully bring their GenAI vision to life.

Driving Unmatched Levels of Efficiency with Generative AI

A recent BCG survey of 1,800 manufacturing executives ranked Artificial Intelligence (including Generative AI) as the most disruptive technology on their radars. The collective belief is that it could play a significant role in enhancing factory activities and augmenting workforce productivity in novel ways.

GenAI opens doors to several benefits in the manufacturing sector. From intelligent quality control to proactive product servicing, document generation to product R&D, supplier contract management to automated troubleshooting and self healing: manufacturers across sectors can leverage GenAI in three ways:

1. Assistance systems: GenAI applications can act as assistance systems, enhancing the efficiency of the workforce. For example, instead of manually writing code for a new feature, programmers can provide a natural language description of the desired code. A GenAI code generator can then use this text input to automatically generate code. This minimizes the time and effort required for engineering new solutions while also reducing related expenses.
2. Recommendation systems: GenAI tools can provide valuable recommendations that help workers identify the best methods for specific tasks. In the realm of predictive maintenance, GenAI can act as a valuable tool. Instead of having workers plan and run a long list of maintenance activities, Generative AI can provide recommendations on the next steps, enhancing productivity and reducing costs.
3. Autonomous systems: GenAI platforms can also perform autonomously, requiring no human intervention. For instance, in the event of a cyberattack, GenAI systems can enable machinery to adapt to the new environment. They can shut themselves down and autonomously rebuild themselves with new software patches and firmware updates.

Bringing the GenAI Vision to the Production Floor

GenAI can bring exceptional benefits to digital factories. However, the complexity of the technology makes having a robust foundation crucial. If you want to successfully bring your GenAI vision to the production floor, you must carefully embark on the implementation journey.

Here’s how you can set the right foot forward:
· Source the right data: GenAI tools are fundamentally data-dependent. Therefore, you need to collect and assimilate data from a variety of sources. These range from sensors to IoT devices, factory equipment, warehouse management systems, ERP and CRM systems, etc. The more relevant data you collect, the more comprehensive outcomes your Generative AI applications can deliver.
· Clean and process the data: Once all relevant data has been collected from different manufacturing processes and systems, it is important to clean and process this data. You need to cleanse, filter, and contextualize data that is inputted into the GenAI models. You must also have proper governance policies in place to ensure secure and authorized sharing, archiving, and deletion of critical manufacturing data.
· Build the right hosting model: The model you choose can make or break your GenAI vision. Therefore, take time to understand the different models and choose one that makes the most sense for your business. For instance, you can partner with a qualified vendor to host the large language models (LLM) required for your GenAI application. You could also opt for an open-source foundational model or develop your own LLM for greater control and customization.
· Ensure enough computing power: GenAI applications need to handle humongous volumes of data. Therefore, you need to ensure they have enough computing power. Investing in cloud-based central processing units (CPUs) and graphics processing units (GPUs) is a great way to have the memory bandwidth and power needed for your GenAI application to work efficiently.
· Strengthen connectivity: Strong connectivity is essential to enable real-time communication and data availability of GenAI applications. While upgrading your networks, make sure to factor in latency and bandwidth requirements. Relying on cloud and edge technology is also a great way to boost reliability, processing speed, and power.
· Establish governance: The amount of data GenAI applications rely on makes them a prime target for hackers. To safeguard against attacks, it is important to establish the right levels of governance. Implement robust identity and access management and data protection measures to improve threat detection and response, risk analysis, and recovery planning.

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Introduction

The manufacturing sector is often considered a key driver of economic growth. Despite this, there are alarming concerns. Rising production costs, global economic uncertainties, pollution concerns, etc. have put considerable pressure on optimizing factory production.

However, over the years, the sector has been subject to many diverse challenges as well. For instance, the sector contributes about 1/3rd of all greenhouse gas emissions globally. Growing competition and rising inefficiencies are putting profitability at risk too. By some estimates, income from manufacturing has contracted over the last 5 years by over 8%. It’s also proving to be hard to recruit people for manufacturing jobs. There are nearly 800,000 open positions in USA’s manufacturing sector currently and projections are that this number could grow to 2.1 million by 2030.

As with other sectors, manufacturing leaders are leveraging technology solutions to solve the challenges of modern factories. Their core objective is to maximize the utilization of assets currently at their disposal.

The concept of digital factories or smart factories has become a mainstream entity in the manufacturing world. And with it, ThingWorx has become a “go-to” solution in the industry to help manage the end-to-end operations of a digital factory.

The rise of ThingWorx

In simple terms, ThingWorx helps manufacturers embrace the power of connected machinery, automation, and digital experiences. It is an Industrial Internet of Things (IIoT) platform that enables industrial establishments to harness data from their operations and drive value from it. Supported by a range of accelerators, pre-built solutions, and developer tools, ThingWorx is the holistic platform for manufacturers to pivot meaningfully and effectively into Industry 4.0.

How does ThingWorx help smart factories?

Smart factories operate based on data acquired from their ecosystem and processed for efficient outcomes at lower costs and increased productivity. With PTC ThingWorx, it becomes easier to set up such a smart establishment, manage its data connections using Kepware, and track and monitor for success.

ThingWorx transforms a manufacturing facility into something like a digital ecosystem wherein manufacturing outcomes are measured by data like a productized service. Let us explore the top areas where ThingWorx becomes a key pillar of modern digital factories:

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Introduction

“Every once in a while, a new technology, an old problem, and a big idea turn into an innovation.”

– Dean Kamen (Inventor, Entrepreneur, and holder of over 1000 patents)

Amid today’s highly dynamic and competitive business environment, innovation has become necessary for growth. Those who can innovate their products and services (and their approach to customer service) have the best chance of achieving all-around success.

Read on to learn how innovation consulting services help organizations drive growth, enhance competitiveness, and foster a culture of performance.

Introduction

“Every once in a while, a new technology, an old problem, and a big idea turn into an innovation.”

– Dean Kamen (Inventor, Entrepreneur, and holder of over 1000 patents)

Amid today’s highly dynamic and competitive business environment, innovation has become necessary for growth. Those who can innovate their products and services (and their approach to customer service) have the best chance of achieving all-around success.

Read on to learn how innovation consulting services help organizations drive growth, enhance competitiveness, and foster a culture of performance.

Why Technology Innovation Has Become Critical to Business Success

Change is the only constant in this era of business operations. To stay relevant and meet the evolving needs of customers and the market, organizations are compelled to make some permanent changes. As digital transformation becomes the key enabler of new business models, one of the most significant changes enterprises must make is concerning technology.

Today, irrespective of the business sector, technological innovation is a crucial strategic imperative for long-term growth and success. Through the development and implementation of new or improved products, processes, and approaches, technological innovation allows businesses to revamp themselves and enjoy higher levels of growth and competitiveness.

The use of digital twins, exploring the power of Generative AI, the microservices revolution, and the limitless possibilities of Augmented Reality are all just examples of technological innovation that can deliver a wide range of benefits for individuals, businesses, societies, and the economy.

The key advantages of embracing technological innovation include:

1. Competitive Advantage

Organizations that embrace technology innovation can develop unique products, offer improved services, and optimize their operations, making them more attractive to customers and investors.

2. Increased Efficiency

Technology innovation leads to more efficient processes, reducing costs and resource consumption. Through automation, streamlined workflows, and better use of data, businesses can boost operational efficiency.

3. Improved Collaboration

The proliferation of digital technology can facilitate communication, collaboration, and the exchange of ideas, leading to a more interconnected business environment.

4. Environmental Sustainability

Innovations in clean energy, resource management, and sustainable practices can also contribute to a more environmentally friendly future.

5. Resilience and Adaptability

Organizations that innovate their technology ecosystem are better equipped to adapt to changing market conditions, disruptions, and uncertainties. By encouraging a mindset of continuous improvement and resilience, it enables businesses to stay a step ahead of the competition.

But even when organizations accept the value of embracing technology-led innovation, they struggle to make that leap forward. In most cases, this is because they do not have the technology understanding, deep experience, and rich background in the cutting-edge technologies that enable this transformation. That’s where the role of an Innovation Consulting partner becomes crucial.

Understanding the Key Aspects of Innovation Consulting Services

Working with an expert organization can help enterprises across sectors jump-start their technology-led innovation agenda. The Innovation consulting services provided by these expert organizations are tailor-made to address the key challenges enterprises face today.

For instance, the innovation services will contain solutions and approaches that enable organizations to overcome the uncertainty caused by various market and economic factors. Innovative technologies can help them to identify new ways to reach consumers and build new distribution channels or business models.

Innovation partners are not just focused on the immediate problems. Given their understanding of the rapid evolution in the world of technology, they also know what’s likely to be possible with technology in the medium-term future. Thus, they can help organizations develop a long-term vision, design the right solutions, and ensure that investments in today’s innovation are a part of building tomorrow’s competitive advantage.

That said, there are the key aspects of innovation consulting services:

1. Market Research

Understanding the market landscape is crucial for successful innovation. Innovation consultants can conduct detailed market research to identify customer needs, assess the competitive landscape, and spot gaps that can be addressed through innovative solutions.

2. Technology Exploration

Innovation consulting services also help unearth emerging technologies and trends that can be leveraged to create a competitive advantage. Consultants constantly scout for new technologies, assess their potential impact, and recommend ways to integrate them into the business’s operations.

3. Strategy Development

Innovation consultants work with organizations to develop a clear and effective innovation strategy. They help outline the goals, focus areas, and resource allocation for innovation initiatives and ensure efforts are aligned with overall objectives.

4. Idea Generation

Innovation consulting services help in generating creative and novel ideas. Through brainstorming, ideation workshops, and trend analysis, they help align new product ideas with the organization’s long-term objectives, evolving market trends, and future customer needs.

5. Prototyping and Testing

These services can also bring ideas to life through prototypes. By conducting testing, they help validate concepts before full-scale implementation while also conducting pilot tests and refining ideas based on feedback.

6. Process Improvement

Innovation consultants analyze the organization’s existing processes to identify inefficiencies and opportunities for optimization. They recommend changes in workflows, organizational structures, and collaboration methods to foster a more innovative environment.

7. Change Management

Innovation often requires a shift in organizational culture and mindset. Innovation consultants can provide change management support to help employees adapt to new ideas, processes, and technologies and sustain long-term growth and innovation.

8. User Training

Building innovation capabilities within the organization is essential for long-term success. Consultants may offer training programs, workshops, and skill development initiatives to empower employees with innovative tools and methodologies.

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Introduction

The cloud has transitioned into the go-to place for any business to build its digital infrastructure, irrespective of its domain. Gartner predicts that 95% of all new digital workloads will be deployed in the cloud by 2025.

Over the past decade, cloud computing has managed to overcome some of the most pressing concerns against its mass adoption from the business fraternity. The major concern was surrounding security. Cloud is today a far more secure place for hosting major digital initiatives than it was in the early days. But there is a word of caution, especially for non-tech startups and product companies who are jumping into the cloud to build their digital backbone.

The cloud is secure, but not fully.

What Does It Mean by Not Fully Secure?

This question must be brimming in the minds of most founders by now. The answer is that there are too many misconceptions that enterprise leaders have surrounding cloud security. By firmly believing them, enterprises often end up on a pathway that will eventually lead to security compromises in their cloud infrastructure, which is never a good thing.

Let’s have a deeper look into some of the top misconceptions surrounding cloud security that non-tech founders have and what exactly should be their course of action to correct these misconceptions.

Security is a Vendor Headache

The biggest mistake most enterprises make from a security standpoint is believing that securing their cloud infrastructure is the sole responsibility of the vendor. Today, organizations partner with different cloud service providers for a variety of needs, such as storage, computing resources, SaaS tools or platforms for various departments, etc. This helps them achieve faster growth at lower costs and with lesser effort. But the challenge arises when they believe that end-to-end characteristics of the cloud service, which include areas like security, will be fully handled and managed by the vendor.

There will be several areas where the vendor may have limited control over transactional and information exchange processes that happens regularly. From vulnerable APIs to unguarded storage repositories, there are numerous areas where product companies must contribute their fair share of vigilance and due diligence to thwart threats. Enterprises must work together with their cloud partners to understand the entire landscape of vulnerabilities and clearly arrive at a responsibility matrix to cover all areas.

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Introduction

Ever since the late 2000s, the thrust to push for a sustainable future has been higher in most countries. Climate change, depleting fossil fuel reserves, the impact of the COVID-19 pandemic, and wars on global energy dynamics have been instrumental in creating awareness in the general population about clean energy alternatives and the need to have a sustainable energy ecosystem.

The discussions surrounding renewable energy have gained renewed focus, and studies estimate that the global renewable energy market will be worth over $2.1 trillion by 2025.

The harnessing of renewable energy is not a new concept. From wind turbines to nuclear power stations, we have seen a fair share of progress even in the early 80s. The goal is to take renewable energy into the limelight as the major power contributor globally in today’s economic and geographical context. This can happen only through digital transformation.

The focus is on leveraging digital innovations to distribute and manage renewable energy systems and drive efficiencies across the entire value chain. Two of the most prominent technologies that can drive this journey forward are the Internet of Things (IoT) and Analytics.

Let us explore the top practical use cases for IoT and Analytics in the renewable energy sector:

Sensor-Driven Load Management

The first step to optimizing any kind of energy production and distribution is to ensure failproof measurement and tracking of the same. This is where IoT-enabled sensors coupled with data analytics come into play.

Today, sensors can be deployed across various production and distribution points of renewable energy to get real-time visibility into different energy parameters. The sensors could range from simple pressure and temperature monitoring sensors to complex ones that deal with detection motion or proximity, acoustic signals, or even light.

The data captured by sensors is then processed with data analytics to get insights that help in the load management of grids connected to the renewable energy generation source. For example, a solar farm that generates renewable energy may produce less power on a cloudy or rainy day.

Sensors pick up the data on production as well as from environmental monitoring, and this data is processed to find out how much alternate power must be made available through other sources to keep the grid online. This is how real value is derived from an IoT initiative powered by data analytics.

Lower Capital Expenditure

Renewable energy sources like wind farms, solar farms, tidal parks, etc., require massive capital investments to set up a base location that can eventually supply power from natural sources. However, there should be a clear understanding of weather patterns and geographical and demographic trends in the region to go ahead with investing initiatives.

With IoT and analytics, it becomes easier to simulate and test possible operational conditions in different locations to create power output models that can be checked for maximum electricity generation potential. This helps companies plan and execute projects at locations that are more viable and offer faster time to realization for power parameters.

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Introduction

In the renewable energy sector, equipment or asset failure can spell serious negative consequences for any company. Renewable energy companies are switching to data-driven predictive maintenance to protect themselves from unexpected failures. Technologies like the IoT and AI are driving the adoption of predictive maintenance in the sector.

As an integral part of predictive maintenance, real-time asset failure prediction can improve uptime and optimize maintenance costs.

But what exactly is asset failure prediction, and how does it work? Let’s discuss it in full detail in this blog.

What Is Asset Failure Prediction?

In recent times, asset-centric industries, including renewable energy companies, have seen equipment failures and safety incidents associated with their machines. In fact, unplanned machine downtime reportedly costs $50 billion a year to industrial organizations.

Asset failure is the exact manifestation of functional failure. For instance, a solar panel has a functional failure when it does not generate the expected volume of solar energy. Without any identified reason or cause, the solar panel is simply found to have low energy output. In the real world, there are multiple reasons for the functional failure of the asset.

Data-based asset failure prediction is a form of predictive maintenance that uses intelligent sensors to predict when an equipment failure is likely to occur. With this method, energy companies can allocate necessary resources to prevent downtime, thus saving on any part replacements.

Next, let’s discuss the business benefits of asset failure prediction in the energy sector.

Business Benefits of Asset Failure Prediction

Successful predictive maintenance in any industry depends on real-time measurement of assets and their performance levels. The asset failure prediction model can predict asset failures based on previous failure patterns.

Here are the top 5 benefits of asset failure prediction for the renewables sector as part of predictive maintenance:

1. Lower Asset Downtime

Regular monitoring of important assets can cut machine failures significantly. Based on this principle, asset failure prediction provides real-time data on the health and performance of every asset. This means maintenance engineers can take action before equipment failure.

2. Lower Maintenance Costs

Predictive maintenance in the renewable industry can reduce capital expenditures in equipment maintenance. By predicting asset failures, energy companies can prevent unexpected downtime and its associated costs. They can also reduce asset repair costs by adapting to data-powered maintenance schedules.

3. Improved Customer Satisfaction

In the renewables sector, asset failure has a direct impact on customers. Utility companies try to avoid any unexpected outages to prevent any inconvenience to their customers. With asset failure prediction, customers are notified in advance about any possible outage or failure. This helps in improving customer satisfaction.

4. Longer Asset Lifetime

By detecting asset-related problems at the earliest, predictive maintenance can also increase the service life of the asset. Energy companies can also reduce the severity of damage caused by a malfunctioning component – and prevent it from affecting other parts.

5. Improved Safety

Along with the accurate prediction of asset failures, predictive maintenance can address any safety-related risks to maintenance teams or asset operators. Maintenance teams can quickly take corrective actions and mitigate these safety risks.

What are the concerns in the renewables sector about applying this technology? Let’s discuss them next.

Industry Concerns About Asset Failure Prediction

The renewable energy industry handles massive volumes of data from their field assets and IoT sensors. While some players leverage this data using predictive maintenance, others are hesitant to implement this model.

Among the main challenges, energy companies fail to understand the business impact of any asset behavior. For example, how does increasing machine uptime improve the company’s bottom line? Energy companies need a top-down approach to identify their business objectives and map them to the machine outcomes.

An additional challenge is the lack of the right data. While utility companies generate massive data volumes, it is complicated for them to extract the right data. For accurate predictions, predictive maintenance models must receive the right and relevant data.

With the increase in connected renewable assets and IoT devices, renewable companies need to maintain a complex ecosystem of products, processes, and services. The challenge for them is to extract real-time data insights from this ecosystem of physical assets and networks.

Digital twins can facilitate this by creating a replica of this complex ecosystem. With digital twin technology, renewable energy companies can effectively:

• Troubleshoot problems in equipment located in remote locations
• Visualize the entire asset ecosystem in real-time
• Connect disparate systems and enable traceability
• Manage the complexities of this ecosystem of renewable assets

Conclusion

Across the growing renewable energy sector, companies are adopting predictive maintenance to reduce downtime and improve the service life of their installed assets. Asset failure prediction is an efficient tool to predict and prevent the failure of renewable assets.

As a solution provider to the energy industry, Pratiti Technologies provides a range of services, including asset monitoring and predictive maintenance. With our technical expertise, we can create a digital twin version of your physical plant, which is useful in improving operational efficiency and performance.

Want to be part of our growing digital ecosystem? Contact us today!

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Introduction

In a recent LinkedIn post, Pratiti Tech’s Co-Founder Prashant Anaskure talked about the inevitability that “the worlds of AI and IoT would collide.” He highlighted that IoT devices generate massive volumes of data that can also drive AI solutions.

In recent years, the growing convergence of AI and IoT is evolving into what is known as Artificial Intelligence of Things (or AIoT). With this convergence, AIoT solutions are gaining more industry acceptance. Some industry applications leveraging AIoT technology include smart homes and cities, wearable devices, etc.

Industry experts estimate that the global market for AIoT solutions will reach $83.6 billion by the year 2027. Recent reports also find that AI-enabled edge computing will be the fastest-growing segment within the AIoT market.

But what exactly is AIoT technology, and what are its business benefits? In this blog, let’s learn more about it.

What Is AIoT?

In his post, Prashant shares as good a definition of AIoT as any by calling it a “smart, connected network of devices that can seamlessly communicate.”

Effectively, AIoT is the technology combined with the connectivity of the Internet of Things and the data-driven knowledge enabled by AI. By integrating AI with IoT, AIoT applies AI-powered machine learning techniques to data collected from distributed nodes or devices. This effectively moves machine learning closer to the data source. Hence, this concept is also called Edge AI or Edge Intelligence – as it enables more scalability, efficiency, and robustness.

Here’s how AIoT technology works:

• AI technology is first embedded into software programs and other infrastructure components, which are then connected with IoT networks.
• Using APIs, each of the hardware and software components operates and communicates with each other.
• As IoT-connected devices gather data, the AI component analyzes the data to provide data-driven insights.

As AIoT-generated data is processed at the edge, it means IoT devices require minimum bandwidth to move the data for analysis, thus avoiding any unnecessary delays.

How does AIoT benefit businesses? Let’s discuss that next.

Business Benefits of AIoT

By combining AI with IoT technology, organizations can leverage a host of business benefits. With AI, IoT can move beyond simply gathering and conveying information. AIoT technology creates “smarter” machines that can make accurate decisions without human intervention.

Here are some of the business benefits of AIoT:

1. Boosts Operational Efficiency

With AIoT technology, IoT devices can analyze data faster to reveal useful data patterns and insights. This capability helps organizations improve their operational data-driven insights, automate manual processes, and fix common problems. For example, an industrial facility can use AIoT tools to automate quality inspections and track if industrial applications adhere to operational guidelines.

2. Enables Real-time Data Analytics

Real-time data analytics and monitoring enable organizations to save valuable time and reduce operational interruptions. AIoT solutions enable continuous machine monitoring in industrial facilities for any defects or abnormalities. This, in turn, facilitates predictive maintenance.

AIoT technology minimizes human involvement and delivers accurate outcomes. For instance, AIoT-connected sensors in the oil & gas industry detect oil leaks through remote monitoring.

3. Reduces Operational Costs

AIoT technology can reduce operational costs in any industry in the long run. Intelligent AIoT-enabled systems enable efficient use of available resources. For instance, AIoT-based smart buildings can control light and temperature settings based on human occupancy. Similarly, smart factories can leverage AIoT to reduce the chances of equipment failure and associated escalating costs.

4. Elevates the Customer Experience (CX)

AIoT solutions can also elevate CX by accurately predicting consumer behavior and preferences. For instance, AIoT enables retailers to determine the restocking time or identify the products that have not attracted any consumer visits.

How does AIoT impact companies building software products? Let’s discuss that next.

Impact of AIoT on Software Products

With AI integration, IoT devices are expected to work smarter and have more real-life applications across industries. Among the latest trends, 5G technology will emerge as the most innovative in the AIoT space – enabling faster data transfers with its improved bandwidth.

Besides 5G, Edge AI is also among the key technologies driving the adoption of AIoT solutions. Edge AI technology can reduce data volumes and improve response time.

So, how does AIoT impact the world of software products and applications? Prashant points out the slew of IoT-enabled applications used in smart homes, smart cities, and wearables. The emergence of AIoT poses a different challenge for existing IoT application developers. This requires them to “re-architect” their internal infrastructure and “relook” at how they process data.

The Way Forward

Is AIoT the “next big thing,” or is it all hype? Along with ongoing development in the IoT space, connectivity like 5G and Wi-Fi will continue to drive AIoT-based innovations. On their part, AIoT solutions can benefit many industrial domains, including oil & gas, retail, manufacturing, and healthcare. They can raise a business’s innovation capabilities and nurture improved operational excellence.

As a technology solution provider,  Pratiti Technologies has built years of expertise in technologies like IoT, edge computing, and data analytics. With our IoT expertise, our customers have derived real-time benefits from their IoT investments.

You can benefit from our IoT services, including consulting, development, and testing. Contact us today!

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Introduction

With companies still reeling from the aftermath of the pandemic, inflation, and global recession, cost reduction has become a priority. They must strike a balance between costs, digital transformation, and innovation. That’s why most of them choose to outsource product development. 59% of companies cite cost reduction as the main reason for outsourcing. They save costs on hiring, training, and other overhead expenses.

However, it’s not as cost-effective as it seems. Companies are often taken by surprise when they receive a bill that’s more than what they had expected. Most of these expenses are hidden charges. Hidden charges are expenses that are not very apparent. They could include consultation charges, documentation and knowledge transition, server management, and even creating an outsourcing contract.

Companies must be aware of these charges to avoid any unexpected surprises in the future. They must consider certain factors while finalizing the budgets.

What are these factors, and what should companies know while working with a partner? Let’s explore all this in detail.

What Are the Typical Costs Involved in Outsourcing?

Before finalizing the budget, companies must understand the typical or direct costs of outsourcing product development. These are the main components of the outsourcing budget.

1. Hiring Costs

Different projects need different types of hiring. The hiring costs vary accordingly. For example, small-scope projects might require a single developer from an offshore location. These developers charge around $30-$120 per hour. Large-scope projects that would continue for more than 12 months might require a team of full-time developers. They work as an extension of the in-house team and are hired specifically for this project. The company will have to pay for each of them. The costs may vary according to the location, expertise, and skill sets needed for the project. Companies may have to shell out additional money if they onboard a technical consultant or a CTO.

2. Project Scope

The scope and complexity of the project play a crucial role in determining the project costs. The cost of developing a single-page application would vary from that of a full-fledged application with multiple screens and functions. Similarly, building a complex application for different platforms might require more time, resources, and budget. The outsourcing partner could also charge more if they use their proprietary tools and systems for the project.

3. Stages and Types of Development

The budget varies depending on the stage and type of project. For example, building a new product from scratch will cost more than building a new feature for an existing application. Modernizing a legacy application could cost more too. The stage and scale of a project could influence the budget too. Sometimes the company might need help building a minimum viable product (MVP). After that, they might complete the project in-house or continue with the same outsourcing partner. The budget would vary based on that decision.

What Are the Different Pricing Models?

Pricing models help determine the budget for outsourcing based on the project’s duration, scope, and scale.

1. Fixed Pricing

Fixed pricing plan works well when the company has defined the project goals clearly and agreed upon the timelines, deliverables, and other details. This type of model is not flexible. So, any changes in the project scope could lead to budget readjustment or cost escalation. The outsourcing partners have to complete the project within the stipulated amount.

2. Time and Material (T&M)

In the time and material model, the founder pays the partner for the time they invest and the material (in this case, the resources and tools) they use to develop the product. T&M is a perfect choice for building products that evolve based on market and customer needs. The partner allocates resources with pre-determined skill sets and provides a time-based billing rate to the client. The only challenge is there’s no visibility on the overall costs. The company comes to know the final amount after the project ends. The chances of exceeding the budget are high.

3. Offshore Development

Offshore development can reduce outsourcing costs depending on the partner’s location. They have to follow the compliance and basic payout rates determined by the law of the land.

4. Dedicated Team

A dedicated team could cost high for the company. However, they are necessary for large-scale and long-term projects that would continue for several years.

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Introduction

In the age of cloud-native application development, microservices are a major talking point among software developers. Technology-driven companies like Amazon, Uber, and Netflix are adopting microservices over monolithic architecture for better business agility. Hence, it’s no surprise that the global market for microservices is predicted to reach $10.86 billion by the year 2027.

Lately, “microservices” is also becoming a must-adopt buzzword among product development startups. With their loosely coupled architecture, microservices are becoming popular among application developers in startups. After all, they are more compatible with delivering applications on the cloud platform.

Besides, as businesses evolve to become agile and adopt DevOps practices, they are naturally moving from the monolithic mode to microservices. To that end, let’s discuss why startups should consider adopting microservices for product development.

What are Microservices in Product Development?

Microservices are a set of smaller and loosely coupled services that communicate using APIs. This approach is in direct contrast to the monolithic architecture that is tightly coupled and comprises a single service. Monolithic applications are difficult to scale along with the increase in features and code base.

On the other hand, microservices-based applications use multiple independent components that run every application process as a service. Using lightweight APIs, these services can communicate through an API interface.

Here are some of the unique characteristics of microservices:

Modularity

Modularity is the core feature of microservices. This means that application can operate as a suite of smaller services, each of which can deploy independently and run its processes.

Autonomous

This means that application developers can create, deploy, operate, and scale each service in the microservices architecture without impacting other services. Effectively, services do not share their code or implementation with other services.

Specialization

This characteristic means that every service has its specialized capabilities. Hence, it can focus on solving a specific business problem. As services become more complex, they can be divided into smaller services.

How do microservices benefit product development? Let’s discuss that next.

Benefits of Microservices in Product Development

Camunda Research reports that 63% of enterprise-level companies use the microservices architecture in their product development. Here are some of the benefits of microservices in product development:

1. Agility

The microservices approach encourages development companies to create smaller independent teams that can take complete ownership of services. Smaller teams can understand the context of their work and work more independently. This leads to short development cycles and increased agility.

2. Compatible with Agile Environment

As compared to monolithic architecture, microservices are more compatible with the agile culture. For instance, this approach focuses on individual services, rapid iterations, continuous development & testing, and cost-effective development.

3. Service-Specific Development and Testing

Thanks to their service-oriented architecture, microservices enable organizations to create individual teams to work on specific services. These services cater to all elements, including product development, testing, maintenance, and deployment. Thus, every delivered service benefits from focused and dedicated attention.

4. CI/CD Support

Continuous development and integration (CI/CD) is core to the DevOps philosophy. Microservices supports CI/CD, where cross-functional teams can develop, test, and troubleshoot services independently. Thus, organizations can benefit from faster deployment and shorter turnaround times.

5. Improved Scalability

Scalability is necessary for growing businesses. With microservices, startups can deploy services to multiple servers, thus improving scalability and performance. Development teams can scale individual services independently and also add (or test) new components without any downtime or redeployment.

Next, let’s discuss how to implement microservices in product development.

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