A successful brand not only focuses on its employees and manufacturing process, but also pays attention to the development of the product. Product development cycle is classified into two types, pre-launch and post launch cycle.
In this blog we will study the role of artificial intelligence and machine learning in the product development cycle.
(Speaking of Product Development Cycle, check out - Phases of Project Life Cycle)
When a product is launched in the market, the R&D sector of the brand keeps the research on looking for further improvement. It is the collection of all stages that are involved in the journey of product from concept or idea to market launch and further.
It is hereby clear that product development is part of the entire product’s journey.
Though, for different products and different brands the stages of product development are quite different depending on its needs and requirements. But the common strategies involved in the process are as follows:
Products are launched to fulfill customer needs and market requirements. To keep your product updated and improved, it is essential for the brands to identify market needs. This can be achieved by maintaining healthy conversations with potential customers, surveys and other user research activities.
Organizations need to find how much and how many people are willing to pay for their solutions.
The designing team needs to put efforts and apply their creativity to devise how their product might serve the different needs of the customers.
After setting and conceptualizing the product. It is essential to validate the products. An early test is always the best method to check if the particular product is worthy to pursue or not. If not, the owner can reject the idea at base level avoiding heavy expenses.
With a legitimate product concept in mind, the team should build out a product roadmap. This roadmap identifies the themes and goals that are needed to solve and develop the most significant points related to the product.
MVP stands for a minimum viable product; it is the initial version of the product. It just contains enough functionalities to be used by customers.
Do the practical test of your product by launching MVP beforehand in the market. These experiments can gauge interest, prioritize marketing channels and test the sensitivity and packaging. The release of MVP can bring ideas, complaints, and suggestions into the prioritization process and populate the product backlog.
Continuous iteration based on user feedback and strategic goals is an essential strategy. Feedbacks given by the customers are essential for enhancements, expansions, and changes related to product.
Mentioned above are the strategies involved in the improvement process. Though product improvement is a never-ending process, over time only the product roadmap will evolve from time to time setting up new goals and observations.
(Take a look at - Customer Journey Analytics)
With the advent of AI and ML algorithms, the product designing, and product improvement centers of the enterprises have been facilitated. The businesses are employing several facets of AI and ML to develop and launch new products as well as to improve the existing ones.
Unlike traditional data gathering technologies, AI and ML can provide best possible options to the businesses. AI at the same time helps in gaining competitive advantages and leads to faster product development.
In enterprises, startups and firm businesses, AI helps in product development in following ways:
Ensure company’s peers have a successful product integration.
The product development teams can utilize AI and ML algorithms in the development cycle.
Help in achieving greater economies of scale, efficiency and speed gains across the entire process of product development.
Assist the three core areas of product development, i.e., digital prototyping, PLM and product profile management.
Enable the organizations to find their optimal configuration out of the array of possible combinations.
Give the organizations insights over their new product development on a large scale.
Assist with neural networks integration.
Perform a deep analysis of price elasticity and sensitivity.
Provide deep analysis to reduce energy costs and negative price variances of the product.
Help in cost management, strategic sourcing and procurement for the new product development process.
Provide a variety of customization options, thus, help in creating tune propensity models.
Empower next generation thinking and frameworks design.
Develop systems to reduce time to showcase while improving product quality.
Enhance product adaptability and catering to the needs of the customer.
Assist generative designs.
Help in identifying product design constraints.
Overall assistance in developing an optimized product.
(Learn how - chatbots are used in marketing)
Benefits of AI in Product Development
Now let us study the benefits and advantages of AI and machine learning in product development. The main list includes the following benefits:
Generative design involves a program that generates different outputs to meet the specified criteria of the organizations. To generate a generative design designers and engineers input design goals and parameters.
These include requirements such as materials, manufacturing methods, cost constraints and alternatives designs. Later the software employs ML algorithms to learn from each iteration and understand the concept. The AI and ML then generate a variety of options. It provides an augmentation to human work.
AI helps in product development by optimizing assembly lines. Let us consider an example, if any equipment operator shows signs of fatigue, supervisor and technicians get notifications. Thus, the system automatically activates contingency plans and other reorganization activities.
AI helps in building a virtual copy of a physical production system. This includes both a machinery asset or entire machinery system. These digital copies help in real-time diagnose, evaluation of the process, prediction, tic, and visualization of product performance.
Data science engineers utilize AI and supervised ML algorithms to assist these digital copies. The entire process takes place by processing historical history and unlabeled data gathered from real-time monitoring. In short words, virtual models help in optimizing production scheduling, quality improvement and maintenance.
Forecast and prediction is the most basic advantage of AI. AI technologies help in identifying potential downtime and accidents by analyzing real-time data.
AI helps the technicians and supervisors to forecast a failure before it actually occurs. Thus, AI can improve the development efficiency by reducing the cost of machine failure.
ML and AI algorithms promote planning activities. AI tools provide more accurate results than any traditional method. The better inventory control helps in reducing critical scenarios like cash-in-stock and out-of-stock.
Inventory management is sometimes referred to as stock management. It is the process of ordering, purchasing, selling and maintaining of company's inventory.
Quality assurance is the end process set up to assure the quality of products and services. It is basically the test to check the standards of the product delivered by the company.
In earlier times, quality assurance has been managed by highly skilled professionals and has been a manual job since ages. Now, AI and image processing algorithms can technically validate if the item is developed and produced in the right way or not. This real-time and automatic check has eased the process of product development.
Decision making is defined as the process of choosing the right combination among different existing ones. AI and ML algorithms help in taking the immediate decision related to any product than traditional methods.
Process optimization is basically done to set up sustainability of a product in the market. AI software helps the organizations to optimize the production and development process at every level to achieve sustainable production. It helps in identifying the bottlenecks in the operations and eliminating the same.
Also Read | Role of AI in workplace safety
There is no doubt that AI is the future of enterprises and companies. It has immense possibilities and combinations that allow the organizations to imbibe and leverage its innumerable advantages. But, AI and its algorithms will work on input requirements only, AI has no brain of its own and works according to the data fed in the system.
The companies should always maintain an upgraded product development cycle. Also, this cycle should be set up to the standards and requirements of the common public.
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