Turning Big Data Into a Competitive Advantage in New Product Introduction

July 6, 2021

New product introduction (NPI) is an essential part of bringing a product from concept to its final form. The goals of NPI often include reducing waste, speeding up production, and saving money.However, achieving these goals is not always easy.

Only about 53% of manufacturers currently use big data analytics. The insight provided by data processing can lead to more agile NPI processes, which could create a giant competitive advantage for your business.

What Is a New Product Introduction (NPI)?

NPI is the process of taking an idea, developing it, and getting it to market. An NPI process is typically managed by a dedicated team that tracks progress and performs assessments to ensure that the project is on track.

A successful NPI process may include a dedicated project manager and representatives from each of the departments involved in the project. This may include members from the engineering, marketing, and financial departments.

Using an NPI process offers many advantages in manufacturing, including:

1.     Faster time to market

2.     Lower operating costs

3.     Higher quality of product

4.     Streamlined manufacturing

NPI allows you to rapidly move from one stage to the next. You can achieve a faster time to market by not having to stop and evaluate your progress for each step.

NPI also typically brings lower operating costs. The streamlined nature of the NPI process reduces waste and valuable resources. You avoid creating unnecessary prototypes and spend less time redesigning products.

With multiple experts from each department assessing the product, you are more likely to catch design flaws and issues that may typically go unnoticed. This results in higher quality products.

When you are ready to start production, you may find that the NPI process helps streamline your manufacturing. You are less likely to need to make changes after you start producing the product.

These benefits help you surpass the competition and deliver quality products at a faster rate. Only about 29% of manufacturers have adopted NPI processes across their organisations. However, the benefits also depend on your ability to successfully implement an NPI process. Leveraging the power of big data can help.

How to Use Big Data to Improve the NPI Process

Big data provides more insight for making better decisions, which can assist every area of the NPI process. The NPI process often includes the following steps:

You can use data to enhance each of these stages. Here is a closer look.

Develop a Plan

The first step in the NPI process involves developing a plan. You need to select members for the team, set deadlines, and define the roles of each member of the team.

Your initial planning should also include the identification of resources and the requirements for completing the product design. Big data can assist with this step by supplying decision-makers with more insight into the availability of resources and materials. You can focus the design around readily available or affordable materials.

Determine the Feasibility of the Product

NPI processes include a review of the feasibility of the product. This is an area where big data provides a significant advantage. You can use data analytics to assess the marketability of the product.

For example, you may learn more about the demand for similar products and the potential size of the market. These details help you determine whether moving forward with the project makes sense.

Develop the Product

If the product appears marketable, you may move forward with developing the product. This step often involves creating and evaluating prototypes. Big data helps you analyse the behaviour of actual users. You can determine what features they need and want most.

Along with using data to assist with evaluating the needs of your users, you can use artificial intelligence (AI) and machine learning (ML) for rapid prototyping. Rapid prototyping involves validating the design and function of the product during the initial phases. Using big data increases the efficiency of these steps by helping to spot weaknesses in the design.

Run a Pre-Production Test

A pre-production test is often used to evaluate the production process to ensure that it runs smoothly. Data analysis helps you monitor the production run by detecting defects and optimising the predictive maintenance schedule. These details result in a faster time to market and less waste.

Manufacture Your New Product

As with the pre-production test, big data and AI help you streamline your manufacturing processes, reduce waste, and detect defects. You enhance the benefits already provided by the NPI process by gaining access to more information.

Big data also provides ongoing benefits after you start production. You can continue to monitor the availability of resources and simplify your supply chain management.

Evaluate Your Success

After manufacturing the product, you need to evaluate the success of the NPI process. Big data can be used to evaluate production costs, timetables, and sales. You can use this information to tweak your NPI process to achieve even better results during your next product development project.


The New Product Introduction (NPI) process helps streamline the steps involved in developing a product and bringing it to market. Using big data allows you to optimise this process. You can use the insight provided by it to inform each stage of the NPI process.

Big data helps you determine the cost and availability of the resources needed for your project. You can identify opportunities that may otherwise be overlooked. You can also use big data for more insightful marketing research. By determining the needs of your customers, you become better equipped to develop products that solve your customer’s problems.

Using big data offers many advantages that could give you a competitive edge. However, you also need to properly implement the technologies needed to capture, sort, and analyse this valuable resource. If you want to enjoy a faster time to market, consider working with experts to adopt the latest machine learning and artificial intelligence solutions.

Feel free to also check out our other posts:

Creating a Digital Transformation Roadmap for Product Ramp-up in Manufacturing

Getting the Next Innovation Project Approved: Tips on How to Get C-Level Buy-in

Vertical Innovation in Manufacturing: Bringing Stakeholders Together for Common Gain