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Leveraging Data: Know your data …

and it will give an end-to-end solution for new product development.

The industry has the data; where we fail is to leverage this data to turn it into actionable insights for better business gains. Photo Courtesy of | © alphaspirit

The industry has the data; where we fail is to leverage this data to turn it into actionable insights for better business gains.
Photo Courtesy of | © alphaspirit

Usha B. TrivediBy Usha B. Trivedi

“We talk a lot about data, but we really don’t know how to use it.”

How many times have you heard that before? Surveys in the Harvard Business Review and from Deloitte found that 80 percent of new products find a place on store shelves for less than 12 months, and about 96 percent of innovations fail to return their capital investments, respectively. Clearly, newly developed products and new entrants do not find the marketplace welcoming. The numbers back up the opening quote, and regardless of the massive amount of data available, companies fail to make the most of their data, perhaps because they just don’t know how to leverage it.

In order to make a new launch a success, managers for new product introduction teams need to know their data. Successful information managers can define, develop, introduce and manage the data available to them, but their jobs do not end here. It is critical that they also know how to leverage their data to improve their planning, research and development when introducing new products.

Where do MANUFACTURERS and Innovators Fail?

The current business ecosystem with enormous data can only be laser scanned using data analytics tools to have the right information available at the right times for these managers. The industry has the data; where we fail is to leverage this data to turn the data into actionable insights for better business gains. This data, once analyzed, gives insights that can disrupt the entire product development procedure and opens new avenues for innovators.

In today’s market scenario, new products are rolled out based on the marketing personnel’s work for pushing the product to customers. Meanwhile, related decisions are made on the basis of the consciences and instincts of managers. In a nutshell, nothing works based on insights obtained through data. We simply fail to leverage data enough at every stage of product development, from concept to final customers.

At a time when agile markets have new demands every single day, pushing newer products based on one person’s instincts and conscience aren’t going to yield concrete results. A product manufacturer might get good results for a short term, but in longer runs instincts-driven products will fail to generate the same output because such instincts fail to focus on customer data touch points and brand perspective.

On the other hand, a new product developed based on insights gained from data will generally show a dramatic change in its development since it will consider data at every stage. In fact, it will drive the entire new product development (NPD) team from the same perspective of optimizing efficiency, revenue or both.

Transforming Data into Insights

Typically, an NPD team consists of marketing professionals, engineers, data scientists and many more in-house resources of the organization. Their work throughout the new product development involves data analytics and developing business and engineering intelligence with predictive analysis – and to be proactive to market agility rather than being reactive.

Toward the later stages, these professionals use data available from market surveys, customer sentiment analysis and customer feedback to filter and analyze data in order to address the existing, latent or untouched needs demanded by the customer. Based on idea screening, relevant and appropriate value-adding data can be used while others can simply be dropped.

Now, since NPD teams are usually cross-functional groups, it will involve professionals from almost every division involved with the product. For instance, a sheet metal furniture company’s new product development team may include manufacturers, design engineers, marketers, pricing division representatives and several data scientists. The data available can thus set up benchmarks and develop new insights for each of these professionals.

Manufacturing or engineering design organizations may use other methods of forecasting to decide product growth, sales and maturity, as well as to predict behavior in the market. However, with data analytics, a multiplicity of variables can help forecast a much higher degree of certainty as compared to any other statistical method.

Improving Organization’s Profits

Together, data analytics can essentially allow firms to constitute and design NPD processes by optimizing marketing and sales, production and operations, pricing and distribution to make their products well received by customers.

Figure 1: The process of transforming data into insights.

Figure 1: The process of transforming data into insights.

For example, consider a manufacturer that, based on analysis results, develops and plans to sell a chair that is 30 percent more durable than its competitor, but the chair carries a higher price. The manufacturer develops models of the chair, runs the marketing on a pilot basis to reach out to customers and forecasts the demand. The manufacturer then gets in touch with a third-party manufacturer to make the same chair at lower cost to ship directly to customers. This way the chair, which otherwise would not have been a great success, can become a cash-maker to the original manufacturer.

Beta testing of products can generate greater profits and branding for manufacturers during the process of launching new products. Once beta testing is done, the actual production and manufacturing forecast for the newly launched product will include everything from finding optimal suppliers to proper manufacturing methods to achieve higher ROIs. But optimization models will yield a great deal of additional insights about setting targets for manufacturing, mass customization needs, determining ROIs for every single component and decision-making.

These same optimization models can also predict customer adoption to new product at local, regional or national levels to determine and set distribution targets, and hence decide the costs considering the profit margins. Further, big data management tools can optimize operational aspects of distribution from the packaging of final complete product to end-point delivery encompassing in-between delivery stops. Efficient data utilization thus collates every requirement for new product development in a structured format.

Usha B. Trivedi is an engineer with Hi-Tech CADD Services, a company that provides design support services for product drafting and modeling across the globe. With years of experience in CAD modeling and rendering, she writes to provide insightful solutions for the process challenges of fabricators, engineers, architects and contractors.

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