While innovation has become a pervasive term, many of today's organizations still find innovation elusive. One reason may be that much of what is being said about innovation contributes to misunderstanding. To truly manifest innovation and reap its benefits, one must recognize that innovation is three different things: innovation is an outcome, innovation is a process, and innovation is a mindset. Innovation as an outcome emphasizes what output is sought, including product innovation, process innovation, marketing innovation, business model innovation, supply chain innovation, and organizational innovation. Innovation as a process attends to the way in which innovation should be organized so that outcomes can come to fruition; this includes an overall innovation process and a new product development process. Innovation as a mindset addresses the internalization of innovation by individual members of the organization where innovation is instilled and ingrained along with the creation of a supportive organizational culture that allows innovation to flourish. Such an understanding defines necessary elements, considerations, and vernacular surrounding the term so that better decisions can be made, thereby enabling innovation and having a greater propensity to succeed.

An important consideration in solving the problems of new product forecasting entails distinguishing new product forecasting from the process of forecasting existing products. Particular differences between the two can be identified across the dimensions of data, analytics, forecast, plan, and measurement. For example, new product forecasting features little to no data with which to begin the process, whereas data are available and accessible in forecasting existing products. The minimal data situation requires a qualitative approach that lays out assumptions to provide transparency; in contrast, quantitative techniques are predominantly used when forecasting existing products. Different assumptions help construct a range of new product forecast outcomes on which company contingencies can be planned versus a singular point forecast for an existing product. And the measure of forecast accuracy, which is a common metric in forecasting existing products, must give way to meaningfulness so that the new product forecast is actionable. Recognizing new product forecasting as a cross-functional, company-wide process helps resolve the problems of new product forecasting. While incapable of remedying all problems, a properly understood and organized new product forecasting effort can help the company better prepare, execute, and support a new product launch, affording a greater propensity to achieve new product success.

New product development (NPD) practitioners are keen to benchmark NPD practices because identifying any practice that is able to more efficiently and/or effectively deliver a new product could represent the difference between success and failure. A common purpose is therefore to identify NPD best practices with the expectation that companies will manifest and sustain these to augment their NPD efforts. To help in identifying such practices, we present a framework developed from prior benchmarking studies, a Delphi methodology with leading experts, and a survey involving over 300 NPD practitioners. The uniqueness of the framework lies in its ability to distinguish NPD practice across seven dimensions: Strategy, Research, Commercialization, Process, Project Climate, Company Culture, and Metrics/Performance Measurement. The framework is also unique in that across each dimension, poor NPD practices are listed as a starting point from which to improve, alongside best practices to which companies should aspire. To further assist in continuous improvement, an audit tool is derived from the framework, suggesting investigative questions that practitioners can ask to evaluate their company's NPD efforts. We conclude with general observations about NPD practice as the continued search for NPD best practice endures.