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  • last updated : 17 August, 2021

R&D Performance Improvement Guide

Category: Articles
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R&D business function is one of the most significant and pivotal departments in an organization as it allows organizations to keep up with ever-changing technological innovations. Imagine a company without an R&D function.
The R&D operates in unexplored technology domains that are complex, difficult to understand, and sensitive, making it even more difficult to measure performance parameters. However, an organization needs to measure such KPIs and ensure the sustainable development of the Innovation Powerhouse.
This brings us to how R&D performance can be measured and what can be done to increase its efficiency. Certain factors can be taken into account while creating an R&D performance improvement guide. Here’s a comprehensive list of factors that can be tailored as per the need of the organization: 

R&D roadmap ahead

R&D roadmap is a crucial phase that can impact the R&D performance. This includes more than one qualitative variable like the quality of the research, scope of the end-product, general user acceptance, and others. These variables can be parameterized to set quantifiable goals. Other business variables like budgets, resources, technological shifts, and others are some of the other factors that are to be considered before creating a comprehensive R&D roadmap. 


Idea generation, brainstorming, idea nurturing plays a pivotal role in improving R&D performances. This is one of the most important formative stages where a significant amount of research goes into generating new ideas, understanding the technology around it, and assessing viable market opportunities around it. Structuring the ideation process through smart AI-based assistance can help in shaping the ideas more effectively. One of the things that are to be considered during the ideation phase is the possibility of the end-product and its utility. n have a long-lasting impact on R&D performance.

So what can be the possible action items that can assist at this step? 

  1. Making sure the research carried out is relevant and in sync with the market.
  2. Gathering as much prior data around the subject matter using the Hybrid (Automated and Manual) approach. Making sure that the important databases are covered to reduce the chances of duplicity.

Setting standards, processes, and protocols

Defining a process helps in improving the results in the long run. Standardization of process increases the chances to identify weak links early in the innovation cycle. This saves time, money, and resources, and improves the quality of the output. 

Efficient Data Management

Data plays an important role in R&D function, whether it is data creations, referring or management, Proper channelization, and management of this data is the key to improve R&D performance. With the volume of data R&D functions operates in,  the chances of missing out on important information or details are not surprising. Hence, companies need to set up comprehensive database management systems in place. 

Managing changes and Reiterations 

During the R&D lifecycle an idea or a concept goes through, some multiple reiterations or changes are incorporated before finalizing the end draft. It is very important to consider these changes as important concept points, even if they are not a part of the final draft. It is also important to communicate the incorporated changes to all the relevant stakeholders within or outside the organization to avoid any conflict of interest. 

Incorporating New-age tools and software 

The Digital era is impacting R&D high time. It is redefining conventional practices. With the ongoing technological pace, R&D teams must consider new-age technologies tailored to their needs. It is important to understand that though conventional approaches were yielding results, they may get obsolete and inefficient within few years.
There are tools and SaaS-based platforms which can assist in data gathering, data management, and cross-function collaborations. Emerging AI-based tools provides data precision which aids in refining existing workflows and streamlining new methodologies. XLSCOUT, one such emerging Artificial Intelligence (AI) and Machine Learning (ML) equipped tool offers multiple solutions for data aggregation, idea generation, and IP & R&D collaboration. 


R&D productivity is very subjective and largely varies with the operational domain of an organization. However, some standard practices can be incorporated to increase the productivity of R&D business functions. 

XLSCOUT supports AI-backed solutions that can streamline certain processes and augment conventional tasks within R&D. For more information on how XLSCOUT can help in strategizing decision making through its advanced algorithms, reach out to us at contact@xlscout.ai