Many organizations today face situations where a particular adjustable aspect of their pipeline is set arbitrarily and more often than not, inefficiently. For example, how often restocking takes place, or the time of interaction with potential clients through social media. Although these “parameters” of the organization’s function don’t seem to matter much, they often influence the end result in subtle and usually non-obvious ways, leaving those in charge of these matters unconcerned (and sometimes baffled).
Problems Arising from This Situation
The main problem arising from all this is that the organization spends more resources for processes that could be fine-tuned to be more efficient. Also, the more savvy employees may lose faith in the organization’s organizational abilities, since they may become aware of the inefficiencies. Moreover, the management may be burdened with additional work in order to tackle these inefficiencies, in order to ensure the pipeline’s smooth functionality.
Implications of These Problems
As these problems remain not addressed, they are bound to have noticeable implication to the organization, resulting to additional costs and loss of potential revenue. For example, if restocking were to be done at a time ensuring that no item would be out of stock (or that no item would be overstocked), this would result to additional sales. In addition, if social media promotion was done at the days / times of maximal impact, it would bring about more engagement from potential customers, and therefore more revenue in the long term. However, by leaving these problems unresolved, they are bound to limit the profit, while potentially also damaging the dynamics of the organization’s ecosystem.
Optimization as a Potential Solution
Although not everything can be resolved in a systematic fashion, many of these inefficiencies can be addressed with optimization. This is basically at the heart of every AI process, since modern optimization methods are part of the AI field. As a result, optimization is efficient by design and geared towards solving practical problems that are often too complex to handle otherwise. What’s more, no specialized software is required, since most programming languages have libraries for this purpose, while some optimization algorithms are not too difficult to implement from scratch. Finally, optimization can be applied in a variety of problems, making it a versatile solution to a whole spectrum of problems an organization faces.
Data Science Partnership (DSP) is atop methodologies like this one, as well as many other aspects of AI. So, DSP can help you fill that gap of optimization expertise in your organization, by providing you with the right human resources, contract-based or in-house, able to undertake the implementation of such a system. Feel free to contact us at email@example.com to learn more about how DSP can help your organization take advantage of this promising AI methodology.