It is quite interesting to study all the different approaches, perspectives, and ideas that are being brought to the ERP organizational change table from many different fields. This includes work from researchers, practitioners, and ERP vendors. The ERP arena is looking at many success factors including emerging technologies, software design, organizational culture and leadership, project management approaches, and many many more factors in an effort to bring value to the ultimate goal: the success of your organization.
This ERP work is driven by the desire to solve a problem. It’s really all about supply and demand. After all, what person or organization in their right mind would spend so many resources on a problem that doesn’t exist? Businesses, practitioners, researchers, ERP vendors, and universities do not spend millions of dollars, countless hours, and significant effort if there is not a recognized problem to be solved. Objective research illustrates how it will make improvements in all aspects of the ERP success triad (people/culture, process, and technology) in order to solve the challenges, mitigate the risks, and reduce and prevent failure.
Please allow me to give you just an introduction and primer on one such idea:
In an article titled “ERP success prediction: An artificial neural network approach” from Rouhani and Ravasan (2012), they discuss an artificial neural network approach to ERP success prediction. Their work is really quite interesting and unique. Without getting too technical, they develop what is called an expert system based on the Artificial Neural Network (ANN) method. Artificial neural networks are a mechanism that allows machine learning and is based on how human beings learn. That is, artificial neural networks are modeled after the human brain. But up until 2012, this ERP success prediction research and study was limited (really nonexistent). Changa, Hsu, Wang and Wu published in 2012 “Measuring the success possibility of implementing ERP by utilizing the Incomplete Linguistic Preference Relations”. Sawah, Tharwat, and Rasmy also did work about this same time (2013) in a paper called “A quantitative model to predict the Egyptian ERP implementation success index” in which they presented an ERP success predication model utilizing organizational culture factors.
The goal of the work of Rouhani and Ravasan was to describe, discuss, present, and utilize organizational factors and ERP success relationships. That is, their developed ANN system would accept specific organizational data input and then determine the likelihood of ERP organizational change success. Now, how cool is that!?
Rouhani and Ravasan note that a primary objective and potential contribution to the ERP organizational change field is that it would allow “ERP project managers who wish to know the ERP readiness situation and prepare readiness plans based on their organizational profiles.”
We know (via objective research) that organizational cultural elements and factors greatly impact ERP organizational change performance. And, we know that organizational temperature can be measured (more to come in future posts on this topic and in particular the “Dension Model”). How cool would it be to take an accurate assessment of the organizational culture and then use that data to input into an accurate ERP success predicator? Given the high cost of time, money, and effort that challenged and failed ERP organizational change can place on businesses, this would be a significant tool.
What this work suggests is that 1) an organization can accurately assess the “temperature” of the culture and other research identified success factors) and 2) we can input that data into a prediction model and 3) then accurately determine the success of your ERP Organizational Change effort. Pretty cool stuff.
Where are we today in 2020 on this idea, eight years after the 2012 work of Rouhani and Ravasan? Is it real? Is it accurate? Will artificial intelligence play a part in ERP organizational change success?