Organizational change is a necessary process for any business that seeks improvement and innovation. In this regard, The Home Depot’s managers and associates are constantly involved in projects that would transform certain operations or the company structure to provide better customer service. For instance, one of the recent major innovations included the implementation of machine learning to solve the so-called ‘shelfout’ problem. Shelfout is the situation when the product is in the store but is not located on the appropriate shelf (Remidez & Beldona, 2021). It often occurs due to the customers who sometimes place one type of product in front of another, thus covering the latter, or leave the merchandise on the ‘inappropriate’ shelf. However, sometimes it also happens due to space constraints or employee mistakes. As for the former, for instance, when the store receives a product from suppliers, it may be placed on a different overhead shelf because another package already occupies the necessary space. As a result, it leads to the issue when the package is in stock but lost inside the store.
To address this problem, The Home Depot’s Applied Machine Intelligence (AMI) group proposed an initiative that suggests creating an algorithm that would help to calculate which types of products are more prone to shelfout. Before starting the project, the working group did an experiment that proved that shelfout is directly and significantly associated with sales and revenue (Remidez & Beldona, 2021). Next, the members of the merchandize team had to scan all the products that were not in the appropriate place for two weeks. Then, the AMI team developed an algorithm that used Google Cloud as the data storage platform and integrated the new solution with the existing Smart List application that the merchandise group already used. Last but not least, the AMI team started testing their product and applying similar technology to other stores. As a result, now the store and merchandize managers can effectively address the shelfout issue.
As for the store that I work at, I noticed that the innovation discussed above was first not accepted quite well by the employees in the merchandise team. However, that period was not very long, and soon the workers understood that the new technology could help them significantly improve one’s work. In my opinion, the problem was that the innovation was implemented at a fast pace, so the workers did not receive a sufficient explanation of what exactly was going to change and why it was necessary. For this reason, the merchandize staff thought that their work would become more complicated. That indeed happened during the testing and learning period, but due to the effective work of the AMI team and training, those issues were resolved fast. Therefore, although there was no substantial resistance to change by staff, the managers could put more effort into communications with the employees, explaining the reasons and consequences of change.
If The Home Depot operated outside of North America as a global company, I think their implementation strategy would need to consider local culture. In regards to the implementation problem in the store that I work at, the same situation would seemingly not be an issue in some traditional societies such as those in the Middle East or Asia. For instance, previous research indicates that in China, authoritarian leadership prevails, meaning that employees do not expect their managers to explain the necessity of certain changes (Sposato, 2019). On the other hand, in countries with an individualistic mentality, such as the U.S. or European Union, the workers usually want to be included in the decision-making process.
Therefore, the major point that the current discussion intends to emphasize is the necessity of the companies to communicate the reasons for the change and its impact on workers. Although, in the case of The Home Depot, that problem was not long-lasting, it may be different for other organizations. As such, while managers make decisions, the frontline employees usually implement them in practice. For this reason, staff resistance may lead to failed attempts to innovate the organization.
References
Remidez, H., & Beldona, S. (2021). Developing and Implementing Machine Learning Software at Home Depot. Journal of Cases on Information Technology, 23(4), 1-10.
Sposato, M. (2019). Understanding paternalistic leadership: How to work with Chinese leaders. Development and Learning in Organizations, 33(6), 19-21.