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Stein Mart's Downturn

936 Words4 Pages

After the shocking election results of 2016, many companies feared going into a downturn. Stein Mart has statistically shown to be one of the worst performing small-cap stocks since Election Day. Right before election, in October the newly appointed CEO, Dawn Robertson quit and the problems started to arise from there. What has happened to the company since?, How to reverse the downturn?, and track the factors affecting their demise, are our main questions for the project. Stein Mart was created back in 1902 by a Jewish Russian Immigrant. At the time, they were dealing in self-made clothes and sold locally in Greenville, Mississippi. From 3 stores in 1977, Jay Stein, the heir to the family business, expanded the chain to 40 stores across …show more content…

They are an organization with immense integrity towards their customers, associates and vendors. Stein Mart has principles of shared values that range across all of their branches. Steinmart’s another goal is to develop effective teams, and this is done by creating an effective work environment which is led by an affective Associate. Lastly, the most important value and vision for Stein Mart is to build and thrive by collaborating and building partnerships with other companies. They involve Associates, Customers, Vendors, Stakeholders and communities to add greater value to the company and increase demand for the stocks leading to greater …show more content…

This involved upgrading infrastructure to incorporate Enterprise Management Software (EMS) into a centralized location in Jacksonville, FL. This allowed the company to monitor information in real time and facilitate communication between its retail outlets countrywide. The database created in the process stores information about store performance and other parameters. While there is no information available in the open about what processes are being measured through the system (it's only accessible to selected individuals i.e. proprietary), an EMS system in general helps with energy conservation and operating costs. The data about operating costs can be used to build prescriptive models to optimize efficiency throughout stores and maximizing profits. Point-of-sale systems and mobile technologies can be used to increase efficiency in the system by tracking sales of items in inventory. Since shelf space is the largest costs for companies selling their products, inventory tracking and optimization can help remove items that don’t generate sales. This in turn can create additional space for other items that are profitable and allow for experimenting with new products. Another application for prescriptive analysis could be optimization of

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