I would place Tyson Foods in the forecasting model because at the local level we use standard reporting and ad hoc reporting to determine past trends and we analyze statistical data to forecast future outcomes. Data or metrics alone do not help in making effective decisions, so analytics is very important as it requires building and collecting data to help in understanding the outcomes and forecasting future needs (Jac Fitz-Enz, 2010, pg. 186).
At Tyson, we collect data from powerBI, workday, SAP and other HRIS programs that the organization uses. PowerBI provides turnover data and breaks it down by department and supervisor. This view helps
identify where the problem areas are allowing us to make adjustments as needed. For example, there used to be very high turnover when hand deboning took place. Hand deboning required the use of knives and scissors which created many issues with repetitive movement illnesses such as carpal tunnel and trigger fingers. Once we determined what department had the greatest turnover, we conducted employee surveys to find out what the problem was. The response was unanimous that the work was too repetitive and hard on their hands. Tyson Foods invested more than 1.3 million to automate the process (Times, 2021). Once the process was automated, illnesses were reduced significantly and turnover was improved in that particular area.
Inadequate or incomplete data can negatively impact the effectiveness of HR analytics (Jac Fitz-Enz, 2010, pg. 187). Though Tyson Foods has been using Workday as an HRIS system where data is stored, the process has changed. HR data used to be the responsibility of an HR manager. The responsibility has now shifted to the production supervisor. Therefore, daily inputs are entered by the supervisor such as transferring employees from one department to another, terminating them, placing them or returning them from leave of absence. The issue here is that supervisors are putting wrong information into the HRIS system. Employees are not being transferred to the correct department and/or sometimes not transferred at all which is an HR problem because HR thinks the department is staffed based on the numbers that are projected from the HRIS system to later find out that the supervisor never completed the task. This is a big barrier as HR cannot make informed decisions with missing data. To mitigate this issue HR and operations have to look at their numbers regularly and communication between both needs to take place every time data is looked at to insure accuracy. Another barrier that we are facing is change management issues as supervisors were not used to entering this data and many are resistant to
change as they do not have the computer knowledge to complete these basic tasks. Therefore, continuous training needs to be provided to supervisors.
The support that is needed from business leaders is that they need to encourage their supervisors to complete their tasks daily and correctly and emphasize to them that the only way to impact change is through having adequate data. Leaders need to allow their supervisors to spend more time out of the floor to learn these new tasks and learn about the importance that each task has and how they affect the business. Organizations always need a return on their investments. If there are no returns, there is no need to invest. Therefore, the business case to get to the next level is to demonstrate to top management how getting the company to the predictive modeling stage can save the company money. We can show what other companies are using to make informed decisions and we need to demonstrate that we are behind the 8 ball when it comes to recruiting the right folks, to getting our folks trained. We can demonstrate how frustrated the supervisors are because they are not fully trained in the new workday process. We can show that the lack of knowledge is taking more time away from the production floor which can affect production efficiencies. It is imperative to get to the next level because
if companies are not on top of what is coming, they will stay behind. AI is growing at a very fast pace. AI
