Reference no: EM132145951
Based on forecasts of potential contracts, the CEO of Kibby and Strand is considering an option to lease the building next door, but he has concerns there may be some slack in current production capacity that could be utilized, negating the need for the addition space. There are also some newer technology cutting and sewing machines available with higher capacity the company could purchase, but they are expensive. So here are the CEOs options:
Do nothing to increase production, but the downside is lost contracts.
Lease the building next door, then expand production by purchasing the same technology cutting and sewing machines as the company has now.
Try to squeeze more production out of the current production department setup. This may require overtime pay, and would definitely increase the maintenance costs on the current machines.
Replace all the machines in production with newer higher capacity machines and remain in the current production space.
How do you decide which option to select without reliable and valid data on the current production department? You can’t, and that is the CEO’s dilemma.
This scenario presents a realistic picture of how outcomes data can serve as a catalyst for change within an organization. While the focus of this case is on consumer satisfaction data, most firms have ready access to a dearth of outcomes data that can be used to investigate causal factors, establish priorities, weight options (alternatives), support decisions, and provide an internal benchmark from which to compare future results. Making operational and supply chain management decisions without having benefit of information coming from sound statistical analyses, is tantamount to playing darts blindfolded and betting your life savings on hitting a bull’s eye on the first toss. Industries are being increasingly more reliant on data to support the decision-making process. Data analytics and informatics permit leaders to leverage big data, perhaps in ways it hasn’t been previously used, to make informed decisions that can positively impact clinical outcomes, financial and operational performance, and the strategic positioning of the firm.
Unit Learning Outcomes
Perform descriptive analyses on datasets using Microsoft Excel. (CLO 4, 5, and 7)
Properly determine standard times for units of work using Microsoft Excel. (CLO 3, 4, and 5)
Calculate cycle time commonly associated with time studies for production using Microsoft Excel. (CLO 1, 3, 4, 5, and 7)
Use quantitative data as the basis for making suggested operational improvements within various organizational structures. (CLO 3, 4, and 5)
Directions
Initial Posting
Go to McGraw-Hill Practice Operations to view the scenario.
Click the "McGraw-Hill Connect" tab in the course navigation menu.
Click the McGraw-Hill Practice Operations link.
Students are to complete Module 4, Human Resources and Capacity (Scenario) in Practice Operations. Based on their observations in this scenario, and upon a careful review of the available literature, the student is to consider him - or herself to be the Production Manager of Kibby and Strand, the company in the scenario.
The CEO is thinking of expanding Kibby and Strand, and you are tasked to create a data collection plan and measurement criteria for how production output will be calculated. Create your collection plan and output measurement criteria assuming the current production capacity in the simulation scenario will be doubled.
In addition, the HR manager asks for a list of qualifications and skill sets required for the production department to be used in a HR job advertisement to hire employees for the expansion. The memo you get states, “emphasis on skills in IT and technology are important as the CEO wants to maximize production using automation”. Create your job ad to include qualifications and desired skill sets, and explain how automation might be utilized in the production department. Also create a staffing plan that would maximize production in the production department.
The student is to create the data collection plan, measurement criteria, job qualifications list, and staffing plan based on knowledge learned in the scenario, and post it in the discussion.