Data Collection Plan
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.
Directions
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 and product quality will be measured. 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 additional staff to operate and maintain new equipment planned for the expansion. Create your job ad to include qualifications and desired skill sets.The student is to create the data collection plan, measurement criteria, and job qualifications list based on knowledge learned in the scenario, and post it in the discussion.
The data collection plan lists all the data items required for the subplans in the CONOPS. The key things you want in this type of plan are: the data items to be collected, what they measure operationally, frequency of collection, method of collection, and who collects them. Your plan should include high, medium, and detailed level data metrics to be collected. I’m only looking for 12-15 metrics total (4-5 from each of the three departments) that demonstrate you understand the different levels of metrics. You do not have to populate the metric with fake or made up data.
An example of a detailed metric is utilization of an individual machine, while utilization of all machines in a department is considered a medium level metric. A high level metric would be throughput of the department, such as the number of orders produced for the week. Here is a good template for a data collection plan taken from https://goleansixsigma.com/data-collection-plan/.
Data-Collection-Plan-v3.5_GoLeanSixSigma.com.xlsxDownload Data-Collection-Plan-v3.5_GoLeanSixSigma.com.xlsx.
The key item in the template is the Operational Definition of the data metric. This needs explain how the metric is to be measured. Avoid collecting raw data numbers such as the number of items because raw counts do not provide much information to make decisions. It is better to use percentages, ratios, averages, etc. when provide context for the data. Please let me know if you have questions on building a data collection plan.
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