Competency 1: Evaluate data-driven processes and approaches of an organization’s operations.
This reflection activity is comprised of two sections, collectively totaling a minimum of 500 words. Complete your reflections by responding to all prompts.
Operations Management
Explain what work in operations management looks like and what key operational decisions a firm needs to make to match supply with demand. Determine how to improve process efficiency by off-loading a bottleneck or how to balance a process by relocating work from one step to another.
Evaluate the Lean Philosophy
Choose 3 of the following questions to discuss:
What is the Lean concept and why is it important to study?
How can Lean be applied to manufacturing and service processes?
Will Lean work in service environments? Why or why not?
Discuss ways to use Lean to improve 1 of the following: a pizza restaurant, a hospital, or an auto dealership.
Why is Lean hard to implement in practice?
Explain the relationship between quality and productivity under the Lean philosophy.
Sample Solution
Data-driven processes and approaches involve the use of data in order to make decisions, prioritize tasks, and implement strategies. These processes allow organizations to identify opportunities for improvement and optimize their operations. Evaluating these processes involves examining how well they are able to utilize data in order to improve efficiency and effectiveness. This can be done by analyzing key performance indicators (KPIs) such as customer satisfaction or operational costs, tracking changes over time, comparing results against industry benchmarks, or running simulations to explore different scenarios. Additionally, it is important that an organization’s data-driven processes adhere to industry best practices regarding privacy and security protocols. Finally, organizations should ensure that all employees understand their roles in the implementation of the process so they can work together towards shared goals.
Sample Solution
Data-driven processes and approaches involve the use of data in order to make decisions, prioritize tasks, and implement strategies. These processes allow organizations to identify opportunities for improvement and optimize their operations. Evaluating these processes involves examining how well they are able to utilize data in order to improve efficiency and effectiveness. This can be done by analyzing key performance indicators (KPIs) such as customer satisfaction or operational costs, tracking changes over time, comparing results against industry benchmarks, or running simulations to explore different scenarios. Additionally, it is important that an organization’s data-driven processes adhere to industry best practices regarding privacy and security protocols. Finally, organizations should ensure that all employees understand their roles in the implementation of the process so they can work together towards shared goals.
Genetic and environmental factors are both contributor of the predisposition of cancer (10). Knowing the nature of these contributers is important to prevent the diseases ( adapting lifetstyle and behavior to the conditions). Sometimes genetic factors and cancer that are associated with each other affect significantly clinical intervention. For instance, as mentioned before if mutations occur at breast cancer susceptibility gene 1 and 2 (BRCA1,BRCA2) and at the same time if mutations occur at tumor suppressor genes , there is higher risk to develop the breast, ovarian, hematologic and prostate cancers(11). For these reason, regular screening, surgical measures and receive adjuvant therapies would undergo to prevent. Also genetic tests are used to analyse the inherited mutations DNA mismatch repair genes. Risk of advencing of colon cancer is high at the MLH1 and MSH2 genes(12). Under the light of this information cancer can be precluded with early screening colonoscopy to early detect and treat for cancer. Cancer databases that are about mutation types and polymorphisms are updated for public. These resources can be used to identify new biomarkers for screening.(13)
Tumor classification and subtyping
Personalized medicine changes the traditional classification of cancers from histologic scale to the molecular scale. Although histological scale does’nt give more information about prognosis , personalized tretment alternatives and risk of recurrence, molecular scale offers to give a detailed information about diseases processes(14). DNA, RNA, miRNA and protein have been used for molecy-ular analyses to classfy different tumor types into the subtypes. Each of them have an unique prognostic outcome that can not be identified with the traditional morphologic ways(15). Generally molecular scale for classification is used for acute myeloid leukemia, glioblastoma, breast cancer , and renal cell carcinoma , and to differentiate between Burkitt’s lymphoma and diffuse B-cell lymphoma. This classification that offers prognosis and treatment options can help to the patients about management of disease.(16)
