Harvard Case - Siena College: Course Scheduling for the Business Analytics Department
"Siena College: Course Scheduling for the Business Analytics Department" Harvard business case study is written by Shahryar Gheibi, Joseph McCollum, John O'Neill. It deals with the challenges in the field of Operations Management. The case study is 9 page(s) long and it was first published on : Dec 31, 2020
At Siena College, we recommend implementing a comprehensive course scheduling solution that leverages technology and analytics to optimize resource allocation, enhance student satisfaction, and maintain academic quality. This solution will address the challenges of increasing student demand, limited faculty resources, and the need for flexibility in a rapidly evolving field like Business Analytics.
2. Background
Siena College's Business Analytics Department is facing a surge in student enrollment, driven by the increasing demand for data-driven skills in the job market. This growth presents both opportunities and challenges. The department is struggling to accommodate the rising student numbers with limited faculty resources and a fixed classroom capacity. The current manual scheduling process is inefficient and prone to errors, leading to suboptimal utilization of resources and potential student dissatisfaction.
The case study highlights the department's efforts to improve its scheduling process, including the use of spreadsheets and a limited online scheduling tool. However, these solutions lack the sophistication and integration required to address the complex challenges of a growing department.
3. Analysis of the Case Study
To analyze the situation, we can apply the framework of Operations and Supply Chain Management. This framework helps us understand the key elements of the problem, including:
- Demand Forecasting: Accurately predicting student demand for specific courses is crucial for effective scheduling.
- Capacity Planning: Determining the optimal allocation of faculty, classrooms, and other resources to meet student demand.
- Scheduling Techniques: Implementing efficient and flexible scheduling algorithms to optimize resource utilization and minimize conflicts.
- Process Design: Streamlining the course scheduling process, from course proposal to final schedule creation, to improve efficiency and accuracy.
- Information Systems: Utilizing technology to automate and integrate scheduling data, enabling real-time updates and improved decision-making.
The analysis reveals several key issues:
- Lack of Data Integration: The current system relies on fragmented data sources, hindering comprehensive analysis and decision-making.
- Manual Processes: The manual scheduling process is time-consuming, prone to errors, and lacks flexibility to accommodate changes.
- Limited Optimization: The current approach lacks advanced algorithms to optimize resource allocation and student satisfaction.
- Lack of Real-Time Visibility: The department lacks a centralized system to track course availability, student enrollment, and faculty availability, hindering proactive scheduling adjustments.
4. Recommendations
To address these challenges, we recommend the following:
Implement a Comprehensive Course Scheduling Software: Invest in a robust, cloud-based scheduling solution that integrates with existing systems, such as student information systems (SIS) and faculty databases. This software should offer features such as:
- Automated Scheduling Algorithms: Leverage advanced algorithms to optimize course scheduling based on student demand, faculty availability, classroom capacity, and other relevant factors.
- Real-Time Data Visualization: Provide interactive dashboards for visualizing course availability, enrollment trends, and faculty workload, enabling data-driven decision-making.
- Conflict Management: Automatically identify and resolve scheduling conflicts, ensuring efficient resource allocation and student satisfaction.
- Mobile Access: Enable faculty and students to access and manage schedules from any device, enhancing flexibility and communication.
Develop a Data-Driven Scheduling Process: Establish a standardized process for collecting, analyzing, and utilizing scheduling data. This includes:
- Demand Forecasting: Utilize historical data and statistical models to forecast student demand for specific courses, enabling proactive capacity planning.
- Capacity Planning: Regularly assess faculty availability, classroom capacity, and other resources to optimize resource allocation and minimize overbooking.
- Continuous Improvement: Regularly review scheduling outcomes, identify areas for improvement, and refine the scheduling process based on data analysis.
Train Faculty and Staff: Provide comprehensive training on the new scheduling software and process to ensure effective adoption and utilization. This training should cover:
- Software Functionality: Familiarize faculty and staff with the features and functionalities of the new scheduling software.
- Data Analysis: Train faculty and staff on how to interpret and utilize scheduling data to make informed decisions.
- Process Management: Implement a standardized process for managing course proposals, scheduling changes, and communication with students.
Promote Student Engagement: Encourage student feedback on the scheduling process and utilize this feedback to improve the system and enhance student satisfaction. This can be achieved through:
- Online Surveys: Conduct regular online surveys to gather student feedback on course availability, scheduling flexibility, and overall satisfaction.
- Student Focus Groups: Organize focus groups to gather in-depth insights from students on their scheduling preferences and challenges.
- Student Representatives: Establish a student representative group to provide ongoing feedback and input on the scheduling process.
5. Basis of Recommendations
These recommendations are based on the following considerations:
- Core Competencies and Consistency with Mission: The proposed solution aligns with Siena College's mission to provide high-quality education and prepare students for successful careers. By optimizing resource allocation and enhancing student satisfaction, the solution supports the college's commitment to academic excellence.
- External Customers and Internal Clients: The solution directly benefits both students and faculty. Students benefit from increased course availability, flexibility, and a more efficient scheduling process. Faculty benefit from a streamlined scheduling process, improved communication, and better access to student data.
- Competitors: Implementing a sophisticated scheduling solution positions Siena College competitively by demonstrating its commitment to utilizing technology and data-driven approaches to enhance the educational experience.
- Attractiveness ' Quantitative Measures: While quantifying the return on investment (ROI) is challenging, the solution is expected to generate significant benefits, including:
- Increased Student Enrollment: By offering a wider range of courses and improving scheduling flexibility, the solution can attract more students, leading to increased revenue.
- Improved Faculty Productivity: By streamlining the scheduling process and providing access to real-time data, the solution can improve faculty productivity and reduce administrative burden.
- Enhanced Student Satisfaction: A more efficient and user-friendly scheduling process can enhance student satisfaction, leading to improved retention rates and positive word-of-mouth marketing.
6. Conclusion
The proposed course scheduling solution represents a significant investment in Siena College's future. By leveraging technology and data analytics, the solution can address the challenges of a growing Business Analytics department, optimize resource allocation, enhance student satisfaction, and maintain academic quality. This investment will position Siena College as a leader in utilizing data-driven approaches to enhance the educational experience and prepare students for the demands of the modern workforce.
7. Discussion
Alternative solutions, such as relying on manual scheduling processes or utilizing less sophisticated scheduling software, are less effective and carry significant risks, including:
- Inefficiency and Errors: Manual processes are time-consuming, prone to errors, and lack the flexibility to accommodate changes.
- Limited Optimization: Less sophisticated software may not offer advanced algorithms to optimize resource allocation and student satisfaction.
- Lack of Data Integration: Fragmentation of data can hinder comprehensive analysis and decision-making.
The key assumption underlying the recommendation is that Siena College is committed to investing in technology and data analytics to improve its operational efficiency and enhance the student experience. This assumption is supported by the college's stated goal of providing a high-quality education and preparing students for successful careers.
8. Next Steps
To implement the proposed solution, the following steps should be taken:
- Step 1: Selection and Procurement: Conduct a thorough evaluation of available scheduling software solutions and select a vendor that best meets the needs of the Business Analytics department.
- Step 2: Data Integration: Develop a plan to integrate the new scheduling software with existing systems, such as the student information system (SIS) and faculty databases.
- Step 3: Training and Implementation: Provide comprehensive training to faculty and staff on the new software and process, and implement the solution in a phased approach to minimize disruption.
- Step 4: Continuous Monitoring and Improvement: Regularly monitor the performance of the system, gather feedback from students and faculty, and make adjustments to optimize the scheduling process.
By taking these steps, Siena College can successfully implement a comprehensive course scheduling solution that will enhance the educational experience for students and faculty, optimize resource allocation, and position the Business Analytics department for continued growth and success.
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Case Description
Dr. Arthur Pierre was responsible for developing a course schedule for the Business Analytics Department at Siena College. He needed to determine which professor would teach a particular class section in a particular time slot for the Spring 2020 semester. Pierre barely survived the last round of course scheduling due to the compressed delivery time from five weeks to three weeks in the previous cycle. This was one of his most sensitive and challenging responsibilities as he had to consider student demand, administrative requirements and faculty preferences in the process of developing a schedule. The importance of the decision and the stressful problems that he had encountered in adjusting to the new timeline necessitated a more systematic approach to scheduling since the shortened timeframe would be implemented for the foreseeable future. His ad hoc approach was too time consuming because he had to incorporate multiple factors into his course schedule and that often required reworking the entire schedule each time he encountered a conflict. In collaboration with a colleague, they identified a potential solution. They needed to prioritize constraints and build an integer linear optimization model.
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