ELE 3797 Consulting Tools and Techniques
ELE 3797 Consulting Tools and Techniques
Students will learn how to use the fundamental tools and techniques employed by consultants in financial management, business analysis, and management consulting. Artificial intelligence (AI), exemplified through tools like ChatGPT, is already an integral part of the consultant's toolkit and will play a significant role in this course. You will learn to use AI effectively and critically.
The course has a practical focus, and students will learn to solve problems for businesses across various industries. Through webinars, case studies, and guest lectures, students will gain valuable insights into working as a consultant.
You will use frameworks such as MECE (Mutually Exclusive, Collectively Exhaustive), SCQA (Situation, Complication, Question, Answer), problem definition, decision trees, and logic trees, as well as storytelling techniques in presentations.
Numbers without stories are just numbers, and stories without numbers are merely stories. By combining the two, your ability to convey a message can be enhanced. Therefore, storytelling techniques can be a vital complement to effectively delivering a message.
If you aspire to work in a consulting firm or wish to learn effective problem-solving techniques, this course will provide you with a comprehensive understanding of how this is done and will give you an advantage in nearly any situation where you face a complex problem.
By the end of the course, students will:
- Have a foundational understanding of what it means to work as a management consultant.
- Understand and be able to explain the utility and limitations of using AI models and their relevance to modern consulting work.
- Have basic insights into classic consulting tools such as the MECE principle, decision trees, and hypothesis-driven problem-solving.
- Know what characterizes effective consultant presentations of solutions or findings through the preparation of presentations and reports for a client.
- Be knowledgeable enough to identify ethical issues related to the use of AI in business analysis and decision support.
Upon completion of the course, students should be able to:
- Use the most common tools that consultants employ to structure problems and find relevant solutions for clients (MECE, SCQA, logical trees, decision trees).
- Formulate effective prompts for AI tools like ChatGPT to generate insightful analyses and proposals for solving business problems or challenges.
- Combine AI tools with traditional consulting techniques to structure and solve complex issues.
- Critically assess the quality and relevance of AI-generated solutions and apply professional judgment to adapt these to the business context.
- Practice professional and ethical standards in using AI, with an awareness of how technology may influence consultants’ recommendations and business decision-making processes.
- Communicate and argue recommendations clearly, structured, and logically through a consultant report, where storytelling can serve as a crucial tool.
By the end of the course, students should be able to:
- Assume a consulting role through independent or group work to address a business problem.
- Integrate AI into consulting work as part of a strategic approach to problem-solving and decision support.
- Critically evaluate the ethics, quality, and relevance of AI-generated solutions and use professional judgment to adapt these to the business context.
- Convey recommendations through a consultant report.
- Definition and structuring of problems.
- Analysis using various tools and techniques, including but not limited to MECE, SCQA, logic trees, decision trees, optimization, and simulation methods.
- Use of AI models to solve problems.
- Data gathering and data comprehension.
- Interpretation and communication of results.
- Storytelling and presentation techniques.
The course will be conducted 100% online and will primarily consist of asynchronous learning activities. The various learning activities are designed to support students' understanding and application of the course content. The course is structured to provide students with a flexible learning experience.
Learning activities will be conducted at the student's own pace within the set deadlines. Active participation in all activities is recommended to maximize learning outcomes. The course combines individual learning with opportunities for collaboration and presentations, allowing students to develop skills and knowledge in an effective and motivating manner.
Higher Education Entrance Qualification
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
There are no special requirements for prior knowledge.
| Assessments |
|---|
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Grouping: Group/Individual Duration: 72 Hour(s) Exam code: ELE 37971 Grading scale: Pass/fail Resit: Examination when next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Webinar | 6 Hour(s) | |
Student's own work with learning resources | 100 Hour(s) | |
Feedback activities and counselling | 44 Hour(s) | |
Examination | 50 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.
