Your team is hesitant about using OR modeling. How do you convince them of its validity?
To get your team on board with OR modeling, show them its real-world impact. Here's how to break down the barriers:
- Present case studies: Use success stories from similar businesses where OR modeling has significantly improved outcomes.
- Simplify the concept: Explain OR in layman's terms, focusing on how it solves complex problems through logical analysis.
- Provide training resources: Offer workshops or online courses to build confidence in using OR tools and techniques.
How have you approached introducing new methodologies to your team? Share your strategies.
Your team is hesitant about using OR modeling. How do you convince them of its validity?
To get your team on board with OR modeling, show them its real-world impact. Here's how to break down the barriers:
- Present case studies: Use success stories from similar businesses where OR modeling has significantly improved outcomes.
- Simplify the concept: Explain OR in layman's terms, focusing on how it solves complex problems through logical analysis.
- Provide training resources: Offer workshops or online courses to build confidence in using OR tools and techniques.
How have you approached introducing new methodologies to your team? Share your strategies.
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While OR can greatly reduce costs, it often requires custom models, making development time-intensive. However, with AI tools, building OR models has become easier and faster. Encouraging the team to first learn basic models and then use AI simplifies the process, making OR more effective for finding optimized solutions.
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Build a PoC using a small to medium size dataset and show how optimization brings in benefits in comparison to the existing state of art.
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Take up a use case and small clean data set, perform POC and demonstrate. Convince the management how this can be scaled up and benefits from it.
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The Analytics professional must also be a Story Teller and adapt its speech on the benefits of Analytics to different audiences. If a solution is supported by top management but is not well accepted by final users you might risk the whole project. If you have the buy-in from final users but top management is resistent you risk no to start the project. For each audience, the analytics professional (together with PMs and POs), must have a story to tell on why a solution is needed. People in companies, like in everywhere else, buy stories more than they buy pure numbers or charts.
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By numbers! Take a day or two to prepare a simple Python script. It doesn't need to be exhaustive—just a light demo without a UI. The goal is to show that we could achieve a certain percentage improvement (e.g., x%) over the current costs. Be sure to address the decision-makers, highlighting the benefits in terms of money, time, and resources.
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