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Last updated on 2025年1月13日

Your Operations Research analysis faces unexpected variables. How do you adjust your decision-making process?

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In operations research, you're often faced with complex decision-making scenarios that hinge on predicting and managing various variables. However, even the most meticulously crafted models can encounter unexpected variables that throw a wrench into the works. When this happens, it's crucial to have strategies in place to adjust your decision-making process without losing sight of your ultimate goals. This article will guide you through adapting your operations research analysis in the face of unforeseen changes, ensuring that you stay on track towards making informed and effective decisions.

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Sreenath S

Data Science | Optimization | Machine Learning | IIT Bombay
Camilo Augusto García Guevara

Inteligencia de procesos | BPMN | Mejora organizacional | Analítica prescriptiva

1 Embrace Flexibility

When unexpected variables arise, flexibility becomes a key asset. You must be willing to reassess your models and strategies. This could mean revisiting the assumptions that underpin your analysis or incorporating new data that reflects the changed circumstances. It's important to remain open to adjusting your methods and even your objectives if necessary. By being adaptable, you can pivot your approach to accommodate new information, which is often essential in maintaining the relevance and accuracy of your operations research analysis.

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Sreenath S

Data Science | Optimization | Machine Learning | IIT Bombay
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Whenever one starts building a model, it is important to list down the assumptions and possibilities of further extension. This helps in imparting flexibility to the model in future. However if unexpected variables come in, that might alter the entire problem statement itself. In that case it is better to build the models from scratch instead of making patches in the existing model to avoid risks.

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Camilo Augusto García Guevara

Inteligencia de procesos | BPMN | Mejora organizacional | Analítica prescriptiva
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The modeling process begins with exploration, not just to solve problems but also to enhance understanding from a constructivist perspective: Identifying variables is key. Models and data evolve together, balanced by the modeler and decision-maker who value usability, utility, and speed for robust models within reasonable timeframes. In a recent reverse supply chain project, we started with few preconceptions. Initial data led to a simple, "classic" model and we incrementally developed further versions with different assumptions. Some iterations required compromising our expectations for a good, compliant response, highlighting future needs for information and control to refine the main models.

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2 Update Models

As new variables emerge, updating your predictive models is essential. This involves integrating the new information into your existing frameworks to ensure they reflect the current situation. You might need to employ different statistical methods or optimization techniques to account for the new data. It's a process that requires both analytical skills and creativity, as you may need to develop novel solutions to incorporate these variables effectively.

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Priyanka Ravichandran

Data Analyst | Master of Analytics, UC Berkeley graduate | Looking for Full-Time Opportunities | Machine Learning | Supply Chain Analytics | Generative AI Enthusiast
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Updating models is definitely an effective method to catch the model up, but it should be a part of a bigger approach. Consider employing broader methods like ensemble modelling, Bayesian models, scenario simulation and probabilistic forecasting.

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3 Risk Assessment

Conducting a thorough risk assessment in light of the new variables is crucial. You need to evaluate how these changes impact the potential outcomes of your decision-making process. This may involve qualitative analysis or quantitative methods such as sensitivity analysis, which examines how different values of an independent variable affect a particular dependent variable under a given set of assumptions. Understanding the risks associated with unexpected variables allows you to mitigate potential negative effects on your operations.

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4 Engage Stakeholders

Communicating with stakeholders is vital when your analysis encounters unexpected variables. You need to inform them about the changes and how they might affect the decision-making process. This is not just about transparency; it's also about collaboration. Stakeholders may offer insights or resources that can help you navigate the new landscape. Their engagement can also facilitate smoother implementation of any necessary changes to your analysis or the subsequent decisions.

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5 Decision Reevaluation

When faced with new variables, it's time to reevaluate your decisions. This doesn't mean starting from scratch but rather reviewing and adjusting your choices in light of the updated analysis. You'll need to consider the implications of the new variables on your objectives and constraints. Sometimes, this might lead to a change in strategy or even a redefinition of the problem you're trying to solve. The key is to ensure that your decisions remain aligned with your goals and the new information at hand.

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Priyanka Ravichandran

Data Analyst | Master of Analytics, UC Berkeley graduate | Looking for Full-Time Opportunities | Machine Learning | Supply Chain Analytics | Generative AI Enthusiast
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Decision reevaluation is heavily analogous to sensitivity analysis. We don't rebuild the entire model from scratch for sensitivity analysis, instead we change the slack/shadow price to assess how the final solution changes to these tweaks. Much similar to this analysis we adjust our decisions in response to new variables to stay aligned with end goals.

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6 Continuous Learning

Lastly, treat unexpected variables as learning opportunities. Each challenge you encounter in operations research can provide valuable insights that improve your analytical skills and decision-making framework. Document these experiences and the adjustments you made so that you can refer to them in the future. Continuous learning helps you build resilience against unforeseen changes and enhances your ability to adapt your operations research analysis effectively.

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Your Operations Research analysis faces unexpected variables. How do you adjust your decision-making process?

1

2

3

4

5

6

1 Embrace Flexibility

2 Update Models

3 Risk Assessment

4 Engage Stakeholders

5 Decision Reevaluation

6 Continuous Learning

Operations Research

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Your Operations Research analysis faces unexpected variables. How do you adjust your decision-making process?

1

2

3

4

5

6

1 Embrace Flexibility

2 Update Models

3 Risk Assessment

4 Engage Stakeholders

5 Decision Reevaluation

6 Continuous Learning

Operations Research

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