Essential Work Samples for Evaluating AI in Supply Chain Disruption Prediction Skills

Supply chain disruptions cost businesses billions annually, with the average company losing 45% of one year's profits over a decade due to supply chain disruptions. As global supply chains become increasingly complex, organizations are turning to artificial intelligence to predict and mitigate potential disruptions before they occur. Professionals skilled in AI-driven supply chain disruption prediction are therefore in high demand, commanding premium salaries and playing critical roles in organizational resilience.

However, identifying candidates with the right combination of AI technical expertise and supply chain domain knowledge presents a significant challenge. Traditional interviews often fail to reveal a candidate's true capabilities in applying machine learning to real-world supply chain problems. Theoretical knowledge doesn't necessarily translate to practical application skills, and the multidisciplinary nature of this field requires assessment across multiple dimensions.

Work samples and technical skill evaluations provide a window into how candidates approach complex supply chain disruption prediction challenges. They reveal not just technical proficiency with AI algorithms, but also domain understanding, data interpretation skills, and the ability to translate predictions into actionable business recommendations. These practical exercises help hiring managers distinguish between candidates who merely understand AI concepts and those who can effectively deploy them to strengthen supply chain resilience.

The following four activities are designed to comprehensively evaluate a candidate's capabilities in AI-driven supply chain disruption prediction. They assess technical skills, strategic thinking, communication abilities, and practical problem-solving – all essential components for success in this specialized field. By implementing these exercises, organizations can make more informed hiring decisions and identify candidates who will truly drive value through AI-powered supply chain risk management.

Activity #1: Supply Chain Disruption Data Analysis and Model Selection

This activity evaluates a candidate's ability to analyze supply chain data, identify relevant features for disruption prediction, and select appropriate AI/ML models. It tests technical AI knowledge, data preprocessing skills, and understanding of which algorithms are most suitable for supply chain disruption scenarios. This foundational skill is critical as the effectiveness of any AI-driven prediction system depends on selecting the right model for the specific supply chain context.

Directions for the Company:

• Prepare a sanitized dataset containing historical supply chain data with features such as supplier performance metrics, lead times, geopolitical risk indicators, weather events, and historical disruptions.
• Include some data quality issues (missing values, outliers) to test the candidate's data preprocessing skills.
• Provide access to a Jupyter notebook environment or similar platform where candidates can analyze the data.
• Allow 60-90 minutes for this exercise.
• Have a technical team member available to answer clarifying questions about the data.

Directions for the Candidate:

• Analyze the provided supply chain dataset to identify patterns and potential predictors of disruptions.
• Perform necessary data preprocessing steps to prepare the data for modeling.
• Recommend 2-3 specific machine learning models that would be appropriate for predicting supply chain disruptions based on this data.
• Justify your model selections, explaining why they're suitable for this particular supply chain context.
• Outline how you would evaluate the performance of these models, with special attention to the business impact of false negatives vs. false positives.
• Present your findings in a brief (5-minute) explanation to the interview panel.

Feedback Mechanism:

• After the candidate's presentation, provide one piece of positive feedback about their approach or analysis.
• Offer one specific suggestion for improvement, such as considering additional features, alternative models, or different evaluation metrics.
• Give the candidate 10 minutes to revise their approach based on this feedback and explain how they would incorporate the suggestion.
• Assess not only their technical response but also their receptiveness to feedback and ability to adapt their thinking.

Activity #2: Supply Chain Disruption Prediction System Design

This activity assesses the candidate's ability to design a comprehensive AI system for supply chain disruption prediction, including data sources, model architecture, and implementation strategy. It evaluates strategic thinking, system design skills, and understanding of how AI fits into broader supply chain management processes. This planning exercise reveals how candidates approach complex, multifaceted projects that require both technical and business considerations.

Directions for the Company:

• Create a fictional but realistic company profile with details about its supply chain structure, key vulnerabilities, and business objectives.
• Include information about existing systems, data availability, and stakeholder concerns.
• Provide whiteboarding tools or large paper for the candidate to sketch their system design.
• Allow 45-60 minutes for preparation and 15 minutes for presentation.
• Ensure the interview panel includes both technical and supply chain operations stakeholders.

Directions for the Candidate:

• Design a comprehensive AI-driven supply chain disruption prediction system for the described company.
• Your design should include:
• Required data sources (internal and external)
• Data integration approach
• Model selection and architecture
• Implementation timeline and phases
• Key performance indicators to measure success
• Integration with existing supply chain management systems
• Approach for continuous model improvement
• Create a visual representation of your system architecture.
• Prepare to present and defend your design to the interview panel in 15 minutes.
• Be prepared to discuss trade-offs in your design decisions and how they align with the company's specific needs.

Feedback Mechanism:

• After the presentation, highlight one particularly strong aspect of the candidate's system design.
• Provide one constructive suggestion about an aspect of the design that could be improved or a consideration that was overlooked.
• Give the candidate 10 minutes to revise one component of their design based on this feedback.
• Evaluate their ability to incorporate feedback while maintaining the integrity of their overall system design.

Activity #3: Supply Chain Disruption Response Role Play

This role play evaluates the candidate's ability to communicate AI-driven disruption predictions to stakeholders and collaborate on response strategies. It tests communication skills, business acumen, and the ability to translate technical insights into actionable recommendations. This skill is essential as the value of AI predictions is only realized when they drive effective decision-making across the organization.

Directions for the Company:

• Prepare a scenario involving a predicted supply chain disruption (e.g., a major supplier facing production issues, geopolitical event affecting shipping routes, or extreme weather threatening distribution centers).
• Create a brief that includes the AI model's prediction, confidence level, potential business impact, and available mitigation options.
• Assign team members to play roles such as:
• Supply Chain Director (concerned about operational continuity)
• CFO (concerned about financial impact)
• Sales Director (concerned about customer commitments)
• Provide the scenario to the candidate 30 minutes before the role play.
• The role play should last 20-25 minutes.

Directions for the Candidate:

• Review the provided scenario of a predicted supply chain disruption.
• Prepare to lead a meeting with key stakeholders to discuss the AI system's prediction and recommend response strategies.
• During the role play:
• Explain the AI model's prediction in business terms, including confidence levels and limitations
• Present 2-3 potential response strategies with pros and cons of each
• Address questions and concerns from different stakeholders
• Build consensus on a recommended course of action
• Outline next steps and monitoring approach
• Be prepared to handle skepticism about the AI prediction and balance technical details with business implications.

Feedback Mechanism:

• After the role play, provide positive feedback on one aspect of the candidate's communication or stakeholder management.
• Offer one specific suggestion for improving how they presented the AI insights or handled stakeholder concerns.
• Give the candidate 5 minutes to demonstrate how they would adjust their approach based on this feedback by redoing a specific portion of the role play.
• Assess their ability to incorporate feedback while maintaining confidence and clarity in their communication.

Activity #4: Model Evaluation and Improvement Exercise

This exercise tests the candidate's ability to evaluate the performance of an existing AI model for supply chain disruption prediction and recommend improvements. It assesses technical depth, critical thinking, and problem-solving skills. This capability is crucial as AI systems require ongoing evaluation and refinement to maintain and improve their predictive accuracy in dynamic supply chain environments.

Directions for the Company:

• Prepare documentation for an existing supply chain disruption prediction model, including:
• Model architecture and features used
• Performance metrics (precision, recall, F1 score, etc.)
• Examples of successful predictions and misses
• Current limitations and challenges
• Include some performance issues that need addressing (e.g., high false positive rate for certain suppliers, inability to predict specific types of disruptions).
• Provide visualization of model performance over time.
• Allow 60 minutes for the candidate to review materials and prepare recommendations.

Directions for the Candidate:

• Review the documentation for the existing supply chain disruption prediction model.
• Identify strengths and weaknesses in the current model's performance.
• Develop specific recommendations for improving the model, which may include:
• Feature engineering suggestions
• Model architecture modifications
• Additional data sources to incorporate
• Changes to preprocessing or training methodology
• Ensemble approaches or alternative algorithms
• Prioritize your recommendations based on potential impact and implementation difficulty.
• Prepare a 10-minute presentation explaining your analysis and recommendations.
• Be prepared to justify your suggestions with technical reasoning and expected business outcomes.

Feedback Mechanism:

• After the presentation, highlight one particularly insightful recommendation or analysis point.
• Provide one constructive suggestion about an aspect of their approach that could be enhanced or a consideration they may have missed.
• Give the candidate 10 minutes to refine one of their recommendations based on this feedback.
• Evaluate their technical depth, analytical thinking, and ability to incorporate feedback into their technical approach.

Frequently Asked Questions

How much technical AI knowledge should we expect candidates to demonstrate in these exercises?

Candidates should demonstrate understanding of machine learning fundamentals, appropriate model selection for time-series and classification problems, and evaluation metrics relevant to supply chain disruption prediction. However, the focus should be on practical application rather than theoretical depth. Look for candidates who can explain their technical choices in business terms and understand the trade-offs involved.

Should we provide real company data for these exercises?

No, use synthetic or heavily anonymized data that resembles your actual supply chain data but doesn't contain sensitive information. The data should be realistic enough to test relevant skills but shouldn't expose proprietary information. Consider creating a simplified version of your supply chain network with fictional suppliers and locations.

How should we evaluate candidates with strong AI backgrounds but limited supply chain experience?

Focus on their ability to ask insightful questions about the supply chain context, their learning agility, and how they apply their AI expertise to this new domain. During the role play and system design exercises, assess whether they consider supply chain-specific factors even if they don't have deep domain knowledge. Strong candidates will recognize the limits of their knowledge and seek to fill those gaps.

What if we don't have technical AI experts on our interview panel?

If possible, include at least one person with AI/ML expertise, even if they need to be brought in from another department or as an external consultant. Alternatively, prepare a structured evaluation rubric with specific technical criteria developed with input from AI experts. Focus assessment on the candidate's ability to explain complex concepts clearly and their problem-solving approach rather than technical minutiae.

How can we adapt these exercises for remote interviews?

All four activities can be conducted remotely with minor modifications. Provide data and documentation through secure file sharing. Use collaborative online tools like Google Colab for the data analysis exercise and virtual whiteboarding tools for the system design activity. For the role play, use video conferencing with clear role introductions. Ensure candidates have stable internet connections and appropriate software access before the interview.

Should we expect candidates to write actual code during these exercises?

For the data analysis exercise, basic code writing is appropriate to demonstrate data manipulation and model selection skills. However, the focus should be on approach and reasoning rather than coding proficiency. For other exercises, pseudocode or high-level algorithm descriptions are sufficient. The goal is to assess problem-solving and AI application skills rather than programming ability, unless the role specifically requires production-level coding.

Supply chain disruptions will continue to challenge organizations in our increasingly complex global economy. Hiring professionals with the right combination of AI expertise and supply chain understanding is critical to building resilience. The work samples outlined above provide a comprehensive framework for evaluating candidates' abilities to apply artificial intelligence to the specific challenges of supply chain disruption prediction.

By implementing these exercises, organizations can identify candidates who not only understand AI concepts but can effectively apply them to create business value through improved supply chain visibility and risk management. The most successful candidates will demonstrate technical proficiency, strategic thinking, clear communication, and the ability to bridge the gap between data science and supply chain operations.

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