Essential Work Sample Exercises for Hiring AI Inventory Management Specialists

AI-driven inventory management optimization represents a critical intersection of data science and supply chain operations. Companies implementing these advanced systems can reduce carrying costs by 20-30%, decrease stockouts by up to 65%, and significantly improve cash flow. However, finding professionals who possess both the technical AI expertise and practical inventory management knowledge presents a significant challenge for hiring managers.

The complexity of modern supply chains demands specialists who can not only build sophisticated predictive models but also understand the business implications of their algorithms. A candidate might have impressive machine learning credentials but lack the domain knowledge to apply these skills effectively to inventory challenges. Conversely, supply chain experts may understand inventory dynamics but struggle with implementing cutting-edge AI solutions.

Work samples provide an invaluable window into how candidates approach real-world inventory optimization problems. Unlike theoretical interviews or generic technical assessments, these exercises reveal a candidate's ability to balance algorithmic sophistication with practical business constraints. They demonstrate whether a candidate can translate complex data patterns into actionable inventory strategies.

The following work samples are designed to evaluate candidates across multiple dimensions: technical proficiency with AI/ML tools, understanding of inventory management principles, data analysis capabilities, and communication skills. Each exercise simulates challenges that AI inventory specialists regularly face, from forecasting demand patterns to optimizing reorder points and explaining technical solutions to stakeholders.

By implementing these carefully crafted work samples, hiring managers can significantly improve their ability to identify candidates who will successfully drive inventory optimization initiatives. These exercises go beyond surface-level technical screening to reveal how candidates think, solve problems, and deliver business value through AI-powered inventory solutions.

Activity #1: Seasonal Demand Forecasting Model

This exercise evaluates a candidate's ability to analyze historical inventory data, identify seasonal patterns, and develop an AI-driven forecasting model. Accurate demand forecasting is fundamental to inventory optimization, requiring both technical modeling skills and business intuition about factors affecting demand. This activity reveals how candidates approach data preprocessing, feature engineering, algorithm selection, and model evaluation—all critical skills for AI inventory specialists.

Directions for the Company:

• Provide the candidate with 2-3 years of historical sales data for 5-10 products with clear seasonal patterns. Include columns for date, product ID, quantity sold, price, and any relevant external factors (promotions, holidays, etc.).
• The dataset should contain some noise and anomalies to test the candidate's data cleaning abilities.
• Allow candidates to use their preferred programming language and libraries (Python with pandas/scikit-learn/TensorFlow is common).
• Allocate 2-3 hours for this exercise, which can be conducted remotely.
• Provide access to a Jupyter notebook environment or allow candidates to use their own development setup.

Directions for the Candidate:

• Analyze the provided historical inventory data to identify patterns and seasonality.
• Develop a forecasting model that predicts demand for the next 3 months for each product.
• Explain your approach to feature selection, algorithm choice, and how you handled seasonality.
• Evaluate your model's performance using appropriate metrics (MAPE, RMSE, etc.).
• Prepare a brief explanation (5-10 minutes) of your methodology and findings, including visualizations of your predictions versus actual data.
• Be prepared to discuss how your model could be improved with additional data or different techniques.

Feedback Mechanism:

• After the candidate presents their solution, provide specific feedback on one aspect they handled well (e.g., "Your approach to handling outliers in the dataset was particularly effective").
• Offer one constructive suggestion for improvement (e.g., "Consider how incorporating external data like weather patterns might improve your seasonal predictions").
• Allow the candidate 10-15 minutes to explain how they would modify their approach based on this feedback, focusing specifically on the improvement area identified.

Activity #2: Inventory Parameter Optimization

This exercise tests a candidate's ability to optimize critical inventory parameters using AI techniques. It evaluates their understanding of inventory management fundamentals like safety stock, reorder points, and economic order quantities, while also assessing their skills in applying machine learning to optimize these parameters. This activity reveals how candidates balance competing objectives like minimizing costs while maintaining service levels.

Directions for the Company:

• Prepare a dataset containing inventory transactions for 15-20 SKUs across multiple locations, including lead times, holding costs, stockout costs, and service level requirements.
• Include information on supplier reliability, transportation variability, and demand volatility.
• Provide a simple simulation environment (or specifications for one) that allows testing of different inventory policies.
• Allocate 3-4 hours for this exercise, which can be conducted remotely.
• Make available any necessary documentation on current inventory policies and business constraints.

Directions for the Candidate:

• Develop an AI-driven approach to optimize inventory parameters (safety stock levels, reorder points, order quantities) across the provided SKUs.
• Your solution should minimize total inventory costs while maintaining the required service levels.
• Implement your approach using appropriate algorithms (reinforcement learning, genetic algorithms, or other optimization techniques).
• Test your solution using the provided simulation environment and quantify the improvements over current policies.
• Prepare a 10-minute presentation explaining your methodology, results, and the business impact of your optimization approach.
• Include visualizations comparing current versus optimized inventory levels and associated costs.

Feedback Mechanism:

• Provide positive feedback on the candidate's technical approach or business understanding (e.g., "Your method of accounting for lead time variability was particularly sophisticated").
• Offer one constructive suggestion for improvement (e.g., "Your model might benefit from more explicit handling of the cost tradeoffs between different service levels").
• Give the candidate 15 minutes to revise their approach based on this feedback, focusing specifically on how they would address the improvement area.

Activity #3: Anomaly Detection System Design

This exercise evaluates a candidate's ability to design an AI system that detects inventory anomalies and recommends corrective actions. It tests their understanding of inventory data structures, anomaly detection algorithms, and practical implementation considerations. This activity reveals how candidates approach system design, algorithm selection, and the creation of actionable business intelligence from technical outputs.

Directions for the Company:

• Provide a dataset containing inventory transactions with embedded anomalies (phantom inventory, shrinkage, data entry errors, unexpected demand spikes, etc.).
• Include documentation on the current inventory management system architecture.
• Specify business requirements for anomaly detection, including acceptable false positive rates and required detection sensitivity.
• Allow 2-3 hours for this exercise, which can be conducted remotely or on-site.
• Make available any necessary information about existing data pipelines and integration points.

Directions for the Candidate:

• Design an AI-powered anomaly detection system for inventory data that can identify potential issues before they impact operations.
• Your design should include:
• Data preprocessing requirements
• Algorithm selection and justification
• System architecture and integration with existing inventory systems
• Alert mechanisms and recommended actions for different anomaly types
• Performance metrics and monitoring approach
• Implement a proof-of-concept using the provided dataset that demonstrates your anomaly detection approach.
• Prepare a 15-minute presentation explaining your system design, including visualizations of detected anomalies and the business value of early detection.
• Be prepared to discuss scalability considerations and implementation challenges.

Feedback Mechanism:

• Provide positive feedback on an aspect of the candidate's system design (e.g., "Your approach to categorizing different types of anomalies and providing specific remediation steps was excellent").
• Offer constructive feedback on one aspect that could be improved (e.g., "Consider how you might reduce false positives for seasonal products").
• Allow the candidate 15 minutes to revise their design based on this feedback, focusing specifically on addressing the improvement area.

Activity #4: AI Implementation Planning

This exercise assesses a candidate's ability to plan the implementation of an AI-driven inventory optimization system. It evaluates their project management skills, understanding of change management, and ability to translate technical capabilities into business processes. This activity reveals how candidates approach complex implementations, stakeholder management, and the practical challenges of deploying AI in inventory systems.

Directions for the Company:

• Provide a case study of a fictional company looking to implement AI-driven inventory optimization, including:
• Current inventory management processes and systems
• Business objectives and KPIs
• Available data sources and quality issues
• Organizational structure and stakeholders
• Include any technical constraints or integration requirements.
• Allow 2-3 hours for this exercise, which can be conducted remotely.
• Make available any necessary information about typical implementation challenges in your industry.

Directions for the Candidate:

• Develop a comprehensive implementation plan for deploying an AI-driven inventory optimization system at the fictional company.
• Your plan should include:
• Project phases and timeline
• Required resources (technical, human, data)
• Risk assessment and mitigation strategies
• Change management approach
• Success metrics and evaluation methodology
• Pilot approach and scaling strategy
• Create a roadmap that balances quick wins with long-term strategic objectives.
• Prepare a 15-minute executive presentation outlining your implementation plan, highlighting key milestones, resource requirements, and expected business outcomes.
• Be prepared to discuss how you would adapt your plan if certain constraints or assumptions changed.

Feedback Mechanism:

• Provide positive feedback on an aspect of the candidate's implementation plan (e.g., "Your phased approach with clear success criteria for each milestone demonstrates strong project management thinking").
• Offer constructive feedback on one aspect that could be improved (e.g., "Your plan might benefit from more detailed consideration of data quality remediation steps").
• Allow the candidate 15 minutes to revise their implementation plan based on this feedback, focusing specifically on addressing the improvement area.

Frequently Asked Questions

How long should each work sample take to complete?

Each work sample is designed to take 2-4 hours of focused work. For remote exercises, we recommend giving candidates a 24-48 hour window to complete the task at their convenience. For on-site exercises, allocate 3-4 hours plus time for presentation and feedback. The goal is to get meaningful insight into the candidate's abilities without creating an unreasonable time burden.

Should we provide real company data for these exercises?

No, it's best to create synthetic datasets that mimic your actual inventory patterns but don't contain sensitive information. This protects your business data while still providing a realistic challenge. If you must use actual data, ensure it's properly anonymized and covered under an NDA.

How should we evaluate candidates who use different technical approaches?

Focus on the effectiveness of their solution rather than whether they used a specific algorithm or tool. The best candidates will justify their technical choices based on the problem requirements. Create an evaluation rubric that assesses problem-solving approach, technical implementation, business understanding, and communication skills rather than mandating specific technologies.

What if a candidate doesn't have access to the software tools we use?

Allow candidates to use tools they're familiar with for the work sample. The goal is to assess their thinking and problem-solving abilities, not their familiarity with your specific technology stack. For specialized tools, consider providing temporary access or focus the exercise on design and approach rather than implementation details.

How can we make these exercises fair for candidates with different backgrounds?

Provide clear instructions and necessary background information so candidates without specific domain knowledge can still demonstrate their problem-solving abilities. Evaluate candidates relative to their experience level—junior candidates should be assessed differently than senior ones. Consider offering multiple exercise options that allow candidates to showcase their strengths.

Should we compensate candidates for completing these work samples?

For extensive work samples (especially those taking more than 3-4 hours), offering compensation is appropriate and shows respect for candidates' time and expertise. This also typically results in higher quality submissions and a more positive candidate experience, particularly for senior roles where candidates are likely to have multiple opportunities.

AI-driven inventory management optimization represents a significant competitive advantage for organizations that implement it effectively. The right talent can transform your inventory operations, reducing costs while improving service levels. These work samples will help you identify candidates who not only understand the technical aspects of AI but can also apply these technologies to solve real business problems in inventory management.

By incorporating these exercises into your hiring process, you'll gain deeper insights into candidates' capabilities than traditional interviews alone can provide. Remember that the goal is not just to test technical skills but to understand how candidates approach complex inventory challenges and translate AI capabilities into business value.

For more resources to enhance your hiring process, check out Yardstick's AI Job Descriptions, AI Interview Question Generator, and AI Interview Guide Generator.

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