Interview Questions for Demand Planning Systems (DPS)

Statistical forecasting relies on historical data and mathematical models to predict future demand. Think of it like analyzing a basketball player’s past performance to predict their future points. We identify patterns and trends in past sales data to extrapolate into the future. Common statistical methods include time series analysis (ARIMA, Exponential Smoothing), regression analysis, and machine learning algorithms. These methods are objective and data-driven.

Qualitative forecasting, conversely, relies on expert opinion, market research, and subjective judgment. Imagine a panel of marketing experts predicting the demand for a new, innovative product. This approach is crucial when historical data is scarce or unreliable, such as with new product launches or disruptive market changes. Methods include the Delphi method (gathering expert opinions iteratively), market surveys, and sales force composite (aggregating sales team forecasts).

The key difference lies in the data source: statistical methods use quantitative historical data, while qualitative methods utilize expert judgment and qualitative information.

Several KPIs measure forecast accuracy. The most common are:

• Mean Absolute Deviation (MAD): The average absolute difference between the forecasted and actual demand. A lower MAD indicates higher accuracy.
• Mean Absolute Percentage Error (MAPE): The average absolute percentage difference between the forecasted and actual demand. It’s useful for comparing accuracy across different products or periods with varying demand levels. A lower MAPE is better.
• Root Mean Squared Error (RMSE): The square root of the average squared difference between forecasted and actual demand. RMSE penalizes larger errors more heavily than MAD.
• Bias: Measures the systematic over- or under-estimation of demand. A consistent bias indicates a problem with the forecasting method or data.

Choosing the right KPI depends on the specific context and business priorities. For example, in a high-margin product environment, RMSE might be preferred because it heavily penalizes larger forecasting errors which directly impact profitability. For lower-margin products, MAPE might be more suitable, as the emphasis is more on percentage accuracy.

I have extensive experience with various demand planning software solutions. I’ve worked extensively with SAP APO (Advanced Planner and Optimizer), a robust system ideal for large enterprises with complex supply chains. My expertise extends to using its features for demand planning, including statistical forecasting, collaborative planning, and simulation. I’m also proficient in JDA Demand, known for its user-friendly interface and strong analytical capabilities. It facilitates streamlined collaboration across departments and supports various forecasting methodologies.

More recently, I’ve been working with cloud-based solutions like Anaplan, which offers flexibility and scalability. Its model-based approach provides transparency and allows for scenario planning, crucial in rapidly changing market conditions. I’ve successfully implemented Anaplan for several clients, migrating them from legacy systems like SAP APO and significantly improving forecast accuracy and collaboration. My experience encompasses the entire lifecycle: from requirements gathering and system configuration to training users and monitoring performance.

Outliers and unexpected events present significant challenges. My approach involves a multi-step process:

1. Identification: Using statistical methods like box plots or standard deviation analysis to identify outliers. Understanding the root cause of the outlier is critical. Was it a data entry error, a one-time promotion, or a true market shift?
2. Investigation: Thoroughly investigate the cause of the outlier. This might involve reviewing sales data, marketing campaigns, and external factors (e.g., competitor actions, economic changes).
3. Adjustment: Depending on the cause, the outlier might be adjusted, removed, or incorporated into the forecast through a more robust model. For instance, promotional effects could be modeled separately.
4. Mitigation: Implement measures to reduce the impact of future unexpected events. This may involve incorporating external data sources, improving data quality, or using more robust forecasting models that are less sensitive to outliers (e.g., robust regression).

For example, a sudden surge in demand due to a viral social media trend can be initially treated as an outlier. However, understanding the underlying cause allows incorporating social media sentiment data into future forecasts.

Several biases can skew demand forecasts. Common ones include:

• Anchoring Bias: Over-relying on initial forecasts or previous periods’ data, neglecting new information.
• Confirmation Bias: Seeking out information that confirms pre-existing beliefs, ignoring contradictory evidence.
• Availability Bias: Overemphasizing readily available information, potentially neglecting less accessible but relevant data.
• Planning Bias: Consciously or unconsciously adjusting forecasts to meet predetermined targets.

Mitigation strategies involve:

• Using diverse data sources: Combining historical sales data with market research, customer feedback, and expert opinions reduces reliance on any single source.
• Blind testing: Evaluating forecasts without knowing the source to minimize bias in interpretation.
• Establishing clear processes: Implementing structured forecasting procedures and utilizing collaborative reviews reduces individual bias.
• Regularly review and update models: Models should be regularly assessed and adjusted to account for changes in the market and new data.

For example, using a Delphi method, where experts anonymously provide forecasts and then iteratively refine their estimates based on group feedback, helps mitigate anchoring and confirmation biases.

Collaborative Planning, Forecasting, and Replenishment (CPFR) is a business practice focused on improving supply chain efficiency through enhanced collaboration between trading partners. Imagine a retailer and its supplier working closely together to anticipate and fulfill consumer demand. CPFR involves the systematic sharing of information and joint planning across the supply chain. Key components include:

• Strategy and Planning: Jointly defining business objectives, sharing market insights, and aligning forecasts.
• Demand and Supply Management: Sharing sales forecasts, inventory levels, and production plans.
• Execution: Coordinating orders, shipments, and inventory replenishment.
• Analysis: Regularly reviewing performance, identifying areas for improvement, and adjusting plans.

The benefits of CPFR include reduced inventory levels, improved forecast accuracy, increased customer satisfaction, and stronger relationships between trading partners. Successfully implementing CPFR requires strong communication, trust, and technology to facilitate information sharing.

Promotional activities and seasonality significantly impact demand. Incorporating these factors requires a multi-faceted approach:

• Historical Data Analysis: Analyzing past sales data to identify the impact of past promotional campaigns and seasonal trends. This could involve regression analysis to quantify the impact of specific promotional variables (e.g., discounts, advertising spend).
• Promotional Planning: Working closely with the marketing team to understand planned promotional activities, including timing, duration, and anticipated impact. This information is crucial for adjusting baseline forecasts.
• Seasonality Modeling: Using time series models that explicitly account for seasonal patterns. This might involve using seasonal indices or decomposing the time series into trend, seasonal, and residual components.
• External Data Integration: Integrating external data sources such as economic indicators, weather data, and social media sentiment can provide valuable insights into potential seasonal variations.

For example, if a retailer plans a Black Friday sale, the demand forecast for that period needs to incorporate the expected boost in sales due to the promotion. Similarly, a seasonal increase in ice cream sales during summer needs to be accounted for in the forecast.

Time series analysis is a core component of demand planning, focusing on historical data patterns to predict future demand. It involves identifying trends, seasonality, and cyclical patterns within data points ordered over time. My experience spans various techniques, including decomposition methods (breaking down a time series into its components), moving averages (smoothing out fluctuations to reveal underlying trends), and exponential smoothing (giving more weight to recent data).

For instance, in my previous role at Acme Corp, I used time series analysis to predict seasonal peaks in demand for our flagship product, leading to optimized inventory levels and reduced stockouts during the holiday season. We utilized a combination of moving averages and exponential smoothing, adjusting the parameters based on the product’s historical volatility.

I’m proficient in several forecasting models, each suited for different data characteristics. ARIMA (Autoregressive Integrated Moving Average) models are powerful for stationary time series (data with constant statistical properties), capturing complex relationships between past and present values. Exponential Smoothing methods, including simple, double, and triple exponential smoothing, are simpler to implement and interpret, particularly effective for data with trends and seasonality. I’ve also worked with more advanced models like Prophet (developed by Facebook) which handles seasonality and trend changes exceptionally well.

In a past project, we compared ARIMA and Exponential Smoothing models for forecasting monthly sales. ARIMA offered slightly higher accuracy for longer forecasting horizons, but Exponential Smoothing was easier to explain to stakeholders and quicker to implement for more frequent forecasting cycles. The choice of model depends on the specific data and business needs.

Reconciling forecasts from various sources – sales, marketing, promotions, economic indicators – is crucial for building a holistic and accurate demand plan. This typically involves a collaborative process. I use a weighted average approach, assigning weights to each forecast source based on its historical accuracy and reliability. Qualitative factors, like upcoming marketing campaigns or competitor actions, are also incorporated through expert judgment.

Think of it like a jury deliberation. Each ‘juror’ (forecast source) presents its case, and a final decision (the reconciled forecast) is reached by considering the credibility of each source’s prediction. The process involves meetings, discussions, and potentially statistical tools to quantify the reliability of different sources.

Inventory management is inextricably linked to demand planning. Accurate demand forecasts are essential for determining optimal inventory levels. Too much inventory ties up capital and increases storage costs, while insufficient inventory leads to stockouts, lost sales, and unhappy customers. Demand planning informs inventory decisions by providing the predicted demand that inventory needs to meet.

For example, understanding the lead time for procurement and the variability in demand helps to set safety stock levels. If we anticipate a surge in demand due to a promotional campaign, demand planning will inform inventory managers to increase stock accordingly to avoid running out of the product during the campaign.

Forecast errors are inevitable; the goal is to minimize them and continuously improve accuracy. I employ several strategies: regularly monitoring forecast accuracy metrics (e.g., Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE)), identifying sources of error (e.g., unexpected events, model limitations), and adjusting the forecasting model or parameters as needed.

Post-mortem analyses after significant forecast deviations are key. We investigate the causes – was it a one-off event, or a systematic problem with the model? Was there new data that should have been incorporated? This iterative process of analyzing errors, adjusting the model, and re-evaluating accuracy is essential for continuous improvement.

Data visualization and reporting are critical for communicating demand plan insights to stakeholders. I use dashboards to present key performance indicators (KPIs) like forecast accuracy, inventory levels, and sales performance. Interactive visualizations, such as charts and graphs, make complex data readily understandable. For instance, a line chart showing historical demand and the forecast provides a clear picture of the predicted future demand.

In my previous role, I developed a dashboard that tracked forecast accuracy by product category and region. This enabled sales teams to focus on areas with the largest forecast errors, facilitating proactive adjustments and improved accuracy.

The forecasting horizon (the time period for which a forecast is made) significantly impacts forecast accuracy and applicability. Shorter horizons (e.g., monthly forecasts) are generally more accurate but offer less strategic planning guidance. Longer horizons (e.g., annual forecasts) are useful for long-term strategic decisions but tend to be less precise due to the increased uncertainty further into the future.

A longer horizon might be suitable for capacity planning and resource allocation, providing a high-level view of future demand. However, it would be less reliable for production scheduling, which relies on shorter-term, more precise predictions. Choosing the right horizon is a balance between accuracy and the planning needs.

Communicating demand forecasts effectively requires tailoring the message to the audience’s needs and understanding. I use a multi-pronged approach.

• Executive Summaries: For senior management, I provide concise summaries highlighting key trends, risks, and opportunities, focusing on high-level implications for strategy and resource allocation. This might include a single-page dashboard with key performance indicators (KPIs) and charts showing projected sales and inventory levels.

• Detailed Reports: For planning and operations teams, I deliver comprehensive reports with granular detail, including forecasting methodologies, underlying data sources, and assumptions. This allows them to understand the forecast’s nuances and make informed decisions about production scheduling, resource allocation, and supply chain management.

• Interactive Dashboards: I leverage data visualization tools to create interactive dashboards that allow stakeholders to explore the forecast data at their own pace. These dashboards might include drill-down capabilities, allowing users to analyze forecasts at different levels of detail (e.g., by product, region, or customer segment).

• Regular Meetings and Presentations: I conduct regular meetings and presentations to discuss the forecast with relevant stakeholders, address questions and concerns, and foster collaboration. These sessions provide opportunities for feedback and ensure everyone is aligned on the forecast and its implications.

For example, when forecasting for a new product launch, I might present a high-level summary to the executive team highlighting the projected market penetration and revenue potential. Simultaneously, I’d share a detailed report with the sales team outlining sales targets by region, incorporating historical sales data, marketing campaign plans, and competitive analysis.

Sales and Operations Planning (S&OP) is the cornerstone of effective demand planning. My experience involves actively participating in all phases of the S&OP process, from demand planning and forecasting to supply planning and inventory management.

• Demand Review: I participate in monthly demand review meetings, where we analyze historical sales data, market trends, and promotional plans to develop a consensus demand forecast. This involves collaborating closely with sales, marketing, and product management teams.

• Supply Review: I work with the supply chain team to assess the company’s capacity to meet the forecasted demand. This includes reviewing production capacity, inventory levels, and supplier capabilities. We identify potential constraints and develop strategies to mitigate risks.

• Integrated Reconciliation: In the integrated reconciliation phase, we align demand and supply plans, balancing customer demand with production capabilities and inventory levels. This often involves negotiating trade-offs and making adjustments to the demand forecast or the supply plan.

• Executive Review: Finally, we present the integrated plan to the executive team for approval. This ensures alignment across the organization and provides visibility into the overall business plan.

In a recent project, we used S&OP to successfully navigate a significant increase in demand due to a competitor’s product recall. By proactively reviewing capacity and supply chain risks, we were able to avoid stockouts and meet the surge in customer demand effectively.

Data analytics is crucial for optimizing demand planning processes. I leverage various analytical techniques to enhance forecasting accuracy and identify hidden patterns.

• Time Series Analysis: I use time series models (ARIMA, Exponential Smoothing) to identify trends, seasonality, and cyclical patterns in historical sales data. This helps to extrapolate historical data and predict future demand.

• Regression Analysis: I employ regression techniques to identify relationships between sales and various factors like price, promotions, marketing campaigns, and macroeconomic indicators. This allows for more accurate forecasting by incorporating external factors.

• Causal Modeling: I build causal models to understand the underlying drivers of demand. This is especially crucial for new products or in rapidly changing markets where historical data may be limited. Examples include incorporating economic data, competitive landscape analysis and market research findings.

• Machine Learning: I utilize machine learning algorithms (e.g., neural networks, random forests) for improved forecasting accuracy and pattern recognition. These methods can handle complex datasets and identify non-linear relationships.

• Data Visualization: I use tools such as Tableau or Power BI to visualize data and communicate insights effectively. This makes complex data easier to understand and allows for quicker identification of anomalies and outliers.

For example, using regression analysis, I identified a strong correlation between online advertising spend and website traffic, which in turn influenced sales. This allowed us to optimize our marketing budget and improve the accuracy of our sales forecasts.

Demand sensing is a crucial component of modern demand planning. It involves using real-time data to detect shifts in customer demand and adjust forecasts accordingly. Unlike traditional forecasting, which relies primarily on historical data, demand sensing anticipates future changes.

• Early Warning Signals: Demand sensing tools analyze a wide range of data sources, including point-of-sale (POS) data, web traffic, social media sentiment, and supply chain data to identify early warning signals of changes in demand. This allows for faster reaction times compared to traditional forecasting methods.

• Improved Forecast Accuracy: By incorporating real-time data, demand sensing can significantly improve forecast accuracy, particularly in volatile markets. It reduces the impact of unforeseen events and allows for more agile responses.

• Reduced Inventory Costs: By accurately anticipating changes in demand, demand sensing can help to optimize inventory levels, minimizing stockouts and reducing excess inventory costs.

• Enhanced Supply Chain Resilience: Demand sensing helps to improve the resilience of the supply chain by providing early warning of disruptions and allowing for proactive adjustments.

For instance, during a period of high volatility, demand sensing alerted us to an unexpected surge in demand for a specific product based on unusually high web traffic and social media mentions. We were able to adjust our production schedule and secure additional inventory, preventing a potential stockout.

Handling changes in market conditions and customer demand requires a flexible and adaptable approach. I employ several strategies to ensure our demand plans remain relevant and effective.

• Continuous Monitoring: I continuously monitor market trends, economic indicators, and competitive activity to identify potential risks and opportunities. This includes regularly reviewing sales data, market research reports, and news articles.

• Scenario Planning: I develop multiple demand scenarios (e.g., optimistic, pessimistic, baseline) to prepare for various potential outcomes. This allows us to plan for contingencies and mitigate risks.

• Agile Forecasting: I utilize agile forecasting techniques, which involve regularly updating forecasts based on the latest available data. This allows us to quickly adapt to changes in market conditions and customer behavior.

• Collaboration and Communication: I maintain close communication with sales, marketing, and other stakeholders to gather insights and feedback. This ensures that our demand plans reflect the latest information and are aligned with the overall business strategy.

• Early Warning Systems: I implement early warning systems to identify potential disruptions or changes in demand. This could include monitoring key performance indicators (KPIs), social media sentiment, and supply chain data.

For example, during the COVID-19 pandemic, we employed scenario planning to anticipate the impact on our sales. We developed different scenarios based on various levels of lockdowns and restrictions. This allowed us to adjust our production schedule and inventory levels to meet the changing demand effectively.

Capacity planning and demand planning are inextricably linked. Effective capacity planning ensures that we have the resources (production capacity, workforce, materials) necessary to meet the forecasted demand. They work together in a continuous feedback loop.

• Capacity Constraints: Demand plans must consider capacity constraints. If the demand forecast exceeds our production capacity, we need to adjust the plan by either increasing capacity or managing demand (e.g., through price adjustments or lead time increases).

• Resource Allocation: Capacity planning informs resource allocation decisions, ensuring that resources are deployed effectively to meet demand. This includes workforce scheduling, equipment maintenance, and material procurement.

• Bottleneck Identification: Effective capacity planning helps to identify potential bottlenecks in the production process. This allows us to address these issues proactively and avoid disruptions.

• Cost Optimization: By aligning capacity with demand, we can optimize production costs and minimize waste. Overcapacity leads to wasted resources, while undercapacity results in lost sales.

In a previous role, we used capacity planning software to model various production scenarios and identify potential bottlenecks. This enabled us to invest in additional equipment and optimize our production process, leading to improved efficiency and reduced costs.

The bullwhip effect is a phenomenon in supply chains where demand variability increases as you move upstream in the supply chain. A small change in customer demand can lead to significant fluctuations in orders placed further back in the chain.

• Causes: The bullwhip effect is often caused by factors like order batching, lead time delays, price fluctuations, and demand forecasting errors. Each stage of the supply chain amplifies these variations.

• Mitigation Strategies: Several strategies can be used to mitigate the bullwhip effect:

  • Improve Forecasting Accuracy: By improving demand forecasting accuracy, using techniques like demand sensing, we can reduce the variability in orders.

  • Reduce Lead Times: Shorter lead times reduce the time it takes to respond to changes in demand, reducing the amplification effect.

  • Information Sharing: Sharing point-of-sale (POS) data and other real-time information across the supply chain improves visibility and allows all stakeholders to make better decisions.

  • Everyday Low Pricing: Removing price incentives that cause customers to stockpile goods will reduce surges in orders.

  • Vendor Managed Inventory (VMI): Allowing suppliers to manage inventory levels can improve forecasting accuracy and reduce the bullwhip effect.

For example, by implementing a VMI program with one of our key suppliers, we were able to significantly reduce inventory holding costs and prevent stockouts, demonstrating a clear reduction in the bullwhip effect.

My experience with various data sources for demand planning is extensive. I’ve successfully integrated and analyzed data from diverse sources to build robust forecasts. Point-of-Sale (POS) data, a cornerstone of my approach, provides real-time insights into actual sales, revealing trends and seasonality. I’ve worked with POS data from various retailers, cleaning and transforming it to ensure accuracy and consistency. Market research data, including consumer surveys, competitor analysis, and industry reports, provides a crucial external perspective, allowing us to anticipate shifts in market demand and identify potential risks or opportunities. For example, I once used market research data indicating an upcoming trend in sustainable packaging to adjust our forecast for a product line, resulting in a significant increase in sales and reduced stockouts. Additionally, I’ve utilized economic indicators (GDP, inflation, etc.) and social media sentiment analysis to gain a holistic view of market dynamics and incorporate these insights into my forecasting models.

Beyond these, I have experience with integrating data from CRM systems (customer relationship management), providing granular customer-level insights, and ERP (Enterprise Resource Planning) systems, offering visibility into inventory levels and supply chain processes. Each data source contributes uniquely to the forecasting process, and the key is understanding their limitations and combining them effectively.

Data quality is paramount in demand planning. My approach to ensuring accuracy involves a multi-step process. It starts with data cleansing and validation: identifying and correcting inconsistencies, outliers, and missing values. This often involves using techniques like outlier detection algorithms and data imputation methods. For instance, I might use a moving average to fill in missing sales data for a particular day or week. Regular checks for data integrity are also crucial. This includes verifying data accuracy against multiple sources and using data profiling techniques to understand data distributions and identify potential anomalies. Regular reconciliation between different data sources is also vital to highlight discrepancies.

Moreover, I collaborate closely with relevant teams, such as sales and marketing, to ensure data accuracy and consistency. This includes establishing clear definitions and standards for data collection and reporting. Finally, regular reviews of forecast accuracy metrics, like Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD), are essential to identify areas for improvement and to refine our data quality control process.

In a previous role, we faced a significant challenge with a new product launch. The initial demand forecast, based on pre-orders and market research, was exceptionally high. However, as the launch date approached, internal capacity limitations became apparent. A crucial decision needed to be made: either risk a significant stockout, potentially damaging brand reputation, or reduce production to meet capacity and risk underselling a high-demand product. The latter option meant potentially forgoing significant revenue.

After careful analysis weighing the risk of stockouts (lost sales, damaged reputation) and the risk of undersupplying (lost sales opportunities), I presented a phased launch plan to mitigate both risks. We initially produced a smaller batch to test market response, gather real-time sales data, and gather feedback. Then, based on actual sales data and customer feedback, we scaled up production in subsequent phases. This approach allowed us to meet immediate demands and adapt our forecast based on real-world sales data, minimizing both financial loss and reputational damage. It also avoided the need for expensive and inefficient emergency production, allowing us to optimize our manufacturing and resources.

My experience encompasses several leading demand planning software solutions. I’ve worked extensively with systems like Anaplan, which is very strong for collaborative planning and complex modeling, and Demand Solutions, known for its advanced statistical forecasting capabilities. I also have experience with simpler tools like spreadsheets with macros for simpler forecasting needs. My proficiency extends to integrating these systems with other enterprise software, such as ERP and CRM systems.

Choosing the right software depends heavily on the specific needs of the organization, including the size and complexity of the business, the data available, and the level of sophistication required in the forecasting models. For instance, a smaller company might benefit from a simpler spreadsheet-based approach while a large multinational organization may require a sophisticated enterprise-level system like Anaplan. My ability to adapt and effectively utilize a variety of tools makes me a versatile asset in any demand planning setting.

Validating a demand forecast is crucial to ensure its accuracy and reliability. My preferred methods include comparing the forecast to historical sales data, evaluating the forecast’s accuracy using metrics like MAPE and MAD, and analyzing the forecast’s residuals (the differences between actual and forecasted values). This allows us to identify potential biases or systematic errors in the model. I also frequently perform scenario planning, exploring different potential outcomes based on variations in key factors like economic conditions or marketing campaigns.

Furthermore, I regularly engage with stakeholders across various departments (sales, marketing, operations) to gather qualitative feedback on the forecast’s plausibility and identify potential blind spots. This ensures that the forecast reflects not only the quantitative data but also the insights and expertise of the people closest to the market. A combination of quantitative and qualitative validation strengthens the forecast’s reliability and ensures that decisions made using it are well-informed.

Prioritizing competing demands in a dynamic environment requires a structured approach. I use a framework that incorporates several key elements. First, I categorize demands based on urgency and importance, often using a matrix that weighs both factors. Urgent and important demands, such as addressing critical stockouts, take precedence. Less urgent, less important demands might be deferred or addressed through alternative strategies. Second, I use data-driven insights to inform prioritization. Real-time sales data and market trends help to identify which demands are most likely to impact overall business objectives.

Collaboration is essential. I work closely with sales, operations, and marketing teams to gain a comprehensive understanding of competing demands and their implications. This ensures that the prioritization decisions are aligned with broader business strategies and operational capabilities. Finally, I employ agile planning techniques, allowing for flexibility and adjustments as the situation evolves. Regularly reviewing and adapting the prioritization plan ensures that it remains effective in a dynamic environment. This ensures that we focus our resources on the most impactful activities, maximizing our return on investment while mitigating risk.

My understanding of inventory management strategies is grounded in the need to balance cost and service levels. Just-in-Time (JIT) inventory is a strategy aimed at minimizing inventory holding costs by ordering materials only when needed. It is highly efficient but relies heavily on precise demand forecasting and reliable supply chains. Any disruption can lead to significant production delays or stockouts. A major challenge with JIT is that it requires excellent coordination across the entire supply chain and exceptional demand forecasting accuracy.

Safety stock, on the other hand, represents a buffer stock held to protect against demand variability and supply chain disruptions. It is essential for maintaining service levels but increases inventory holding costs. The optimal safety stock level depends on several factors, including demand variability, lead times, and desired service level. Calculating safety stock levels often involves statistical techniques considering historical demand data and lead time variability. The choice between JIT and safety stock (or a hybrid approach) depends on the specific characteristics of the product, the industry, and the overall risk tolerance of the business.