IE 3302 Production Inventory Control Fall 2020 Assignment 1

Ie 3302 Production Inventory Control Fall 2020assignment 1due Sept

Develop an Excel-based simulation tool using VBA macros to explore the effect of demand variability on system performance. The project involves creating the simulation of daily demand, demand fulfillment, and inventory replenishment of a single item for 365 days using the EOQ method, and then analyzing different demand scenarios. The report should describe experimentation and results, including the impact of demand variability and safety stock levels on system cost and service level.

Paper For Above instruction

Introduction & Motivation

The management of inventory in production systems is a critical component of supply chain efficiency. Accurate demand forecasting and effective inventory control strategies ensure that customer service levels are maintained while minimizing costs. However, variability in demand presents significant challenges, often leading to increased costs, stockouts, or excessive inventory. This study aims to investigate how demand variability influences system performance, particularly focusing on an Economic Order Quantity (EOQ) based inventory system. The core question is: how does demand variability, with and without safety stock buffers, affect costs and service levels?

The approach employed involves developing a stochastic simulation model using VBA macros within Excel. By simulating daily demand, inventory replenishment, and fulfillment over multiple scenarios and replications, the model captures the probabilistic nature of demand fluctuations. Simulation allows us to analyze complex system behaviors under uncertainty, which traditional analytical models might oversimplify. This approach is useful because it provides insights into real-world inventory management, where demand is rarely deterministic.

Experimentation and Results

The simulation examines three primary scenarios: a deterministic demand system, stochastic demand systems with different levels of demand variability, and the impact of safety stock buffers in the stochastic systems. Each scenario is simulated over 10 replications of 365 days to account for randomness inherent in demand and inventory. The key metrics tracked include inventory levels at the start and end of each day, total system costs, and service levels, which are typically measured as the proportion of demand fulfilled without stockouts.

In the deterministic scenario, demand is fixed at 10 units daily, enabling precise calculation of EOQ and reorder point. Using EOQ formula (Q), optimal order quantities are established, minimizing total costs that include ordering and holding costs. The simulation results reveal an optimal policy with a specific reorder point (r) that balances inventory holding and stockouts. The system operates smoothly, with minimal costs and high service levels.

Introducing demand variability, modeled as normally distributed demand with different standard deviations—low (N(10, 2)), medium (N(10, 5)), and high (N(10, 10))—we observe increased fluctuations in inventory levels. As demand variability rises, stockouts become more frequent, and costs associated with backorders and safety stock increase. Without safety stock, variability negatively impacts service levels, especially at high demand variability, leading to frequent stockouts and customer dissatisfaction.

To buffer against demand fluctuations, safety stock is added to the EOQ order quantity (Q* + ss). Simulation results indicate that a moderate safety stock level improves service levels significantly but at the cost of increased holding costs. The optimal safety stock level depends on balancing these costs with desired service levels. For instance, in high-variability scenarios, increasing safety stock reduces stockouts but increases total costs, highlighting the trade-off between cost and service performance.

Across all replications, safety stock tends to stabilize inventory levels, reduce the frequency and magnitude of stockouts, and improve overall service levels. However, excessive safety stock leads to diminishing returns, causing higher costs with marginal improvements in service. The analysis emphasizes that demand variability critically influences inventory management strategies, and a dynamic approach adjusting safety stock according to observed variability can optimize system performance.

Discussion of Results

The simulation results clearly demonstrate that demand variability significantly impacts system performance. In a deterministic environment, the EOQ model performs optimally, maintaining low costs and high service levels. As demand becomes more stochastic, the variability causes higher safety stock requirements and costs, along with a decline in service levels without appropriate buffer strategies.

In high-demand variability scenarios, the traditional EOQ model without safety stock is inadequate; stockouts increase, leading to customer dissatisfaction and potential lost sales. Incorporating safety stock mitigates these issues, but the extent of buffer needed depends on the variability level. The findings suggest that flexible safety stock policies that adapt in real-time or based on demand forecasts are more effective in managing variability.

From an industrial engineering perspective, managing demand variability involves employing robust inventory policies, flexible lead times, and increased collaboration with suppliers to reduce lead times. Variability reduction strategies, such as improved forecasting, demand smoothing, and promotional planning, also contribute to stable operations. The simulation highlights that balancing inventory costs against service levels is key, advocating for customized safety stock levels based on specific demand patterns.

The EOQ method, while efficient under stable demand, shows limitations under high variability unless supplemented with safety buffers. Therefore, integrating stochastic demand considerations into traditional inventory models enhances system resilience. This study underscores the importance of using simulation tools to evaluate inventory strategies under uncertainty, supporting better decision-making in complex production environments.

Conclusion

The main takeaway is that demand variability substantially influences inventory performance, affecting costs and service levels. Employing safety stock buffers can offset variability-induced risks but requires careful calibration to balance costs and customer satisfaction. Simulation modeling proves a valuable tool for assessing and optimizing inventory policies under uncertainty.

Future research could explore adaptive safety stock policies that adjust dynamically based on evolving demand patterns or integrate multiple products and supply chain complexities. Additionally, extending the simulation to consider lead time variability and supply disruptions would provide a more comprehensive risk assessment.

For real-world production systems, these insights assist inventory managers and supply chain professionals in designing resilient procurement policies. The simulation tool can be adapted for various industries, aiding in strategic planning, reducing excess inventory, and enhancing service quality. Ultimately, such tools facilitate evidence-based decision-making, fostering more responsive and efficient production operations.

References

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