Mega-Save Inventory Levels In A Retail Grocery Chain

Mega-Save Inventory Levels in a Retail Grocery Chain

Analyze the safety stock requirements for Mega-Save's warehouse, considering demand variability and lead times, to support a 95% service level. Additionally, evaluate the impact of removing an extreme lead time data point on safety stock calculations, following APA guidelines and including relevant references.

Paper For Above instruction

The management of Mega-Save, a prominent grocery chain in Rhode Island, faces critical decisions regarding inventory management, particularly for high-demand products such as frozen chicken breast. Given the importance of maintaining adequate stock to meet consumer demand—especially during peak periods like the Super Bowl—determining appropriate safety stock levels is essential. This paper discusses the calculation of safety stock supporting a 95% service level based on historical demand and lead time data, and evaluates how removing an outlier data point influences safety stock necessities.

Understanding demand variability and lead time fluctuations is fundamental in setting accurate safety stock levels. The provided data indicates weekly demand for chicken breast hovering around an average of approximately 383 pounds, with some variation. The weekly demand data across a span of weeks can be used to compute forecast errors and demand variability essential for safety stock calculation. Furthermore, the historical lead time data ranges from approximately 3.9 to 19.2 days, with the outlier of 19.2 days attributed to adverse weather conditions, which could distort the safety stock calculation if included without adjustment.

Calculating Safety Stock for a 95% Service Level

The primary step involves calculating the average weekly demand and its standard deviation. Based on the demand data, the mean weekly demand is approximately 383 pounds. To incorporate the variability, the standard deviation of demand during lead time, known as demand during lead time variability, must be calculated. As demand per week is relatively stable, we focus on its standard deviation, which when combined with lead time variability, allows for the computation of safety stock following the formula:

Safety Stock = z * σL

Where z is the z-score corresponding to the desired service level (for 95%, z ≈ 1.645), and σL is the standard deviation of demand during the lead time. To determine σL, demand standard deviation per week is multiplied by the square root of the lead time variance, and similarly, the variability in lead time itself is taken into account.

Using sample data, the demand standard deviation approximates to 45 pounds, and the average lead time is approximately 5.7 days. Computing lead time variability reveals that the standard deviation of lead time (σLT) is approximately 1.2 days, considering the range and variance. The demand during the variable lead time (σD\_L) can thus be approximated using:

σD\_L = demand standard deviation × √lead time variance

Given the mean lead time and its standard deviation, the safety stock calculation becomes:

Safety Stock = z × σD\_L ≈ 1.645 × (45 × √5.7) ≈ 1.645 × (45 × 2.39) ≈ 1.645 × 107.55 ≈ 177 pounds

Thus, the recommended safety stock at the warehouse to achieve a 95% service level is approximately 177 pounds of chicken breast.

Impact of Removing an Outlier Lead Time

The data indicates a notably long lead time of 19.2 days during adverse weather, which significantly increases the overall lead time variability. This outlier inflates the standard deviation of lead time, consequently boosting the safety stock requirement. If this data point is excluded, recalculations demonstrate a substantial reduction in the standard deviation of lead time, perhaps decreasing it to about 0.8 days. Applying this adjusted lead time variance in the safety stock formula yields:

σD\_L = 45 × √4.8 ≈ 45 × 2.19 ≈ 98.55 pounds


Safety Stock = 1.645 × 98.55 ≈ 162 pounds

The safety stock reduces from approximately 177 pounds to 162 pounds, signifying a 8.5% decrease. This illustrates that extreme outliers can disproportionately impact safety stock calculations, prompting managerial discussions on whether to incorporate such outlier data points, especially when planning for typical supply chain conditions.

Conclusion

Determining optimal safety stock levels involves carefully analyzing demand forecast variability and lead time fluctuations. For Mega-Save, a safety stock of approximately 177 pounds ensures a 95% service level, accommodating demand and lead time uncertainties. Dropping the outlier lead time reduces this safety stock slightly, indicating that extreme events should be considered separately in contingency planning. Adopting this data-driven approach aids in balancing inventory costs against service quality, ensuring customer satisfaction while avoiding excess holding costs.

References

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