Japolli Bakery Offers A Variety Of Bread Types

Japolli Bakery Makes A Variety Of Bread Types That It Sells To Superma

Japolli Bakery produces a variety of bread types that are sold to supermarket chains. The daily demand for each bread type varies significantly, complicating the process of determining the optimal number of loaves to bake. Using sample demand data, the task involves developing frequency distributions for each bread type with suitable intervals. Additionally, it requires identifying which bread type exhibits the greatest and the lowest relative variability. Based on this data, assuming it accurately reflects annual demand, the goal is to determine the number of loaves that should be baked for each type to meet at least 75% of the demand during the year.

Paper For Above instruction

The operational efficiency of bakery production hinges critically on accurately forecasting demand. For Japolli Bakery, which supplies multiple bread types to supermarket chains, understanding demand variability and establishing appropriate baking quantities are vital. The primary objectives are to analyze the demand data comprehensively, assess variability, and develop a production plan aligned with service level goals, specifically covering at least 75% of demand days annually.

Development of Frequency Distributions

Creating frequency distributions involves segmenting the demand data into intervals or classes that reflect the distribution of daily demand. The first step is to analyze the raw demand data for each bread type, extracting the minimum and maximum demand quantities. Appropriate intervals are then determined, typically based on Sturges' rule or similar heuristics, to balance granularity and clarity. For example, if demand ranges from 10 to 100 loaves, intervals might be set every 10 or 15 loaves, ensuring that most data points fall within these classes.

For each bread type, the number of days falling within each interval is counted, forming the basis of the frequency distribution. These distributions provide a visual and statistical summary of demand variability, highlighting common demand levels and outliers.

Assessing Variability and Identifying Extremes

To compare variability across different bread types, the relative variability metric — typically the coefficient of variation (CV) — is used. The CV is calculated by dividing the standard deviation of demand by the mean demand for each bread type, providing a normalized measure of dispersion. A higher CV indicates greater variability relative to the average demand, while a lower CV suggests more consistent demand.

Through this analysis, the bread type with the highest CV is identified as having the greatest relative variability, requiring more flexible or safety stock strategies. Conversely, the bread type with the lowest CV is more predictable, allowing for more streamlined production planning.

Determining Production Quantities for 75% Service Level

Assuming the sample data accurately reflects annual demand patterns, the next step is to determine the production quantities that meet or exceed demand during 75% of the days. This involves calculating the demand level corresponding to the 75th percentile of the demand distribution for each bread type.

Using the cumulative frequency distribution, the 75th percentile demand is identified where the cumulative frequency reaches or exceeds 75% of the total observations. This value indicates the number of loaves that should be produced daily to meet demand on at least three-quarters of days in the year.

Alternatively, if demand data exhibits a known distribution (e.g., normal distribution), statistical methods such as using the mean plus 0.674 standard deviations (z-score for 75%) can be employed to estimate the production level.

By adopting these demand predictions, Japolli Bakery can align its production schedule to meet customer needs efficiently, minimize stockouts, and avoid overproduction, thus optimizing overall operations.

Conclusion

In summary, detailed analysis of demand data through frequency distribution and variability assessment provides valuable insights for bakery production planning. Identifying the bread types with the most and least demand fluctuation helps tailor inventory and production strategies. Calculating the appropriate production quantities at the 75% service level ensures a balance between customer satisfaction and operational efficiency. Implementing these data-driven approaches will enable Japolli Bakery to meet customer demands effectively while managing costs and resources optimally.

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