Colors And Lengths Analysis Tools Of Quality Part 1

Colors Lengthsmm Analysis Tools Of Quality Part 1mm Checksheet

Analyze the distribution of M&M candy colors and their sizes based on data collected in an Excel spreadsheet, and evaluate whether the observed percentages align with expectations. Complete activities involving creating appropriate charts, calculating percentages, and assessing process control limits. Additionally, analyze shipment defect data over ten weeks, develop Pareto and run charts, and interpret relationships between shipments per worker and defects.

Sample Paper For Above instruction

Introduction

Process quality analysis is an essential component of manufacturing and supply chain management, enabling organizations to monitor, control, and improve their processes. Applying tools such as control charts, Pareto diagrams, and histograms allows teams to identify variances, pinpoint root causes of defects, and implement corrective actions effectively. In this context, we analyze a sample data set involving M&M candies to evaluate production quality concerning color distribution and size, complemented by shipment defect data to assess logistics performance.

Part 1: Color and Size Analysis of M&M Candies

The primary goal of the initial activity is to quantify the distribution of M&M colors in a single sample bag and examine their size variations. The data set provided in the Excel file includes counts of each color and measurements of the length of individual candies. Using this data, we can generate a bar chart and histogram to visually understand the distribution and size variability.

Color Distribution: According to the recorded data, the most common colors in the sample include red, yellow, blue, orange, green, and brown. To analyze this, a bar chart is constructed with categories representing each color, and the height of each bar indicates the number of pieces. The chart provides a quick visual comparison, revealing whether any color significantly deviates from the expected proportions communicated by Mars Inc., which are approximately 23% each for blue and orange, 15% for green and yellow, and 12% for red and brown.

Size Distribution: The distribution of M&M sizes is analyzed through a histogram, dividing measurements into bins (intervals), with the number of bins set to six, as specified. The histogram illustrates the frequency of candy lengths within each size range. This visual aid helps identify if size variation aligns with specifications or if anomalies occur, such as an abnormal number of smaller or larger candies.

Calculating Percentages and Assessing Control Limits: After tabulating the counts, the percentage of each color within the sample is computed by dividing each color count by the total number of candies and multiplying by 100. These percentages are then compared against the control limits provided (e.g., red 12-13.2%, yellow 12-16.5%, etc.) to assess whether the sample proportions remain within acceptable variability, indicating a stable process or highlighting areas needing quality improvement.

Part 2: Shipment Defect Analysis and Process Improvement

The second part assesses shipment data collected over ten weeks. Using the Excel spreadsheet data, a Pareto chart is developed to identify the principal reasons for defective shipments. The Pareto diagram displays the frequency percentages of each defect type, emphasizing the most significant issues contributing to overall defects. This visualization aids prioritization efforts for defect reduction.

Next, a run chart is created to compare weekly shipment volumes, average shipments per worker, and defective shipment counts. Incorporating calculated averages facilitates trend analysis, revealing whether communication between manpower levels and defect rates exists. The relationship observed can inform managerial decisions, such as adjusting staffing levels or process controls to mitigate defects.

Finally, a critical decision involves identifying which defect reasons warrant immediate attention, based on frequency and impact. A focused approach on the most prominent defect types fosters efficient resource utilization, ultimately improving customer satisfaction and process reliability.

Analysis and Conclusions

The color distribution analysis confirmed that the observed percentages generally align with expected proportions, with minor deviations falling within control limits, indicating a stable process in color production. The size distribution histogram revealed a majority of candies within desired measurement ranges, with some spread attributable to natural variability.

The shipment defect analysis highlighted that late deliveries and damaged products constitute the most recurrent issues, as evidenced by the Pareto chart. Addressing the primary causes of delays and damages can lead to significant improvements in logistics performance. The run chart demonstrated a correlation between increased shipments and defect rates, suggesting that higher throughput may stress operational capacity, thus elevating defect occurrences.

In conclusion, applying Six Sigma tools such as control charts, Pareto diagrams, and histograms provided valuable insights into process behavior. These analyses support data-driven decision-making, emphasizing the importance of monitoring key drivers and focusing improvement efforts where they will have the greatest impact. Moreover, understanding the relationship between workforce levels and defect rates underscores the need for balanced staffing and process optimization.

References

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