# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 4 Data Analysis
### The Analysis of Temporal Patterns and Demand Trends  
Identifying temporal patterns is critical for forecasting seasonal demand in cold chain logistics, where demand often fluctuates due to seasonal or promotional influences. Time-series decomposition with the ARIMA model provided insights into these trends, allowing the model to capture seasonality and better align inventory with demand.  
\begin{table}
\begin{tabular}{l c} \hline
**CRISP-DM Phase** & **Key Activities** \\ \hline Modeling & - Develop ARIMA and MLR models - Calibrate seasonal trends - Use MAE and RMSE metrics - Assess model accuracy - Align inventory with demand - Recommend improvements \\ \hline \end{tabular}
\end{table}
Table 1: Cont.  
Figure 2: Sales Data Summary provides an overview of sales distribution.  
Figure 5 highlights consistent ordering patterns for products A and B, indicating demand peaks at specific times. Such identifiable cycles are essential for inventory planning, as they allow Company A to proactively prepare for high-demand periods and avoid excess inventory, contributing to waste. These insights align with [PERSON] and [PERSON] (2010), who emphasize the importance of understanding seasonality for effective stock management in time-sensitive logistics.  
Figure 6, Time Series Decomposition for Product C, breaks down demand into trend, seasonality, and residuals, revealing significant demand spikes in late 2022 and early 2023. Such patterns suggest the influence of promotional activities or seasonal cycles on demand. Leveraging these insights allows for targeted inventory adjustments during high-demand periods, thereby reducing overproduction risks. Seasonal trends identified in this manner can guide managers in aligning inventory levels with expected demand spikes, minimizing the potential for waste (2010).  
Figure 4: Aggregated Customer Order Shares.  
Figure 3: The count of products in the sales dataset illustrates product distribution.  
The variations in data for Product C, as shown in Figure 6, are primarily driven by seasonal demand fluctuations and promotional activities, which significantly influence consumer purchasing behavior. Seasonal demand reflects predictable increases or decreases in sales during specific times of the year, such as holidays or peak seasons, while promotional activities result in short-term spikes in demand. These factors create deviations from the overall trend, which are critical to understanding and modeling demand patterns.  
To better illustrate these variations, Table 2 shows the weekly observed demand, trend component, and the difference between the two for Product C. This quantification provides a clearer understanding of the magnitude and drivers of variations in the dataset.  
Figure 5: Temporal Consistency of Orders Over Time for Selected Products.  
Figure 6: Time Series Decomposition for Product “C”.  
Figure 7 displays the order distribution across customers, segmented into high and low-distribution products. The blue and yellow segments represent orders for high and low-distribution products, respectively. We observe that:  
1. Imbalance in Customer Orders: Certain customers place significantly more orders, particularly for high-distribution products, leading to a skewed order distribution. This uneven distribution highlights a concentration in order volume among a few customers.
2. Data Imbalance's Potential Impact: The concentration of orders among specific customers may bias predictive models, as high-order customers dominate the dataset. Such an imbalance can distort demand forecasts and may reduce the generalizability of predictive models.
3. Suggested Adjustments: To address this skew, segmentation or weighting of customers (especially those with high order volumes) is recommended. By applying these adjustments, the model can better balance the data distribution, improving the accuracy and reliability of demand forecasts.  
This visualization underscores the need for data preprocessing steps to balance order volumes across customers for more accurate demand predictions.