Bootstrap Resampling for Print Job Demand Forecasting
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Solution Overview
Problem
Existing methods for forecasting print job demand, such as ARIMA time series models, fail to accurately consider future market outlook and require large datasets, and bootstrapping methods like Efron's are inconsistent when applied to heavy-tailed distributions common in print shop operations.
Innovation Solution
A system that uses a computing device to collect historical print job data, generates bootstrap samples, and determines a target job volume by resampling with replacement, while accounting for percentage increases and heavy-tailed distributions through specific acceptance limits and robustness testing to forecast future demand effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional bootstrapping methods (e.g., Efron's) are used for forecasting print job demand, then the method is simple to implement, but it produces inconsistent results when applied to heavy-tailed distributions common in print shop operations
Solution Approach 1:
The patent modifies the traditional bootstrapping algorithm by changing the resampling parameters to account for heavy-tailed distributions. Specifically, it adjusts the acceptance criteria and resampling strategy to handle the statistical characteristics of print job demand data, which exhibits heavy tails rather than normal distribution. This parameter modification allows the method to maintain simplicity while achieving consistent forecasting results for heavy-tailed data.
2Reliability
If ARIMA time series models are used for forecasting print job demand, then the model structure is well-established, but it fails to accurately consider future market outlook and requires large datasets
Solution Approach 1:
The patent applies a partial bootstrapping approach that resamples only the necessary portion of historical data to generate forecasts, rather than requiring comprehensive long-term datasets. By using bootstrap resampling with acceptance limits, the method achieves reliable forecasts with smaller, more recent datasets that better reflect current market conditions and future outlook.
3Measurement precision
If demand data is collected through traditional methods to simulate and optimize print shop configurations, then the data can be used for operational optimization, but the data collection process is expensive and time-consuming
Solution Approach 1:
The patent performs preliminary bootstrapping analysis on historical print job data to generate demand forecasts before actual production planning. By pre-processing historical data through bootstrap resampling and establishing acceptance criteria in advance, the system eliminates the need for time-consuming real-time data collection during optimization processes, thereby reducing both time and cost while maintaining accuracy.
Data Source
AI summary
A system of forecasting future print job demand for a print shop may include a computing device, a print shop having a plurality of print devices in communication with the computing device, a historical database in communication with the computing device, and a computer-readable storage medium comprising one or more programming instructions.