Demand Forecasting Control for New Product Production Lines
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Solution Overview
Problem
Existing new product development processes are deductive and inefficient, relying on expert opinions and subjective experiences, which makes it uncertain whether new products will meet consumer and market needs after launch.
Innovation Solution
A predictive new product development method and device that calculates a new product differentiation index (PDI) and demand creation index (DCI) using consumer satisfaction coefficients and degree of differentiation, then builds a demand forecasting model through Gaussian process regression to predict initial sales volume and derive an optimal product profile.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deductive new product development process relying on expert opinions and subjective experiences is used, then the development process is simple and quick to implement, but the accuracy of predicting market demand and consumer needs deteriorates
Solution Approach 1:
The patent applies preliminary action by collecting and analyzing consumer feedback data before the new product is launched. The system gathers data from multiple sources including social media, customer reviews, and surveys in advance, then uses machine learning models to predict demand and optimize product features before market release, eliminating the need for post-launch demand assessment
Solution Approach 2:
The patent replaces the mechanical system of expert opinions and subjective judgment with an automated machine learning-based prediction system. The system uses algorithms to analyze consumer data and generate objective demand predictions, substituting human expert analysis with computational modeling that processes large datasets systematically
2Productivity
If production volume is determined without predictive modeling, then the production process is simple and fast, but the efficiency of matching supply with actual market demand deteriorates
Solution Approach 1:
The patent implements feedback by continuously monitoring consumer responses and product performance data, then using this information to adjust production volumes dynamically. The system collects real-time data from sales channels and consumer feedback sources, feeds this information into the machine learning model, and uses the predictions to optimize production planning and inventory management
Solution Approach 2:
The patent applies parameter changes by using the machine learning model to predict optimal production volumes based on varying market conditions, consumer preferences, and product features. The system adjusts production parameters dynamically according to predicted demand, optimizing the match between supply and actual market needs rather than using fixed production schedules
Data Source
AI summary
The present disclosure relates to a method and an apparatus for controlling production lines based on prediction of machine learning models. The method and apparatus: obtain a new product differentiation index (PDI) by obtaining a consumer satisfaction coefficient for each feature of predetermined features and a degree of differentiation of the each feature, obtain a demand creation index (DCI), building a demand forecasting machine learning model through Gaussian process regression of the PDI and the DCI; derive an optimal profile for the new product by using the demand forecasting machine learning model; predict the sales volume of the new product by using the demand forecasting machine learning model; and transmit a control signal to the production lines, such that a production volume of the new product is directly controlled, in real time, based on the predicted sales volume.