From the previous blog, we saw a perspective of how AI can help in the manufacturing domain. It may seem to be overwhelming for Non-Tech people to adopt and make use of this revolution. The examples on the application of AI are very functional oriented and can be used in various other domains.
For Tech folks, I know you are already up and running with the applications of AI, and would like to learn from you. So jump in there and share your learnings in the comments.
Let us consider a hypothetical situation with Examples of AI:
Assume that the company has historical sales data for the past three years, including the number of smartphones sold each month. Additionally, they have access to various external data sources, such as market trends, customer feedback, and promotional activities.
With the assistance of Chat GPT, the demand forecasting process can be enhanced as follows:
1. Data Gathering and Analysis: The AI system, integrated with Chat GPT, collects and analyses the historical sales data, considering various factors like seasonality, product launches, and market conditions. It also incorporates qualitative data from customer conversations, online reviews, and social media sentiment analysis.
For example, the system may analyse the impact of positive reviews and customer discussions on social media regarding the latest smartphone features. Chat GPT assists in extracting meaningful insights from these unstructured data sources, providing a more comprehensive understanding of customer preferences and market dynamics.
2. Model Development and Training: Using machine learning algorithms, the AI system, with Chat GPT's support, develops a demand forecasting model. It leverages the historical data and the insights gained from conversational analysis to train the model. The system continually refines the model as new data becomes available.
Let's assume that during the training process, the AI system identifies a correlation between customer conversations about battery life and the subsequent sales figures. This insight can be incorporated into the forecasting model, enabling more accurate predictions.
3. Demand Prediction and Scenario Analysis: Once the demand forecasting model is trained, it can generate predictions for future months based on input parameters such as marketing campaigns, anticipated product launches, and industry trends. Chat GPT assists in running scenario analysis and considering different factors that might influence demand.
For instance, let's suppose the company plans to launch a new smartphone model in the coming quarter. By discussing potential marketing strategies and promotional activities with Chat GPT, the system can generate demand forecasts for different scenarios, considering variables like pricing, advertising campaigns, and competitor responses.
4. Production Planning and Inventory Management: Using the AI-generated demand forecasts, the manufacturing company can optimize its production planning and inventory management processes. They can align their manufacturing capacity and raw material procurement based on anticipated demand.
For example, if the demand forecast indicates a surge in smartphone sales during the holiday season, the company can adjust production schedules accordingly and ensure an adequate supply of components and finished products to meet customer demand. Chat GPT can assist in analysing production capacity, lead times, and supply chain constraints, facilitating informed decision-making.
Conclusion:
By leveraging AI and Chat GPT for demand forecasting, the manufacturing company gains a competitive advantage by improving inventory management, reducing stockouts, and optimizing production resources. This enables them to meet customer demands more effectively while minimizing inventory holding costs and maximizing profitability.
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