Objective:

The objective of demand forecasting is to predict the future demand for a product or service. This prediction helps businesses make informed decisions about various aspects of their operations

Introduction to demand forecasting:

Demand forecasting is the activity which is aimed at the prediction of the popularity of the goods and services among costumers. The problem of demand forecasting is closely connected with the problem of supply and demand and there are numerous theories and methods which are useful on the way of the prediction of the popularity.

Demand forecasting plays an important role for businesses in different industries, particularly with regard to mitigating the risks associated with particular business activities. However, demand forecasting is known to be a challenging task for businesses due to the intricacies of analysis, specifically quantitative analysis

A company faces inconsistent profits and inefficiencies due to inaccurate predictions of future demand for its products or services.

 This leads to:

• Overstocking: Unnecessary inventory holding costs, potential product spoilage, and wasted storage space.
• Understocking: Lost sales opportunities, frustrated customers, and potential damage to brand reputation.
• Inefficient resource allocation: Difficulty planning production, staffing, and marketing efforts due to unreliable demand estimates.

Importance of demand forecasting:

• Meeting goals – Most successful organisations will have pre-determined growth trajectories and long-term plans to ensure the business is operating at an ideal output. By having an understanding of future demand markets, businesses can be proactive in ensuring that goals will be met in this business environment.
• Business decisions – In reference to meeting goals, by having a thorough understanding of future industry demand, management and key board members can make strategic business decisions that encourage higher profitability and growth. These decisions are generally associated with the concepts of capacity, market targeting, raw material acquisition and understanding vendor contract direction.
• Growth – By having an accurate understanding of future forecasts, companies can gauge the need for expansion within a timeframe that allows them to do so cost effectively.
• Human Capital Management – If there is a rapid demand increase in an industry but a business does not have enough employees to satisfy the sales orders, consumer loyalty may be adversely affected as customers are forced to purchase from competitors.
• Financial Planning – It is crucial to understand demand forecasts in order to efficiently budget for future operations in terms of factors such as cash flow, inventory accounting and general operational costs

Process :

1. Data collection & Pre-processing:

• Gather data for the analysis.
• Clean and pre-process the data: Handle missing values, outliers, and  inconsistencies.

2. Choose machine learning algorithm:

    Several algorithms can be used, each with its own strengths and weaknesses. Here are some popular c­­­­hoices:

• Regression models: Linear Regression, Support Vector Regression, Random Forest Regression
• Decision Trees & Rule-based models: CART, CHAID
• Ensemble methods: Gradient Boosting, Random Forest
• Neural networks: LSTM, Prophet

3. Feature engineering:

• Create additional features from existing data that might be more predictive of demand.
• This could involve transformations, time-based features interaction terms, etc.

4. Train and evaluate the model:

• Split your data into training, validation, and testing sets.
• Train the model on the training data and evaluate its performance on the validation set.
• Use metrics like Mean Absolute Error (MAE) or Mean Squared Error (MSE) to measure accuracy.
• Fine-tune hyperparameters based on validation results.
• Finally, evaluate the final model on the unseen testing data to assess its generalizability.

5. Deploy and monitor the model:

• Integrate the model into your existing processes to generate forecasts.
• Monitor the model’s performance over time and retrain it periodically with new data to maintain accuracy.

Applications:

Demand forecasting plays a crucial role in various business functions by predicting customer demand for products and services. Here are some key applications:

• Inventory Management: By anticipating demand, businesses can optimize inventory levels. This helps prevent stockouts, which can lead to lost sales and customer dissatisfaction. It also avoids excessive inventory, which ties up capital and incurs storage costs.
• Production Planning: Manufacturers use demand forecasts to plan production schedules and resource allocation. Accurate forecasts ensure they have the capacity and materials to meet anticipated demand, avoiding production delays or disruptions.
• Financial Planning: Demand forecasts are essential for budgeting and financial planning. Businesses can predict revenue streams, plan for expenses, and secure financing based on anticipated demand.
• Pricing Strategies: Understanding demand trends helps businesses develop effective pricing strategies. They can adjust prices based on seasonal fluctuations, competitor activity, and anticipated demand for new products.
• Marketing and Promotions: Demand forecasts can inform marketing campaigns and promotional strategies. Businesses can target promotions towards periods of high demand and tailor marketing efforts to products with anticipated growth.
• New Product Development: Demand forecasting can guide new product development by identifying potential market opportunities and assessing the viability of new product launches.

Advantages:

Improved Demand Prediction Accuracy: 

        More accurate forecasts lead to:

• Reduced overstocking and understocking issues, minimizing inventory costs and storage needs.
• Optimized inventory levels, freeing up capital and space for other investments.
• Enhanced resource allocation, improving production efficiency and staffing plans.

Increased Revenue and Profitability: 

• Better production planning to meet demand and avoid stockouts, maximizing sales opportunities.
• Targeted marketing campaigns based on predicted demand segments, attracting the right customers.
• Enhanced customer satisfaction and potential for increased sales due to consistent product availability.

Conclusion: