Short answer
When forecasting demand for products influenced by economic conditions, like farm tractors, incorporate relevant external variables (e.g., credit availability) into your forecasting models for improved accuracy.
- Field
- Commercial Production
- Source
- Indian Journal of Agricultural Research (2019)
- Method
- Quantitative forecasting model comparison
- Evidence
- Strong effect
Incorporating external economic factors like agricultural credit significantly improves the accuracy of monthly farm tractor demand forecasts compared to purely time-series models. This commercial production research insight is drawn from a 2019 study published in Indian Journal of Agricultural Research. Using Quantitative forecasting model comparison, researchers explored how this design variable affects real-world outcomes. The key design takeaway: When forecasting demand for products influenced by economic conditions, like farm tractors, incorporate relevant external variables (e.g., credit availability) into your forecasting models for improved accuracy.
ARMAX model with agricultural credit outperforms MSARIMA for Indian tractor demand forecasting
Incorporating external economic factors like agricultural credit significantly improves the accuracy of monthly farm tractor demand forecasts compared to purely time-series models.
Indian Journal of Agricultural Research · 2019
Key Findings
- 01The ARMAX model, which included real agricultural credit as an exogenous variable, demonstrated superior performance in forecasting monthly farm tractor demand over a six-month horizon compared to the MSARIMA model.
- 02Accurate monthly forecasting of farm tractor demand can significantly aid manufacturers in managing raw materials, inventory, and supply chains more effectively.
Application
Design takeaway
When forecasting demand for products influenced by economic conditions, like farm tractors, incorporate relevant external variables (e.g., credit availability) into your forecasting models for improved accuracy.
How to apply
When developing production plans for seasonal or economically sensitive products, use forecasting models that can integrate external data such as interest rates, credit availability, or relevant commodity prices.
Project actions
- 01When choosing a forecasting model for your design project, consider if external factors significantly influence demand.
- 02If you are forecasting demand for a product, research economic indicators that might affect its sales and explore models that can incorporate them.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Direct comparison of two established forecasting models.
- +Inclusion of a relevant exogenous variable (agricultural credit) that logically impacts demand.
- +Focus on a significant market for the product.
Limitations
The availability and quality of external data can be a challenge. The chosen external variables might not capture all influencing factors, and the relationship between variables can change over time.
Reliability & validity
The study's reliability is supported by the use of established statistical models (MSARIMA, ARMAX) and quantitative comparison metrics. Validity is enhanced by the logical inclusion of agricultural credit as a driver of tractor demand.
Think critically
How might the relationship between agricultural credit and tractor demand change in different economic climates or with shifts in government policy?
Design Principles
"Demand forecasting accuracy is enhanced by incorporating exogenous economic variables that influence purchasing power and market behavior."
Accurate demand forecasting is crucial for optimizing production planning, inventory management, and resource allocation in industries with long product development cycles, such as agricultural machinery. This research demonstrates a method to achieve higher forecast accuracy, directly impacting cost efficiency and revenue maximization.
What This Means for Your Design
To guess how many tractors will be sold next month, it's better to use a method that looks at past sales AND considers things like how much money farmers can borrow, rather than just looking at past sales alone.
How to use in your project
- 1.This research can be cited to justify the selection of an ARMAX or similar model that incorporates exogenous variables for demand forecasting in your own design project, especially if your product's demand is influenced by economic factors.
Add to My Project
Quick Cite
Paragraph starter
This study highlights the importance of integrating external economic factors into demand forecasting models. The research found that an ARMAX model, which included agricultural credit as an exogenous variable, significantly outperformed a purely time-series based MSARIMA model in predicting monthly farm tractor demand in India over a six-month horizon. This suggests that for products whose demand is sensitive to economic conditions, incorporating relevant external data can lead to more accurate and actionable forecasts, thereby improving resource management and revenue potential.
Source
Indian Journal of Agricultural Research
Forecasting monthly farm tractor demand for India using MSARIMA and ARMAX models
journal · 2019
View sourceQuestions About This Research
- What does the research say about armax model with agricultural credit outperforms msarima for indian tractor demand forecasting?
- When forecasting demand for products influenced by economic conditions, like farm tractors, incorporate relevant external variables (e.g., credit availability) into your forecasting models for improved accuracy. Evidence: Indian Journal of Agricultural Research (2019).
- Why does "ARMAX model with agricultural credit outperforms MSARIMA for Indian tractor demand forecasting" matter for design?
- Accurate demand forecasting is crucial for optimizing production planning, inventory management, and resource allocation in industries with long product development cycles, such as agricultural machinery. This research demonstrates a method to achieve higher forecast accuracy, directly impacting cost efficiency and revenue maximization.
- How can designers apply this research?
- When forecasting demand for products influenced by economic conditions, like farm tractors, incorporate relevant external variables (e.g., credit availability) into your forecasting models for improved accuracy.
- What were the main findings?
- The ARMAX model, which included real agricultural credit as an exogenous variable, demonstrated superior performance in forecasting monthly farm tractor demand over a six-month horizon compared to the MSARIMA model.. Accurate monthly forecasting of farm tractor demand can significantly aid manufacturers in managing raw materials, inventory, and supply chains more effectively.
- What research method was used?
- Quantitative forecasting model comparison.
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2019 journal from Indian Journal of Agricultural Research.
- What should I do differently in my next project?
- When developing production plans for seasonal or economically sensitive products, use forecasting models that can integrate external data such as interest rates, credit availability, or relevant commodity prices.
- What are the limitations?
- The study focused on a specific market (India) and product (farm tractors), and the performance of the models might vary in different contexts or for other product types. The specific choice of exogenous variables in the ARMAX model was limited to agricultural credit.