Using Publicly Available Data for Decisions in Agricultural Supply - - PowerPoint PPT Presentation

Using Publicly Available Data for Decisions in Agricultural Supply Chain

Authors: Satya Dhavala and Derik Smith Advisor: Dr. Bruce Arntzen Sponsor: Dow AgroSciences MIT SCM ResearchFest May 22-23, 2013

Agenda

• Key Question
• Introduction
• Methodology
• Results
• Conclusion

May 22-23, 2013 MIT SCM ResearchFest 2

Key Question

• How can a manufacturer of agricultural chemicals use the

variety of available data to improve its forecasts and supply chain decisions?

May 22-23, 2013 MIT SCM ResearchFest 3

Data in Agriculture

• Data Types
• Crop projections and actuals
• Yield projections and actuals
• Weather forecasts and reports
• Data Sources
• United States Department of Agriculture (USDA)
• Universities – Land-grant and others
• Meteorological agencies
• Applications of Data
• Macro economic forecasting
• Environmental and sustainability related decisions
• Decisions by growers on various aspects of farming

MIT SCM ResearchFest 4 May 22-23, 2013

• Ag. Chemical Distribution Chain
• Finite manufacturing capacity
• Long production and distribution lead times
• Production is based on forecasts and occurs month in

advance of a short, uncertain sales season

• Many factors influence demand

MIT SCM ResearchFest 5 May 22-23, 2013

AgChem

MIT SCM ResearchFest 6

• A crop chemical predominantly used on corn
• Two possible application windows – fall and

spring

• Choice of AgChem for this study
• Sales have increased significantly in recent years

May 22-23, 2013

AgChem - Application Windows

MIT SCM ResearchFest 7

Crop cycle Crop cycle Sales Season Fall Spring

• Sales occur ahead of the growing season

May 22-23, 2013

Methodology

• Identify and analyze the factors
• Structure the problem
• Gather data for each factor
• Develop models (regression) and test significance of each

factor

• Eliminate insignificant factors and fine-tune significant factors

for better accuracy

MIT SCM ResearchFest 8 May 22-23, 2013

Factors Analyzed

MIT SCM ResearchFest 9

Time Dimension Annual

• Corn price
• Fertilizer usage
• Corn acres planted
• Corn acres harvested
• Average yield
• Fertilizer price
• AgChem price
• Corn acres planted
• Corn acres harvested
• Average yield
• Increase in yield
• Number of retailers
• Bulk storage capacity

No relevant variables Weekly No relevant variables No relevant variables

• Temperature
• Precipitation
• Sales to date

National County City Geographical Dimension

May 22-23, 2013

Structuring the Problem

• Some of the factors are leading indicators and some are

lagging

• Factors differ by the way they influence demand and their

granularity

• Divide the problem
• Annual, nation-wide demand
• Annual, county-level demand
• Short-term, city-level demand

MIT SCM ResearchFest 10 May 22-23, 2013

Results: Annual, Nation-wide

MIT SCM ResearchFest 11 May 22-23, 2013

Results: Annual, county-level demand

• Significant factors for annual, county-level demand
• Corn acres harvested
• Number of local retailers
• AgChem sales in the first few weeks of the season

MIT SCM ResearchFest 12 May 22-23, 2013

Additional Findings

MIT SCM ResearchFest 13

2007 – 2008 2008-2009 2009 - 2010 2010-2011

May 22-23, 2013

Results: Short-term, city-level demand

• Harvest completion date acts as a trigger point for fall sales
• Average temperature is the only significant factor

MIT SCM ResearchFest 14

Sales volume by week

May 22-23, 2013

Harvest completion

Conclusion

• Implications for DAS
• Better understand the external factors
• Fine-tune existing forecasting models
• Position inventory more effectively by tracking trends such as harvest

completion

• Limitations and scope for further research
• Retailer city was used instead of application city, for short-term

model

• Temperature and precipitation were used based on the nearest

weather station

• Period we analyzed experienced only an upward trend in volumes

MIT SCM ResearchFest 15 May 22-23, 2013

MIT SCM ResearchFest 16 May 22-23, 2013