{"id":24734,"date":"2017-11-15T18:21:35","date_gmt":"2017-11-15T23:21:35","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/monsanto-predicting-weather-crazy-or-crazily-smart\/"},"modified":"2017-11-15T18:21:35","modified_gmt":"2017-11-15T23:21:35","slug":"monsanto-predicting-weather-crazy-or-crazily-smart","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/monsanto-predicting-weather-crazy-or-crazily-smart\/","title":{"rendered":"Monsanto Predicting Weather: Crazy or Crazily Smart?"},"content":{"rendered":"

How is Monsanto impacted? <\/b><\/strong><\/p>\n

Monsanto is a Missouri-based agricultural biotechnology company that produces genetically engineered seeds. It has a 18 month supply chain cycle, which means that in Q1 of Year 1, the company needs to plan for the customer seed demand in Q3 of Year 2 (taking into account the time to grow and harvest the seeds for selling). Because of this long supply chain process, demand forecasting is critical to its planning and risk management processes. [1]<\/p>\n

Weather is one of the biggest drivers of demand forecast accuracy, in addition to acres and seeding rate. More than 70% of a farmer’s decisions are influenced by weather. [2] For example, if the growing season encounters drought, it’d in Monsanto’s interest to drastically increase seed planting in anticipation of supply shortages. Climate change in the recent years has severely impacted the company’s ability to predict demand accurately, which translates into increases in cost. With the growing competitive pressure in the agricultural inputs business, customers are becoming less willing to accept costs that result from inadequate supply chain management. [1] As such, Monsanto has taken active steps to respond to both the rapidly changing market conditions and the growing supply chain risk that comes along with climate change.<\/p>\n

What is Monsanto doing? <\/b><\/strong><\/p>\n

In 2013, Monsanto spent $1Billion to acquire Climate Corporation, a company that uses machine learning to predict weather and other elements driving demand of agribusiness. Since its launch in 2006, Climate Corp built a data-driven platform that combines weather and yield data to predict the impact of weather on crop yield predictions. When Monsanto first looked into this acquisition, skeptics expressed concerns about the viability of weather prediction. However, Monsanto saw a future wracked by climate change and took an early action to manage future supply chain risks. In the recent years, increasing number of agricultural companies are taking similar steps to better leverage big data (including weather prediction technology) to manage supply chain risks affected by climate change. [3]<\/p>\n

The Climate Corp acquisition marks one of Monsanto’s steps into big data analytics. To improve its supply chain management in the medium term, Monsanto continued to introduce a series of big data tools (e.g., SmartForecasts Enterprise) to improve demand forecasting and inventory optimization. [4]<\/p>\n

What else should Monsanto consider? <\/b><\/strong><\/p>\n

While the acquisition of data analytics tools is a smart move, tools alone are insufficient. Monsanto needs to adjust its culture and organizational structure to ensure its managements can appropriately<\/i><\/em> use these tools <\/i><\/em>in decision making. In fact, cultural inertia can be one of the biggest roadblocks to operations improvement via big data. [5]<\/p>\n

First, executives who are used to relying on business judgement in decision making may embrace the tools on the surface<\/i><\/em> but stubbornly resist using big data in real decision making<\/i><\/em>. Second, management and working teams may have this belief that predictive platforms needs to be 100% or they cannot be trusted. As we saw in IBM Watson case, the standard for errors is a lot higher for machines than humans. Lastly, if predictive analytics tools (e.g., Climate Corp, SmartForecasts) were to be used to improve supply chain, organizational structure needs to be considered to integrate analytics and traditional supply chain departments. In sum, Monsanto cannot neglect the amount of change management required to ensure that its acquired predictive tools are truly used and embraced by its management \/ working teams. [6]<\/p>\n

What are the ethics of Monsanto\u2019s big data usage? <\/b><\/strong><\/p>\n

Monsanto’s move into big data undoubtedly improves its supply chain efficiency by allowing the company to better predict and track its customers’ demands. However, it also creates a power asymmetry between this large agribusiness and its customers (the farmers). Monsanto’s ability to amass huge quantities of data to generate insights creates informational advantage over farmers and allows it to gain more negotiation power over them. [7] For example, not only does Monsanto plan to use the weather predictive tool for internal supply chain management, it also plans to commercialize the technology by selling to farmers to enable them to grow crops in changing climate. This then allows Monsanto to collect a massive amount of data from its farmers. [8]<\/p>\n

How can we ensure that data is used equitably (e.g., not as a way for Monsanto to squeeze profits out of farmers)? What are the ethics of big data commercialization by large conglomerates? If Monsanto were to sell the weather predictive technology to its farmers, should it exploit farmers’ maximum willingness to pay? Or leave it free and see it as a way to fulfill corporate social responsibility? These ethics-related questions apply to many large conglomerates moving into big data but they are particularly interesting for Monsanto because over the years it has already weathered a large amount of negative publicity for its GMO products.<\/p>\n

 <\/p>\n

Word Count: 798<\/p>\n

[1] Stephen C. Graves et al, “Optimizing Monsanto’s Supply Chain Under Uncertain Demand<\/em>“, Semantic Scholar (1997) https:\/\/pdfs.semanticscholar.org\/9c35\/5c0f080f60a361c477e5aa35b63cc1fda455.pdf<\/p>\n

[2] Eugene S. Takle et al, “Climate Forecasts for Corn Producer Decision Making<\/em>“, U.S. Department of Agriculture: Agricultural Research Service (2014) http:\/\/digitalcommons.unl.edu\/cgi\/viewcontent.cgi?article=2416&context=usdaarsfacpub<\/p>\n

[3] Megan Stubbs, “Big Data in U.S. Agriculture<\/em>“, Congressional Research Service (2016)https:\/\/fas.org\/sgp\/crs\/misc\/R44331.pdf<\/p>\n

[4] John Comando, “Monsanto Selects Smart Software’s SmartForecasts to Improve Forecasting Process<\/em>“, BusinessWire (2004) http:\/\/www.businesswire.com\/news\/home\/20040225005095\/en\/Monsanto-Selects-Smart-Softwares-SmartForecasts-Improve-Forecasting<\/p>\n

[5] Murli Buluswar et al, “How companies are using big data and analytics<\/em>“, McKinsey & Co (2016) https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/how-companies-are-using-big-data-and-analytics<\/p>\n

[6] Randy Bean, “Why Cultural Change is Necessary for Big Data Adoption<\/em>“, Forbes (2016) https:\/\/www.forbes.com\/sites\/ciocentral\/2016\/11\/08\/another-side-of-big-data-big-data-for-social-good-2\/#690032c86628<\/p>\n

[7] Isabelle M. Carbonnell, “The Ethics of Big Data in Big Agriculture<\/em>“, Journal on Internet Regulation, Policy Review (2016) https:\/\/policyreview.info\/articles\/analysis\/ethics-big-data-big-agriculture<\/p>\n

[8] Dan Mitchell, “Why Monsanto Spent $1 Billion on Climate Data<\/em>“, Modern Farmer (2013) https:\/\/modernfarmer.com\/2013\/10\/monsanto-spent-1-billion-climate-data\/<\/p>\n","protected":false},"excerpt":{"rendered":"

Farming in face of climate change? 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