When people think about global supply chains, Facebook probably isn’t the first company that comes to mind. Facebook, however, operates a complex global supply chain, delivering its products to billions of people and collecting trillions of data points every day. Companies like Facebook were born in an age of explosive technological change and, in many respects, have inherently digital supply chains. However, as the pace of innovation and competition increase, these companies must continue leveraging digitalization to improve their supply chains at every step.
Supply Chain and Forecasting at a Consumer Internet Company
The consumer internet supply chain follows a familiar model: teams plan for new products and features, forecast demand, develop these products and ultimately distribute them across their user bases. Forecasting sits squarely in the center of any consumer internet company’s supply chain, and is critical for three primary reasons:
- Forecasting allows companies to predict demand, which in turn helps to predict server, storage and system capacity needs. Accurately forecasting these variables helps to reduce costs, avoid downtime, and deliver the optimal product experience to users.
- Forecasting provides an “organic baseline”, which can be used to compare against actual results and assess the efficacy of products over time.
- A strong forecast can prevent teams from spending days and weeks investigating fluctuations in demand that could be explained by seasonality or holidays. These investigations often cause bottlenecks in the development supply chain and can waste millions of dollars of resources.
Why Digitalization Matters in Forecasting
Forecasting is typically a costly and highly manual process whereby entire teams of “forecasting experts” utilize historical data to predict future demand. However, this process can be subjective and inefficient, and an opportunity exists for companies to embrace digitalization in this crucial step of their supply chains.
What is Facebook doing about it? Meet Prophet
In February 2017, Facebook introduced “Prophet”, a forecasting tool available in Python and R designed to “make it easier for experts and non-experts to make high quality forecasts that keep up with demand” . Prophet utilizes an additive regression model that analyzes historical data to identify seasonal trends, shifting holidays, and other trend anomalies . What this means in practice is that teams can feed historical data into Prophet and receive a highly accurate organic forecast. Teams can then manually adjust these forecasts for growth limitations or other irregular factors as they see fit.
Compared to the highly manual process of creating granular forecasts “by hand”, Prophet provides companies with a significantly more accurate and efficient process and removes the need for entire teams of “forecasting experts”. This ultimately allows the entire supply chain to function more efficiently, as the company can better predict demand and control costs, and product development teams can more effectively assess the impact of new releases and optimize their efforts going forward.
While Google and Twitter have launched efforts in their own right , neither product approaches the problem as directly. Google’s CausalImpact, focuses more on “estimating the causal effect of a designed intervention on a time series” . Twitter’s AnomolyDetection follows a similar path, focusing on “anomalies in system metrics”  rather than providing a high-quality forecast.
What should Facebook do next?
Prophet is not without limitations. The following opportunities could take Prophet from semi-automated forecasting tool to world class supply chain software:
- Reduce the need for human input: Prophet currently requires users to provide access to databases and input key historical events such as shifting holidays and product launches. Facebook should enable Prophet to automatically pull back-end data and evaluate that data more effectively in order to account for these atypical events. Prophet relies too heavily on the forecast owner’s institutional knowledge, which makes it challenging to deploy at scale.
- Expand down the supply chain: Prophet could be used to inform product development decisions in addition to operational performance. Using predictive analytics, Prophet could identify engagement trends before they happen and inform the product development supply chain.
- Increase accuracy and granularity: As Prophet gets “smarter”, Facebook can utilize machine learning to help create more accurate forecasts with even more granularity. Imagine a world where Prophet could forecast engagement down to the county, provide estimates on data center usage by location, and suggest the most efficient methods by which to deliver data across the network. This could drive cost savings down the supply chain and improve site speed and product experience by providing data more efficiently to users.
- How could a tool like Prophet be applied in industries outside of technology?
- As we rely more on machine learning and AI for back end business functions, how do we maintain control and transparency in the way our companies operate?
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 Sean J.Taylor and Ben Letham, “Prophet: forecasting at scale,” Facebook Research (blog), February 23, 2017, https://research.fb.com/prophet-forecasting-at-scale/, accessed November 2017.
 Kay H. Brodersen and Alain Hauser, “CausalImpact,” Github, Publishing Date Unknown, https://google.github.io/CausalImpact/, accessed November 2017.
 Author Uknown, “AnomalyDetection,” Github, Publishing Date Unknown, https://github.com/twitter/AnomalyDetection, accessed November 2017.