Used properly, analytics, predictive analytics, data science, and machine learning can get beyond being just buzzwords, and be leveraged as critical tools in sales and operations planning (S&OP) in the supply chain.
S&OP is a continuous business process that enables firms of all types, from electronics OEMs hospitals, and from auto makers to chemical firms, to respond to emerging situations in an intelligent manner maintaining a balance between demand and supply. The intelligence is achieved by collaboration and alignment throughout the organization supported by software where there are five major activities: demand management, supply planning, inventory management, available-to-promise, and ongoing analysis.
What is analytics?
Analytics is a portfolio of loosely coupled methods for quantitative analysis that historically had "separate" disciplines or neighborhoods with common ancestors and occasional visits between groups. These include "older" areas such as statistics, optimization, simulation, differential equations, and dashboards and "new" areas such as machine learning and data science. The term emerged over the last 10 years driven by:
- A recognized need to apply quantitative analysis without borders to a myriad of challenges from credit card fraud detection, to imbedded antennas in cell phones, to spotting likely buyers based on click speed, to alert conditions for "heart attacks," to targeting messages for voter groups or buyers of ice cream, to suggesting matches for online dating sites.
- The reality that computational methods had achieved equal standing with traditional mathematical equations.
- The growing availability of data both in quantity and relevant time frequency.
The term analytics is new; the application of quantitative analysis without borders has been a mainstay of agents of change since the 1970s.
Data and fast computation are the starting points for many applications. For example, credit card fraud detection requires the real-time capture and storage of transactions and the ability to analyze and classify the pattern of activities. A critical element in heart monitoring is "capture and store" pressure via sensors placed inside the body. The data by itself is insufficient to drive an intelligent response. In credit cards, the role of the data scientist is to find the story in the data (see Dr. Perlich's article What's a Data Scientist). For heart monitoring, data science alone is insufficient. Knowledge of the cardiac system and a computational model are critical to success.
Let's use credit card fraud to illustrate machine learning. The credit card company wants to identify purchase transactions that are fraudulent well before the customer receives a monthly bill. It pulls data sets with transaction histories with and without fraudulent purchases and applies machine learning to identify patterns associated with fraud. One pattern is a succession of short purchases in a location away from home. I came across this on a recent trip to LA, when I purchased gas and then coffee. The coffee purchase was rejected. The "machine learning" had failed to notice I had purchased a plane ticket to LA, flew to LA, and purchased groceries upon arrival. I had a learned response – call the 800 number and have the agent post a manual override.
How can analytics help S&OP?
In the list of topics above and most articles, there is no mention of S&OP! In fact, best of breed applications to support S&OP have been analytics leaders for years. However, implementation is sporadic (a topic for a different time). Let's take a look at the various areas of S&OP—and some potential use cases.