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.
The potential in this area is substantial, including the opportunity to:
- Improve estimates of the future using historical information.
- Continually identify the best “collaboration combination” between sales reps, planners, and computational (statistical) methods by time period.
- Move from a point estimate of demand to capturing the likelihood in a usable fashion.
- Identify latent demand opportunities.
As with cardiac monitoring, the critical success factor is a computational model of the demand supply network (DSN). To be really useful, we need to improve our capabilities in a number of areas:
- Current DSN models work at a simplified level, so richer models are needed. Good planners have work-a-rounds; others are chasing or buried.
- This limitation is especially true for capacity, where organizations sometimes have the illusion rather than the reality of success.
- The ability to search for a quality answer quickly.
- The capacity to explain the solution to potential users. (This is a huge opportunity.)
This is often the poor step sister, caught between irrational discussions between finance, marketing, and production much of current practice has not changed in 20 years. Emerging analytical methods involving empirical distributions, better risk models, and multi-echelon methods will generate improved performance.
Creation of the plan is the start of the process, but the real work is the analysis to avoid over committing while not missing opportunities. The level of analytics support is typically just “dashboards,” a huge opportunity for analytics. Simple example:
- Part AAA has demand between 50 and 100 per day for 90 days. For the future, the demand forecast used for the supply plan is 80.
- This part exclusively uses tool A00 which has a stated average daily capacity of XXX; this average incorporates planned maintenance and estimates of unplanned repair time.
- If XXX=200, then there is sufficient surplus capacity to support demand, therefore the planner might look for other demand for part AAA
- If XXX=120 and tool A00 has a history of long unplanned repairs, the planner may decide to maintain extra safety stock
- If XXX=90, the supply will show as “capacity feasible”, there is a risk of demand surge that outstrips capacity
- If XXX= 70, the supply plan will call for a build ahead (creating inventory), but there is a risk demand will drop creating inventory without a home
What does this mean for the S&OP executives?
You need a balanced portfolio of methods to improve responsiveness. Better knowledge of demand generated by predictive analytics is of limited value if you leave inventory unused or you cannot identify where capacity adjustments need to happen. Optimizing capacity allocation to a poor demand is just a nice piece of mathematics.
What is the key ingredient to growing this portfolio? In short, we need agents of change. Stay tuned and we'll dig into that in a future blog!