Supply and demand are the first two concepts that most people learn about with regard to economics—and they’re also two of the most crucial elements of any manufacturing supply chain. In order to effectively meet customer demand, you need to ensure that you have enough supply on hand; and in order to profit by that demand, you have to make sure that your supply doesn’t wildly exceed your needs. As with so many things in manufacturing, this is easier said than done.
Sales and operations execution, or S&OE, is a little bit like flying an airplane. In the modern era, you already have a host of processes that have been digitized and automated, including many of the actions that pilots themselves used to be solely responsible for. Your point of departure and destination, as well as the route that you’ll take from one to the other, is already fixed—all of which means that as a pilot your job is mostly to monitor incoming information and make slight adjustments as needed, even if those adjustments are just a fairly rote response to alerts being sent to you by your instruments.
Ah, the old dilemma: make to order vs. make to stock. The debate has been raging in the world of manufacturing for many years. On the one hand, making to stock (i.e. the process of creating products in anticipation of demand that hasn’t yet materialized) involves a lot of guesswork, with potentially costly results: if demand for a particular product doesn’t meet forecasted levels, you could find yourself in possession of large quantities of unsold stock, which you might have to sell at a loss in order to free up costly warehouse space. Making to order (in which you start your production process only once an order has been placed), on the other hand, presents its own potential pitfalls: you risk meeting demand comparatively slowly, and the relatively lean nature of the typical make-to-order supply chain makes it more susceptible to risk in some ways.
Most businesses in the manufacturing sphere have some form of sales and operations planning (S&OP) workflow that covers the monthly or quarterly timetable that’s often left unplanned in longer term business goals. In the Industry 4.0 era, a newer, even more granular level of planning has emerged to supplement S&OP by covering the daily, weekly, and monthly supply chain activities that might otherwise go without any cohesive planning structure. The name of this new level of planning? Sales and operations execution, or S&OE.
Baseball may not be the most popular sport in many parts of the world, but when one considers all of the analytical and statistical breakthroughs the game has made in the past two decades, it really deserves to be a favorite of supply chain managers in the Industry 4.0 era. Since the dawn of the “Moneyball” era, scouts, commentators, and prognosticators have developed new, increasingly complex ways of measuring past performance and forecasting future outcomes. Because everything that happens in the game of baseball, from a stolen base to an outfielder dropping the ball, can be represented numerically, entire seasons can be simulated in granular detail, and insights can be gained from those simulations. By integrating these systems with real-time game data, we can now make an ongoing estimate of the win probability of each team in the middle of each contest.
For many decades, baseball statistics barely changed. People counted hits, batting average (the number of hits per at bats), and runs batted in and measured the value of their players based on those statistics. In the late twentieth century that all changed. With the advent of Sabermetrics and what would eventually be known as Moneyball, statisticians, baseball executives, scouts, and even casual fans entered a period of statistical renaissance. Old-fashioned stats took a backseat to complex new creations like OPS (on-base percentage plus slugging percentage) and Wins Above Replacement (a complex, GDP-like formula meant to distill value into a single statistic).
Imagine you’re packing up supplies for a backpacking trip across Tuscany. You’re limited by how much you can comfortably carry for many miles of walking, and you have to decide which items and in what quantities you’ll need in order to make it the entire length of the trail. You start with clothing and a first aid kit—but how much food do you bring? You want to pack light, and you think that you’ll reach the next town (where more food could potentially be acquired) within a day or two, but if you’re forced to slow down for some reason you don’t want to run out of things to eat. Bringing more food, however, means leaving behind one or two of the books you planned to read in your more leisurely moments.
In the past five to 10 years, real-time information has become a key value-added proposition for bolstering efficiency and decreasing waste in modern, digital supply chains. Businesses have used it to power more agile, responsive processes within their own value streams, creating environments that are primed for improved data-quality and easier analytics integration. The question remains, however, is this technology being utilized to its maximum effect, or are there still use-cases for real-time information that most businesses are failing to fully leverage? The answer is resoundingly the latter, as evidenced by these four surprising uses for real-time supply chain data.
Think about some of the biggest supply chain risks for a moment: unexpected weather events or natural disasters; price fluctuation for oil or other transport factors; inaccurate forecasts—all things that require an immediate response in order to prevent complete supply chain shutdowns. Now, think about most sales & operations planning (S&OP) workflows: focused on mid-term, quarterly or yearly cycles; designed to support longer-term goals like new product launches—quite simply, the opposite of immediate. Of course, S&OP is crucial to shaping a business’ mid-term strategy, but when disruptions hit there’s rarely time to wait for the next quarterly planning meeting in order to respond. As a result, without a secondary workflow to cover the weekly or monthly planning timeframe, the inherent risks in longer-term planning processes are significantly amplified.
Let’s say you and a coworker are attempting to find areas of waste in your supply chain. You have a large conference table on which you’ve laid a file that contains all of the transport plans utilized by the company for the past few years. When your coworker hypothesizes that a different grouping of goods would improve fuel efficiency, you need new documents with additional information, meaning that you have to leave the conference room and descend to the basement level where the files are kept. By the time you’ve returned, a new idea has occurred to your coworker, and you have to make a new trip to wherever your files are stored in order to retrieve the necessary information. The result of all this walking to and from the files? Some good cardio, but no plan to speak of.