The two most basic concepts in business are supply and demand, but as they play out in something as complex as the modern industrial supply chain they’re anything but basic. To wit, about 70% of supply chain businesses have adopted some kind of S&OP (sales and operations planning) workflow in order to more effectively match demand projections and production/operational plans on a quarterly or yearly basis. Though processes like these are a good start, even they aren’t the be-all-end-all. To wit, nearly two thirds of respondents to a recent survey said they wanted to take steps to improve their S&OP processes.

As the Harvard Business Review points out, the 2011 Fukushima disaster had a large and unexpected impact on the global supply chain. While most large supply chain players didn’t expect their sourcing workflows to be impacted (based on the locations of their first tier suppliers), they quickly realized that a tremendous number of second and third tier suppliers were being hit hard by the incident. The result was that planners had to scramble to find new sources for raw materials, or risk shortages, outages, and late deliveries.

By all accounts, advanced analytics are becoming a more important part of the manufacturing sector than ever—and it’s easy to see why. By McKinsey’s estimates, the combined effects of using advanced predictive algorithms to proactively schedule machine downtime and prescriptive analytics to optimize machine yields can add up to a whopping 10% increase in gross earnings. How is this possible? Well, for starters, predictive analytics trained to predict machine breakdowns can help reduce machine downtime by up to 50%, which not only improves your production plant’s throughput, it also extends the lifespan of each machine, saving additional costs down the road.

Think back to the last birthday party you planned. How did it go? As the organizer, you were responsible for everything from getting invitations in the mail through to making sure you had enough drinks and cake for everybody. Then there’s ensuring that everybody got themselves home safely after the festivities (whether it was kids being picked up by parents or adults who may have needed a cab). But what happens next time when 10 of your invitees take it upon themselves to invite 10 additional people, not on the list? Demand for drinks and cake just shot up and you’re not ready for that. Or are you? This is an extremely simplified, yet apt, analogy for the role a production planner plays in keeping the business running smoothly and ensuring that when demand does spike there won’t be any disruptions. 

There’s a coffee shop down the road known for ham & cheese croissants. So you stop by one morning only to discover that they’re out. The barista says they only get 3-4 each day and that they’re generally gone before 8:00 AM. The 2 people behind you sighed and said they were looking for the same thing. This is the best (and smallest scale) analogy I’ve ever seen for poor demand capacity planning. The shop knows there is a demand for the item, and they know the bakery makes more than they order, yet they never have enough to even come close to meeting demand. Leaving many unhappy, and unsatisfied, customers debating the breakfast options down the street. For you, the demand capacity planner, this is the scenario you dread more than anything—being unable to meet your customers’ demands and losing them to competitors as a result. Follow along with the following steps, and you’ll be on your way to avoiding this situation by keeping production and demand evenly matched.

Let's say you’re a bartender in a sleepy neighborhood pub in your city. Your place might reasonably be called a dive bar, and you have a handful of regulars who come and order basically the same drinks week after week. As a result, your ordering process for restocking the bar is extremely simple. With little variation, you expect to go through predictable quantities of gin, tonic, bourbon, and cheap beer every month, and you’ve simply placed a standing order with the local distribution company to restock these things in the same quantity every so often. Life is pretty easy.

Let’s say that you run a pizza delivery joint. As orders come in by phone or through your website, you have one employee who’s in charge of giving delivery estimates and getting the pizzas to the relevant doorsteps, and another who’s in charge of running back and forth between the storeroom and the kitchen to make sure that the chefs have everything they need to actually make the pizzas. If any of the ingredients in the storeroom get too low, that employee calls the relevant suppliers and arranges to receive and store the delivery. One day, you get a bright idea: what if the delivery person and the employee in charge of restocking the storeroom had direct visibility into one another’s processes?

Let’s talk for a second about pattern recognition. The human brain is constantly searching the perceptible world for patterns, sometimes in order to make better decisions (in the case of, say, emerging traffic patterns while driving) and sometimes simply in order to pass the time (in the case of constellations). The thing is, while the search for patterns is an innately human pastime, it’s not something that we as a species are necessarily all that good at. Think about it: how often do we read or hear about people making the same mistakes over and over again without identifying the common factor? How often do we see businesses rolling out the same strategies over and over again without ever noticing the ways in which those strategies could be improved? 

Effective supply chain management is about getting the right goods to the right place, at the right time, in the right condition. This is easier said than done. Why? Because there are a number of intermediate steps separating a finished product from its final destination, including warehousing and shipping, both of which can be complex logistical problems in their own right. The mark of a successful supply chain is its ability to optimize each element in turn before integrating the different pieces into a cohesive, profitable whole. Today, we’re going to focus on the warehousing side of the equation. For many businesses, this represents the thorniest part of the entire value chain—a touchpoint for which effective tracking and management is particularly difficult. Don’t believe us? We’ve got the stats to prove it. 

Real supply chains have curves. Demand and capacity curves, to be specific. The goal of every supply chain and production manager out there is to constantly take whatever steps are necessary to match these two curves as closely as possible. If there’s a spike in demand, that means finding a way to increase capacity; if there’s an overabundance of capacity, that means taking steps to drum up demand. This is, essentially, the goal of demand capacity planning workflows in modern supply chain management. As anyone in the industry can attest, this is much easier said than done.