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.
Topics: Demand Capacity Planning
Let’s take a second to compare two hypothetical World Cup forecasts. Both forecasts are trying to determine who the likely winner of the contest will be, but their methods differ fairly radically. The first forecast starts out with team rosters, facts and figures, and all manner of statistics pertaining to the various players and teams. Based on those facts and figures, a statistician begins to derive and weight a set of probable outcomes. Those outcomes are sent on to a human prognosticator (an expert in the sport, perhaps a former player or coach or a newspaper commentator) who uses his experience and judgment to tweak the probabilities handed down to him by the statistician. The stats think that a particular player on the French team will age poorly, but the prognosticator thinks otherwise, and changes the predictions accordingly. After this first round of edits, the predictions are passed on to the next editor, who brings her own experience to bear, changing the projected outcomes yet again.
Let’s say you’re a freelance writer. You’re trying to grow out your list of clients and get some more work, but you don’t want to commit to doing more work than you can actually handle. So, you sit down to analyze your previous work habits. How many words did you write per hour on average? Did the number of words vary by project type or industry? How many hours are you willing to work each week? How much overtime can you work before you become burned out? With these considerations, you’re able to figure out roughly how much demand you can meet. In this way, you optimize your earning potential and utilize your work hours in the most efficient way.
Topics: Demand Capacity Planning
Let’s say you’re moving into a new home. After a long day of loading boxes and furniture into your car and driving them to your new place of residence you’ve finally transported everything into your new house and you’re almost ready to start unpacking and get the place settled. Before you get down to the work of opening up all of your packed boxes, you realize that you haven’t eaten all day, and should probably whip something up before you do any more manual labor. It should be pretty easy to find your kitchen supplies and snack foods, because you made a list of what was in each box before you moved them. One problem: you don’t know which box the list is in.
As the worlds of manufacturing and retail evolve to meet the unique challenges of the 21st century, logistics (including shipping and freight forwarding) will have to evolve with them. This will likely mean not just a push towards increased digitization, but a continued reimagining of how logistics operates and provides value. Though many of these changes will no doubt seem daunting, they will also present new opportunities for businesses to grow and gain competitive advantages. Here are a few of the most interesting emerging trends to watch out for:
Imagine you’re at the grocery buying cooking supplies for the coming week. You see that tomatoes are on sale if you buy them in quantities of ten. Hoping to make use of the savings, you do some quick calculations in your head: the ripe tomatoes will remain fresh for about a week; you cook roughly one meal a day; your favorite dish requires two tomatoes. You determine that you could easily utilize ten tomatoes before they go bad, but you would have to commit to making the same dish five nights out of seven, and you might not be in the mood for it later in the week.