If you’re in or around manufacturing, you’ve likely heard of “statistical process control” or “control charts”. Statistical process control (SPC) is the idea of monitoring a process and using data about past behavior to predict when things have changed. Quality tools like SPC helped revolutionize the manufacturing industry in the 1970s and 1980s, but SPC is still almost unheard of in the logistics world. With the growth of IoT solutions though, SPC could play a big part in supply chain.The Control Chart
The most common SPC tool is the “control chart”, which is a graph of how an important variable changes over time. For example, let’s say I make hammers. The hammer has two pieces, the wooden shaft and the metal head. If the hole in the metal head is too large, the hammer falls apart. If it’s too small, the wooden shaft won’t fit and I'll have wasted parts. In the world of SPC, the diameter of the hole is a critical dimension that I should monitor (or “control”). After the hole is bored in each metal head, the operator measures the diameter of the hole and plots it on a chart, which is typically posted next to the work station.
Image credit: ASQ
The control chart enables manager to identify outliers, or "out of control" data points. These outliers can then be documented, investigated, and the root cause corrected.
Applying SPC to Supply Chains
Supply chains, like manufacturing processes, have critical dimensions, like trip durations, handling rates, temperature environments, and so on. In fact, variation in these variables is the main cause of headache for supply chain professionals: products arriving late, damage rates spiking, and inventories running out. So why haven’t supply chain professionals embraced SPC like their manufacturing peers?
Lack of data.
In the world of manufacturing, you can whip out a pair of calipers and measure the diameter of a hole, or weigh a part, or scan the surface - the operation is happening in your facility and you have control. Supply chain management is all about things happening “out there” - on the roads and oceans of the world, where you don’t have control. And as a result it’s not easy to measure the critical dimensions. All you can measure is the “output” - a late delivery or a damaged part. Without the “input” data, you can’t tell if your process is in control until it’s too late.
Here Comes the Data
The growth of Internet of Things (IoT) devices in supply chain is finally giving supply chain managers access to the data they need to measure and control their processes. A tracking device that travels with the product and reports back on location and condition during transit can enable supply chain managers to build control charts of their processes. Instead of hole diameter, the control would show the duration of a transit leg, or the number of >15G shocks a product has received, or other data that the manager has determined is critical.
This use of data is different from simply receiving an alert and responding to fix the immediate problem. By monitoring earlier-stage variables (like duration on a particular leg of a route, or dwell time at a transit point), managers can identify when a process is “out of control” - maybe the 3PL has changed their driver instructions, or the DC operators have switched from hand trucks to forklifts to move your product around. If this change causes your process to go “out of control”, it’s time to act.
Quality Tools Finally Coming To Supply Chain
When you read about IoT tools, it’s often in the context of “big data” and “AI” and other science-fiction-y topics. These are some powerful tools, but it’s also possible to start with simpler, established tools and have a meaningful impact on your organization’s delivery times and quality levels. Control charts are one of the best-understood tools in the manufacturing tool kit, and it’s time to apply them broadly in the supply chain world.