All supply chain models must be approximations to some degree because making absolutely precise measurements of supply chain entities in the real world is not possible. One can make very precise operating measurements (fractions of millimeters and seconds) at the level of a machine tool making an engine part. And one can still make precise operating measurements at the level of a factory or a warehouse (fractions of meters and minutes). But what level of precision is realistic when measuring operations across an entire supply chain?
Is it possible to measure the precise weight of every product in a supply chain? What if the products being measured are shipping containers loaded with a similar mix of items, but not exactly the same mix of items in every shipping container? Is it possible to measure the precise length of a delivery route? When people talk about the distance between two facilities do they mean the distance from one facility’s loading dock to the other facility’s loading dock, or is it the distance from the front door of one facility to the other? When moving products from one facility in a supply chain to another is it realistic to attempt to measure those operations in seconds and minutes, or is it better to go with hours and days?
Levels of Precision in Supply Chain Models
Google Maps and SCM Globe measure route distances based on how closely the calculated route lines actually follow real roads. The screenshot below shows blue route lines following real roads. They follow closely — but not exactly. Even two actual vehicles traveling on these roads would record slightly different route distances due to things such as swerving within their lanes or the turning radius of the turns they make. Potential distance errors from close, but not exact, route lines can add up to a kilometer or two on a short route, and much more on a longer route, especially if there are lots of twists and turns on the route. So route distances are always approximations.
Truck speeds are also approximations. When a truck departs, will it always travel at exactly 90 kilometers per hour? What if there is a traffic jam or the truck has engine problems? What about the time it takes to drop off or pick up products at facilities on the route? Specifying exact speeds down to the last kilometer per hour is not possible in the real world. So within a supply chain simulation truck speeds represent an estimated average speed over the route that includes the factors above. And for a ship on a journey across the ocean it’s speed too can only be an estimated average that includes many factors from wind and waves to storms and mechanical problems.
Supply chain models can only be defined at levels of detail that correspond to what is possible in the real world. What does it mean for a truck to leave a factory at precisely 20.05 hours after it returned from its last delivery? Is that realistic? Will there be a manager with a stopwatch on the loading dock dispatching trucks at precisely the right moment?
Margins of Error in the Simulations
Small differences in measurements one way or another are not significant because they are within the margin of error of the supply chain model. Opinion polls and research surveys operate with acceptable margins of error defined as 4 – 8 percent with a 95 percent confidence interval. The same is true of supply chain models and simulations. Results shown by simulations are not absolutely precise. Depending on small differences in the data values used to define the four entities, results can vary 4 – 8 percent (and sometimes more). The same is also true of weather forecasts (forecasts are simulations). What if the forecast predicts 2 inches of rain tomorrow afternoon, but it actually rains 2.4 inches that night, does it mean the forecasting model is wrong? In forecasts and simulations this variability in predictions created by small differences in the input data is known as the “Butterfly Effect”.
Regardless of the Butterfly Effect, weather forecasts still show what the weather is most likely to be, and supply chain simulations still show which supply chain designs work best in any given situation. They show whether trucks or railroads or airplanes deliver the lowest costs. They show the best locations for different facilities, and they show how inventory flows through those facilities. They also identify facilities where problems are most likely to occur, and provide data for deciding how to fix these problems.
If you use the simulation results to keep adjusting your supply chain model so as to minimize cost and inventory while always meeting product demand, then your supply chain model, and all other models in a given situation, will converge on one or two best solutions (known as “attractors”). Small differences between the models do not make a significant impact on their overall performance. Small differences in operating costs and inventory levels shown in the simulations are not significant if they fall within the margin of error for the simulation.
Supply chain models producing simulation results which are within the simulation margin of error are equally good. For instance, suppose a simulation based on model A creates a total on-hand inventory level of 350,356, and a simulation based on model B creates a total on-hand level of 350,489. These simulation results are within the simulation margin of error, so both supply chain models are equally good in this area. However one supply chain model may achieve this inventory result at a significantly lower operating cost so that would make it the best model overall.
Realistic Levels of Supply Chain Optimization
Because measurements for operating a machine tool or an individual factory can be done at greater levels of detail and precision, optimizing solutions for machine tools and factories can also be very precise. However, measurements of supply chain operations must necessarily be less precise. So optimizing solutions for supply chains are also less precise. Optimal solutions that exceed what a supply chain model can realistically measure may be mathematically possible, but such solutions produce supply chain designs and operating plans that cannot be implemented in the real world.
For instance, suppose there is a truck in your supply chain that runs on a route from Cincinnati and drops off products at stores in Indianapolis and Chicago. After some experimentation you find the best result comes from a delay between departures for the truck of precisely 13.36 hours, as shown in the screenshot below.
This best result also assumes the truck can run its route to Indianapolis, Chicago and back (calculated at 957.84 km) and then wait precisely 13 hours and 21.6 minutes before departing again. It also assumes the truck will maintain a speed of exactly 90 km/hr over the length of the route. Calculations can be done assuming all these variables will be just as specified. But that does not mean the resulting precision of the calculations is realistic because the assumptions that made them possible are unrealistic.
Small changes to these precise but unrealistic numbers can deliver results that are almost as good. And small changes can create realistic designs that could be implemented in actual supply chains. Having a delay between departure of 12 hours instead of exactly 13.36 hours, and making minor adjustments to the product delivery quantities at the two stores gives results that are somewhat different but are still within the margin of error for the simulation. So they are just as good, and much more realistic.
Strive for Good Supply Chains not Perfect Supply Chains
Unless you are creating a detailed model of a very small part of a supply chain, it is best to avoid the illusion of accuracy that comes from using two decimal points of precision in the creation of your supply chain model. It is mathematically possible to use two decimal points of accuracy and create a supply chain model that produces optimal results over a 30 or 60 day period, but it is still just an abstraction, and not something that could be built or operated in the real world.
Use measurements for your supply chain entities that are realistic. Be conservative in the numbers you use. Assume products are a bit heavier and bigger, make vehicles travel a bit slower, set rent costs a little higher, and have demand levels somewhat greater than expected. When you find a supply chain model that works well with those assumptions, then you know it will work even better if the actual numbers turn out to be more advantageous than those you used.
In a complex world where changes are hard to predict and hard to control, supply chains should be built with an appropriate degree of resiliency. The degree of resiliency is reflected in the conservative nature of the measurements and assumptions you use to build your model. The best supply chains are those that deliver good results even under conditions that are more demanding than what was expected.
Perfect performance in a predictable world is an illusion. Value lies in designing supply chains that deliver good performance in a difficult world.
Remember – As you build more realistic and complex supply chain models be sure to use the modeling techniques presented in “Tips and Techniques for Building Supply Chain Models“