When BMW design a new line of cars, the engineers put their heads together and figure out an optimal performance within limits that are known based on physical laws and restrictions. They do not just shoot from the hip and estimate that the gas consumption will maybe lie between 10 and 20 miles per gallon, or think in silos when it comes to choosing an engine, tires and the gear box. All departments work together to ensure that the most effective chassis design goes with the performance of the engine and provides housing for all control mechanisms.
Why don’t we do the same for our supply chain performance? How does a manager come up with the call to reduce raw materials inventory by 25 percent? How do we know what to do when the new company directive is ‘reduction of cycle times by elimination of waste’? How does the material planner know whether ordering more frequently will result in lower average inventory levels and better availability of the component? And how frequent? And how much?
In SAP we just go with that MRP type PD – the only one we know – a couple of lot size procedures and a static safety stock level to the best of our guesstimate.
Back to car design. When marketing determines what the customer wants, all departments start figuring out what can be done – in an integrated fashion. A chassis is designed, the body fashioned and power requirements are determined. Let’s say the body and chassis design result in a car that will have a mass of 1000 kg and marketing’s research calls for a sports car with an acceleration of 2.5 meters per second squared. The engine department knows that they cannot produce an engine with more than 200 Newton of force.
Instead of starting the procurement of parts and running the lines to start producing, any automobile producer would thoroughly calculate the expected results, considering all integrational and inter-relational aspects of the end product and its production. In many cases formulas are used. In our example we would calculate:
F = ma (Force equals Mass time Acceleration)
200 Nt ≠ (1000 kg) (2.5 m/s2) = 2,500 Nt
Looking at the result, it becomes clear that one has to go back to the drawing board and, getting all parties involved, working on a new design and specifications.
Is this any different in the design of our supply chain? In fact we need to create a vehicle which allows us to accelerate or decelerate the flow of goods between locations, go down a different route when the situation calls for it (change policy from deterministic replenishment to consumption based), and we need to switch gears when our sales turn out to be more predictable, when it wasn’t two months ago (set an MTO product to MTS).
How would you like your driving, if all you know is how to use second gear? You can make it work, but there is a better way to do it with that $30,000 investment in a car. Then why do you only use strategy group 40? PD for MRP type? And a static safety stock?
Every company sets goals for their supply chain. A desired service level to the customer, rate of production, availability of raw material, cycle times, utilization of resources and min/max ranges of investment tied up in inventories. These indicators are frequently measured and there are many initiatives (lean, six sigma, agile and the like) underway to improve on them. However, the problem is that each KPI is put in a silo and when the targets are set individually then, holistically, they state conflicting goals.
The only way to achieve improvements as a whole is to relate the individual indicators to one another.
This is what Factory Physics strives to do with laws, principles and corollaries. As an example, the famed Little’s Law states that Work in Process equals the product of Throughput and Cycle Times.
WIP = TH x CT
Insight from this law would tell us that if we want to reduce inventory in the supply chain (WIP), we have to reduce either cycle time or throughput (not a good idea) or both. We can turn the formula around to, maybe
TH = WIP/CT
which clearly demonstrates that throughput can be held constant for high WIP and long cycle times as well as low WIP and short cycle times. Imagine the possibility. When the output rate of a supply chain can be held the same with long lead times and a lot of capital tied up in stocks, as it is with a short time to deliver and minimal inventory, then the goal is clear: achieve the same sales with reduced cycle times and inventory!
Factory Physics (by Spearman and Hopp) tells us that the difference lies in variability. The stuff which is caused by unreliable suppliers, production lines going down and customers ordering as much as they want, whenever they want it. Variability is part of our daily lives. Last time I went through immigration at Newark airport, on my way back from an international business trip, I stood in the wrong line. When I was guided towards the passport checking booth there were 5 people in front of me and the lady coming behind me was placed in a line right next to me and had 9 people in front of her. Unfortunately to me, she was through customs about 10 minutes before I even got to the baggage line. The reason was variability; sometimes the officer takes fingerprints, sometimes she doesn’t.
Is there a way now to use this kind of information to our advantage? Absolutely. If you understand the relationship between cycle time, throughput and wip and you can predict what happens if one of these levers is changed, then we are making decisions and look for solutions based on a solid foundation. ..
…more in a future blog titled “Factory Physics and it’s possible application in SAP”