Introduction to Fractal Manufacturing

In this chapter, we explain the underlying theory of Fractal Manufacturing. We develop the understanding and necessary requirements for the manufacturing-mathematical conditions to implement the Corollary to the Theorem of Balanced Workstation Occupancy (Corollary of Stochastically Minimized BCT Variability or BCT Deviation Function) to match the Theorem of Batch-Flow Requirements (or Flow Imitating Theorem). This to be able to apply the “batch cycle time” BCT Deviation Function in practice. We have put BCT in quotation marks because we intend here batch generically; indeed, we will apply it not to the original batch but to a portion, a fraction, called job made of variable quantum, which is also a kind of batch but much smaller manufacturing batch.

In this chapter, we compare the characteristics of Fractal Manufacturing to other manufacturing systems. The aim of Fractal Manufacturing Systems is to speed up manufacturing lead time (MLT) while observing on-time delivery (OTD). This has to be done by implementing a flow-like single job flow (SJF). As just mentioned before, to maximize throughput speed, queuing of jobs must be avoided, therefore, WIP has to be minimized. Due to the fact that it is not Pull-based but relying mainly on a Push manufacturing principle, we can characterize it as a Push-JIT manufacturing, or more precisely Push-JIS to the opposite of Toyota’s Pull-JIT system. Due to the JIT characteristic, intended as minimized WIP manufacturing, and the application of many Toyotas’ lean implementation techniques, also this Fractal Manufacturing System has a clear lean connotation. In the following, Fractal Manufacturing will be compared to the existing manufacturing systems.

As already stated, the performance of a manufacturing system is not only given by the degree of automation but primarily by the manufacturing mode, i.e. how the equipment is operated (modus operandi). However, the cost of a manufacturing system is heavily influenced primarily by the degree of automation as well as equipment and not by the modus operandi. The overall performance lies in the intelligence of how the manufacturing system is designed to operate, and which controls are implemented to maintain a high throughput, i.e. exit rate (ER). Apart from the rational beauty of a perfect theory, such as JIT systems represent, an additional important aspect which must be considered is the practical aspect of implementation and operating cost.

Usually, commercial manufacturing orders are accepted, scheduled, produced and supplied OTIF (on-time in full). Therefore, the delivery time is often a consequence of the plant workload and less a maintained expected delivery specification, and if, it is seldom 100% observed and has to be renegotiated. To improve OTD (on-time delivery) in the widespread B&Q-based production systems, improved and advanced production planning and scheduling PPS systems (so-called APS) are applied instead of questioning the pushed B&Q manufacturing mode. And exactly this has been the topic of this book.