He never got the math

Keith Oliver's Supply Chain Management definition[edit]

Oliver defined in 1982 the Supply Chain concept as follows: “Supply chain management (SCM) is the process of planning, implementing, and controlling the operations of the supply chain with the purpose to satisfy customer requirements as efficiently as possible. Supply chain management spans all movement and storage of raw materials, work-in-process inventory, and finished goods from point-of-origin to point-of-consumption”.^[16] Since then, almost all Supply Chain Book authors have developed their own definitions. Some of them are subtle variations and others add more detail, but most of them remain close to Oliver's original definition..

Faster than the definition.

When the probability of an inventory shortage is equal at all stages of production.  That is when the arrivals look maximally Gaussian and can be treated using congestion theory ( Gaussian noise for the  engineers). At that point the distribution network looks like independent arrivals. Each node then deals with discrete Poisson inventory statistics. They are optimally congested, relatively independent and for a Markov chain, they are one node on the tree.

There is a simple trick, the Markov condition tells you to keep no more than two trucks in the incoming queue.  Walmart is the master at this.

Government is a supply chain, and bot do we foul it up in the USA. We have jamming all through the system, whole crowds waiting in line, clamoring, rioting. Very few economists seems to understand the problem. Yet almost everywhere other sciences have been well clued on this for a long time.

It is the same theory as optimal portfolio management, both considered an independent arrival problem, except portfolios generally contain too many categories, (Markov N-Tuple). In theory, the entire investment business can be condensed into one S/L as long as corporations all used standard accounting. If so, then the sandbox automated S/L tech could replace the stock market.

If you think of Shannon information theory he is talking about the three entries and exit to the store. Clerks, customers, and supplier; a Markov triple. Customers are Gaussian noise, the clerks manage the sample rate of suppliers.  They all share finite bandwidth in a self sampled system.   The Walmart store is mostly about supply, one of the Markov triple.  In general stores are tripples.

When he says the -iLog(i) are all within one, he means you can count up as many digits as you have various i.  The more  The optimal congestion condition centers them all as maximally binomial with a fair coin.  The lengtheir you supplier,  the greater the precision of i, and this is the link to number theory.

There is a limit to how precise you can make i and still balance the three  binomial arrival distributions. That limit is there because Shannon is operating on the (1,y,z) chain, one of the relative primes is unity.  The limit is due simply because nothing is perfectly Gaussian, his assumption. It implies we precompute the generator and Gaussian noise is perfectly rounded independent arrival.  In self sampled systems we have to distribute the map, otherwise we cannot jump Markov nodes. Finiteness connects to number theory via the Horwitz theory. Self sampled systems have an Avogadro, and when they hit that limit they bounce into a 4 -Tuple. Getting you another version of relative prime theory.