Production logistics:

The term is used for describing logistic processes within an industry. The purpose of production logistics is to ensure that each machine and workstation is being fed with the right product in the right quantity and quality at the right point in time.

Logistic regression:

logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several predictor variables that may be either numerical or categories. Logistic regression is used extensively in the medical and social sciences as well as marketing applications such as prediction of a customer's propensity to purchase a product or cease a subscription.

For example, the probability that a person has a heart attack within a specified time period might be predicted from knowledge of the person's age, sex and body mass index.

Extensions:

Extensions of the model cope with multi-category dependent variables and ordinal dependent variables, such as polynomial regression. Multi-class classification by logistic regression is known as multinomial logit modeling. An extension of the logistic model to sets of interdependent variables is the conditional random field. 

Logistics map:

The logistic map is a polynomial mapping, often cited as an archetypal example of how complex, chaotic behavior can arise from very simple non-linear dynamical equations. The map was popularized in a seminal 1976 paper by the biologist Robert May, in part as a discrete-time demographic model analogous to the logistic equation first created by Pierre Francois Verhulst. Mathematically, the logistic map is written

xn is a number between zero and one, and represents the population at year n, and hence x0 represents the initial population (at year 0)

r is a positive number, and represents a combined rate for reproduction and starvation.