**1. Introduction**

Probabilistic foundations of one of the modern directions in the field of after sale product service—integrated logistic support (ILS) are systematically treated. Stochastic continuous acquisition logic support (CALS) is the basis of ILS functioning in the presence of noises and stochastic factors in organizational-technicaleconomic systems (OTES). While spelling this chapter we firstly tried to explain reader the new approaches for creation informational technologies (IT) of modeling, optimal data processing in stochastic systems (StS) for high-quality manufacturing products (MP). Secondly, we consider optimization problems for complex of enterprises being part of virtual enterprise (VE).<sup>1</sup> In broad sense VE

<sup>1</sup> Virtual enterprise is such an enterprise that consolidates geographically separated economical subjects and interact in the process mutual production using chiefly electronical communicational means.

presents geographically distributed OTES whose consolidated budget at fixed time period is divided between two basic structure types of VE. First structure is responsible for MP creation and production with given functional and exploitational-technical qualities.<sup>2</sup> Second structure is responsible for professional quality and staff life quality (professional skills, medical services, etc.). In this case OTES criteria for complex optimal OTES control<sup>3</sup> are defined by the socialtechnical-economic efficiency indicators. Such indicators depends on the resources costs at required quality of basic processes in both structures during life cycle (LC).

Besides standard ILS problems solving by such OTES systems modern IT provides deep OTES integration in general structures of local and global markets of finances, goods and services (FGS).

New approaches for OTES control are based on the probabilistic methodology for analytical modeling of stochastic processes coming from stochastic nature of internal and external noises. Special attention is paid to stochastic noises generated by injurious OTES-NS (noise supplier).

In the modern ILS models and strategies ERP (enterprise resource planning) and MRP2 (manufacturing resource planning) only statically deterministic mathematics is used for solving planning problems. Unlike the existing methodology the suggested stochastic methodology firstly takes into account stochastic optimal planning processes dynamics and secondly performs current operative control using modern methods of stochastic analysis, modeling and estimation (filtering, for casting, identification, etc.) and control methods and technologies [1–4]. It gives opportunity to raise the level and the quality of OTES control by means of informational-analytical tools (IAT). There tools are being global control VE net based on CALS principles and technologies. Stochastic imitational models and complex imitational models give the opportunity to estimate the accuracy of analytical models and solve problems of optimal data processing and control in high dimensional and fast OTES-CALS.

complex through cost life cycle (CLC) estimation according to modern

*Integrated modeling, estimation and control technologies for cost life cycle control in after sale support (ASS)*

*Probabilistic Modeling, Estimation and Control for CALS Organization-Technical-Economic…*

• computational algorithms for various LC stages are simplified and do not permit forecasting with necessary accuracy and perform optimal control at

So ILS standards do not provide the whole realization of advantages for modern and perspective IT including staff structure in the field of stochastic modeling and control of two interconnected spheres: techno-sphere (technics and technologies) and social ones. These systems form the new the system class: OTES-CALS systems. Such systems destined for the production and realization of various services including staff structure, engineering and other categorical works providing exploitation, after sales MP support and repair, staff, medical, economical and financial support of all processes. New developed approach is based on the new stochastic modeling and control IT (**Figure 1**). These technologies are based on generalized social-technical-economic efficiency indicators for LC processes in

Research and control object in OTES are processes total LC of homogeneous sets

According to [13, 14] we introduce composite elements (CE) as OTES with the following elements: (1) basic technical means (TM) and TM being part of serving equipment; (2) staff. For creation unique stochastic model of interoperable OTES processes it necessary to define the data set forecasting CLC indicators at given period of exploitation. This set of indicators includes: (1) coefficient of CE performance at planning for given period of exploitation; (2) level of professional and

of MP and resources. Special attention is paid to staff as object of professional

conditions of internal and external noises and stochastic factors.

decision support algorithms;

*DOI: http://dx.doi.org/10.5772/intechopen.88025*

**Figure 1.**

*systems.*

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comparison with usual CALS standards.

training, improvement and medical service.

**3. Probabilistic modeling and analysis**

**3.1 Basic elements of OTES stochastic modeling**

Stochastic CALS methodology was firstly developed in [5] for modeling and analysis. Let us consider the development the stochastic estimation and control problems. We hope that these approaches will be useful for probabilistic systems engineering [6].
