Journal of Systems Science and Systems Engineering 

About: Journal of Systems Science and Systems Engineering is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Supply chain & Service (business). It has an ISSN identifier of 1004-3756. Over the lifetime, 653 publications have been published receiving 9945 citations. The journal is also known as: Systems science and systems engineering (Internet) & JSSSE (Internet).

Decision making — the Analytic Hierarchy and Network Processes (AHP/ANP)

Abstract: This is the first part of an introduction to multicriteria decision making using the Analytic Hierarchy Process (AHP) and its generalization, the Analytic Network Process (ANP). The discussion involves individual and group decisions both with the independence of the criteria from the alternatives as in the AHP and also with dependence and feedback in the entire decision structure as in the ANP. This part explains the Analytic Hierarchy Process, with examples, and presents in some detail the mathematical foundations. An exposition of the Analytic Network Process and its applications will appear in later issues of this journal.

Fundamentals of the analytic network process — Dependence and feedback in decision-making with a single network

Abstract: The Analytic Network Process (ANP) is a multicriteria theory of measurement used to derive relative priority scales of absolute numbers from individual judgments (or from actual measurements normalized to a relative form) that also belong to a fundamental scale of absolute numbers. These judgments represent the relative influence, of one of two elements over the other in a pairwise comparison process on a third element in the system, with respect to an underlying control criterion. Through its supermatrix, whose entries are themselves matrices of column priorities, the ANP synthesizes the outcome of dependence and feedback within and between clusters of elements. The Analytic Hierarchy Process (AHP) with its independence assumptions on upper levels from lower levels and the independence of the elements in a level is a special case of the ANP. The ANP is an essential tool for articulating our understanding of a decision problem. One had to overcome the limitation of linear hierarchic structures and their mathematical consequences. This part on the ANP summarizes and illustrates the basic concepts of the ANP and shows how informed intuitive judgments can lead to real life answers that are matched by actual measurements in the real world (for example, relative dollar values) as illustrated in market share examples that rely on judgments and not on numerical data.

Fundamentals of the analytic network process — multiple networks with benefits, costs, opportunities and risks

Abstract: The general theory of the ANP enables one to deal with the benefits, opportunities, costs, and risks (the BOCR merits) of a decision, by introducing the notion of negative priorities for C and R along with the rating (not comparison) of the top priority alternative synthesized for each of the four merits in terms of strategic criteria to enable one to combine the four B, O, C, and R values of each alternative into a single outcome. Strategic criteria are very basic criteria individuals and groups use to assess whether they should make any of the many decisions they face in their daily operations. They do not depend on any particular decision for their priorities but are assessed in terms of the goals and values of the individual or organization. Synthesis is made with two formulas, one multiplicative and one additive subtractive that can give rise to negative overall priorities. This paper summarizes and illustrates basic complex decisions involving several control criteria under each of the BOCR merits.

A case for service systems engineering

Abstract: A case is made for further developing a branch of systems engineering that focuses on problems and issues which arise in the service sector. We promulgate this special focus not only because of the size and importance of the service sector but also because of the unique opportunities that systems engineering can exploit in the design and joint production and delivery of services. We begin by considering the economic, technological and demographic contexts within which the service sector has flourished; we then address both services, especially emerging services, and systems engineering, followed by a discussion of how to advance the field of service systems engineering, and concluding with several remarks. In particular, a number of service systems engineering methods are identified to enhance the design and production/delivery of services, especially taking advantage of the unique features that characterize services — namely, services, especially emerging services, are information-driven, customer-centric, e-oriented, and productivity-focused.

Big Data: Unleashing information

Abstract: At present, it is projected that about 4 zettabytes (or 10**21 bytes) of digital data are being generated per year by everything from underground physics experiments to retail transactions to security cameras to global positioning systems. In the U. S., major research programs are being funded to deal with big data in all five sectors (i.e., services, manufacturing, construction, agriculture and mining) of the economy. Big Data is a term applied to data sets whose size is beyond the ability of available tools to undertake their acquisition, access, analytics and/or application in a reasonable amount of time. Whereas Tien (2003) forewarned about the data rich, information poor (DRIP) problems that have been pervasive since the advent of large-scale data collections or warehouses, the DRIP conundrum has been somewhat mitigated by the Big Data approach which has unleashed information in a manner that can support informed — yet, not necessarily defensible or valid — decisions or choices. Thus, by somewhat overcoming data quality issues with data quantity, data access restrictions with on-demand cloud computing, causative analysis with correlative data analytics, and model-driven with evidence-driven applications, appropriate actions can be undertaken with the obtained information. New acquisition, access, analytics and application technologies are being developed to further Big Data as it is being employed to help resolve the 14 grand challenges (identified by the National Academy of Engineering in 2008), underpin the 10 breakthrough technologies (compiled by the Massachusetts Institute of Technology in 2013) and support the Third Industrial Revolution of mass customization.