ERP Reference models and module implementation Based on Supporting the module sequencing decision in the ERP implementation process An application of the ANP method, Petri Hallikainen, Hannu Kivijärvi and Markku Touminen, International Journal of Production Economics, v 119, 2009, pp. 259-270. Duane Truex, 2010, all rights reserved
ERP Implementation in practice Is typically a combination of Organizational business process adjustment and some configuration adjustment of the ERP during configuration and implementation. Determining an optimal fit and reducing mismatches up front, i.e., planning proper IT and Business process alignment, reduces the risk and problems in ERP implementation. Hundreds of decisions, strategic to operational, are required in the process of an ERP deployment. Researchers have sought to create methods and tools to help structure and rationalize the decision making process.
ERP Implementation Options Big-Bang, Mini-Big Bang and Phased Implementation approaches can be characterized by differences in: Physical scope, BPR scope, Technical scope, Module implementation and Resource allocation (Parr and Shanks, 2000)
ERP requires alignment of three domains Information systems, organizational strategy and organizational processes It is a multi-level change management process Strategic, tactical and operational Strategic choices are made concrete at the tactical and operational level Six phases 1. Strategic Identification 2. Environmental analysis 3. Resource analysis 4. Gap analysis 5. Strategy alternatives 6. Strategic choice
Decision Variables in ERP Implementation 1. Single ERP Package vs. multiple packages (one vs. many vendors) 2. Big-bang, vs. Mini-big-bang vs. phased-in 3. How many and which modules to implement 4. The sequence of module implementation 5. Degree of modification to the ERP system 6. Which and what degree of process reengineering to do upfront 7. Whether or not to use an accelerated implementation strategy
Implementation decisions are complicated with multiple feedback structures Methods allowing complex decision factor interactions and multiple feedback loops are needed Hierarchical vs. Network models Hierarchy best if criteria and alternatives are hierarchical and sequential Networks are generalized hierarchies with relaxed assumptions about interaction and multi-criteria dependencies
Halaikainen et al, p. 262
Analytic Network Process (ANP) The ANP, developed by Thomas L. Saaty, is a framework for the analysis of societal, governmental and corporate decisions. ANP allows one to include tangible and intangible factors and criteria that have bearing on making a best decision. ANP allows both interaction and feedback within clusters of elements (inner dependence) and between clusters (outer dependence) capturing the complex effects of societal and human interplay, especially when risk and uncertainty are involved. Analytic Hierarchy Process (AHP) is a special case of the ANP. Both the AHP and the ANP derive ratio scale priorities for elements and clusters of elements by making paired comparisons of elements on a common property or criterion. ANP can be used to allocate resources according to their ratio-scale priorities. ANP models have two parts: 1. the first is a control hierarchy or network of objectives and criteria that control the interactions in the system under study; 2. the second are the many sub-networks of influences among the elements and clusters of the problem, one for each control criterion. (Means creating the eigenspace, and computing the eigenvectors and eigenvalues.)
ANP a Six-Step Process Problem structuring Determine the logical grouping of the elements (clusters) in the problem to be modeled High level Model definition Create clusters Lower level model definition Build nodes (elements) within clusters Model construction Create/specify links between nodes and within clusters Data Collection Make pair wise comparisons for each controlling (boss) element. The system calculates weights/priorities. Comparisons represented as a matrix (eigenvectors) and eigenvalues are computed Solution(ing) Prioritize and adjust weights to establish scenarios/alternative solution sets Requires specific problem/work system/process domain knowledge
Simple ANP model example comparing autos
Implementation Phases strategic to tactical
Module Sequencing Case study example from Hallikainen, Kivijärvi & Touminen 2009 Global Manufacturing firm Grown via modest acquisition Resulted in a Heterogeneous IS with legacy ERP system unable to be readily adopted to new sales and operations planning requirements Needed SCM & CRM capability, not readily available in the legacy ERP Eliminated domains from the get go Financials and data warehouse-based OLAP reporting exempted Collected key business requirements for other domains Early focus on Sales & operations planning Because it was critical to future growth
Phase 11 Module Sequencing 11.1 Problem structuring 11.2 High level model definitions
11.3 Lower level model definition Flesh out the model 11.4 Model construction
11.5 Data Collection
11.6 Solution (ing) 11.7 Sensitivity analysis
Case ANP solution for module sequencing
The point and the value of the technique Rationalizes and structures a complex decision process Provides a mathematical basis for computing networks of smaller decisions Especially interrelated decisions Quantifies subjective choices (judgments) Provides readable (graphical) output that may further discussion
The limitations of the technique Rationalizes and structures a complex decision process Provides a mathematical basis for computing networks of smaller decisions May lead to false sense of the objectivity of the outcome Quantifies subjective choices (judgments)
Eigenvectors and eigenvalues Eigenvalues, eigenvectors and eigenspaces are properties of a matrix and give important information about the matrix. In general, a matrix acts on a vector by changing both its magnitude and its direction. However, a matrix may act on certain vectors by changing only their magnitude, and leaving their direction unchanged (or possibly reversing it). These vectors are the eigenvectors of the matrix. A matrix acts on an eigenvector by multiplying its magnitude by a factor, which is positive if its direction is unchanged and negative if its direction is reversed. This factor is the eigenvalue associated with that eigenvector. An eigenspace is the set of all eigenvectors that have the same eigenvalue, together with the zero vector. The concepts cannot be formally defined without prerequisites, including an understanding of matrices, vectors, and linear transformations. In graphs, like the ANP networks, the principal eigenvector is used to measure the centrality of its vertices. An example is Google's PageRank algorithm. The principal eigenvector of a modified adjacency matrix of the World Wide Web graph gives the page ranks as its components. This vector corresponds to the stationary distribution of the Markov chain the second smallest eigenvector can be used to partition the graph into clusters, via spectral clustering. Source: http://en.wikipedia.org/wiki/eigenvalue,_eigenvector_and_eigenspace, March 14, 2010