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

Flesh out the model 11.3 Lower level model definition

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