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Samenvatting Data-driven Design Approaches in SCM (325232)
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Lecture Summary Data-driven Design Approachesin Supply Chain Management 2022-2023
Table of contents
Lecture Summary Data-driven Design Approaches in Supply Chain Management 2022-2023...............1
Lecture 1: From a mess to a model........................................................................................................2
Lecture 2: Conceptual model development...........................................................................................6
Lecture 3: Quantitative model development..........................................................................................9
Lecture 4: Quantitative model development (2)..................................................................................16
Lecture 5: Model quality & data analysis..............................................................................................20
Lecture 6: Policy analysis......................................................................................................................23
Lecture 7: Communication & implementation.....................................................................................26
1
,Lecture 1: From a mess to a model
Audax
Modelling results
Arrival patterns: night shift workers arrived at 10 pm, most newspapers after 12 pm
Sorting methods: combination of field experiment and simulation analysis
Packaging bottleneck: from 1 extra machine needed to 1 machine less needed costs 100+k
Workforce reductions: 25% potential
Business results
Design of and smooth transfer to new layout
Implementation of new sorting methods
Removal of packaging bottleneck
Continued reduction of working hours
Accomplishment of timely early departures
Substantial financial savings
Organizational results
Belief in findings and commitment for implementation
Appreciation of problem-solving approach
Higher esteem of the operations manager
Higher quality of systems thinking
Organizational learning
Main finding from a modelling perspective
The modelling and the consultancy approach cannot be separated
A model-building project can be both a threat and an opportunity for a manager
Switching between the facilitator and expert roles is beneficial, but should not be absolute
Modelling is complex, models should be simple (Jacob Wijngaard, 1990)
Many insights are gained during the data analysis phase
The availability of company data is a crucial factor for project success
Perceived model validity depends on both the opinion of the client AND on data
2
, The implementation environment for the simulation is not important
After the modelling project, the simulation model soon becomes obsolete and unnecessary
The engaged scholarship research diamond by Andy van de Ven
1. Problem formulation: situate, ground, and diagnose the research problem by determining
who, what, where, when, why, and how the problem exists up close and from afar.
Answering these journalist's questions requires meeting and talking with people who experience
and know the problem, as well as reviewing the literature on the prevalence and boundary
conditions of the problem.
2. Theory building: develop plausible alternative theories (or propositions) that address the
problem as it exists in its particular context.
Developing these alternative theories requires conversations with knowledgeable experts from
the relevant disciplines and functions that have addressed the problem, as well as a review of
relevant literature.
3. Research design: gather empirical evidence to compare the plausible alternative models that
address the research problem.
Doing this well typically requires getting advice from technical experts in research methodology
and the people who can provide access to data, and of course, the respondents or informants of
information.
4. Problem solving: communicate, interpret, and apply the empirical findings on which models
better answer the research question about the problem.
Communications might begin with written reports and presentations for knowledge transfer,
then conversations to interpret different meanings of the report, and then pragmatic and
political negotiations to reconcile conflicting interests.
Summary of argument for engaged scholarship
3
, Claim: We can increase knowledge for science and practice by engaging stakeholders in four research
steps: problem formulation, theory building, research design & problem solving
Reasons:
Research problems questions exceed our capabilities
It's about knowledge co production, not knowledge transfer
The mission of scholars in professional schools is to engage practitioners in research
Academic practitioner research relationships are often negotiated poorly
Evidence:
Time spent in research sites correlates positively with implementation & impact
Extraordinary knowledge advances are triggered by phenomena outside of science.
Reservations:
Problem complexity moderates engaged scholarship
Engagement raises false expectations of consensus, control, influence
Qualifications:
Not all research questions require engagement
Assume relationships involves negotiation, mutual respect, & desire to learn
Akkermans’ research pyramid
The feedback view of management and policy
The nature of the decision process
4
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