Skip to main content
Have a personal or library account? Click to login
Logistics decision-making based on the maturity assessment of imperfect knowledge Cover

Logistics decision-making based on the maturity assessment of imperfect knowledge

Engineering Management in Production and Services
Volume 11 (2019): Issue 4 (December 2019)
By:   
Open Access
|Dec 2019
Figures & tables

Figures & Tables

Fig. 1

ROC curve

Tab. 1

Development steps of logistics in business practice

STAGETHE MAIN CHARACTERISTICTHE DOMINANT APPROACHTHE MAIN GOAL
Logistics 1.0Delivery of goods in three steps: “transport — transshipment — storage”functional, regulatory, reactiveeffectiveness
Logistics 2.0Management of goods flow processes within a single enterprisesystemic, activelean, flexibility
Logistics 3.0Comprehensive management of the goods flow in supply chainsintegration across the borders of the organisationefficiency, agility
Logistics 4.0Optimisation and virtualisation of logistics networks within the 3rd IT platform (SMAC)dynamic, global perspective, realtime operation (RTE)effectiveness efficiency, leagility, resilience
Fig. 2

Diagram of the knowledge preparation process for managerial decision-making

Fig. 3

Model of the knowledge-creation process

Fig. 4

Model of the imperfect knowledge management process

Fig. 5

Example of an extended risk matrix

Tab. 2

Data Veracity Value appraisement for the given example

DATA VERACITY ATTRIBUTES Ai012RANK
A1 –Accuracy (ACC)x1
A2 – Clarity (CLA)x2
A3 – Consistency (CON)x1
A4 – Plausibility (PLA)x2
A5 – Traceability (TRA)x1
Data Veracity Value (DVV) =4 - moderate
Tab. 3

Informati on Utility Value estimation for the given example

INFORMATION UTILITY ATTRIBUTES Bi123RANK
B1 – Believability (BEL)x2
B2 – Completeness (COM)x3
B3 – Correctness (COR)x2
B4 – Relevancy (REL)x3
B5 – Timeliness (TIM)x2
Information Utility Value (IUV) =72 - high
Tab. 4

Example of the Knowledge Processing Quality evaluation for the given example

KNOWLEDGE PROCESSING QUALITY ATTRIBUTES Ci123RANK
C1 – Model adequacy (MA)x3
C2 – Parameter accuracy (PA)x2
C3 – Decision correctness (DC)x2
C4 – Communication reliability (CR)x3
C5 – Realization compliance (RC)x1
Knowledge Processing Quality (KPQ) =36 - moderate
Tab. 5

Example of the Knowledge Maturity Level rating for the given example

KNOWLEDGE MATURITY ATTRIBUTES DiVL=1L=2M=3H=4VH=5RANK
D1 – DVVx3
D2 – IUVx4
D3 – KPQx3
Knowledge Maturity Level (KML) =36 - moderate
DOI: https://doi.org/10.2478/emj-2019-0034 | Journal eISSN: 2543-912X (formerly 2300-0813) | Journal ISSN: 2543-6597
Journal RSS Feed
Language: English
Page range: 65 - 79
Submitted on: Jun 15, 2019
Accepted on: Oct 15, 2019
Published on: Dec 18, 2019
Published by: Bialystok University of Technology
In partnership with: Paradigm Publishing Services
Keywords:
Industry 4.0,
Logistics 4.0,
Big Data,
imperfect knowledge,
knowledge maturity
Related subjects:
Business and economics,
Business management,
Industries,
Transportation, logistics, air traffic, shipping,
Engineering,
Mechanical engineering,
Production technology,
Process engineering and industrial engineering

© 2019 Lech Bukowski, published by Bialystok University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 11 (2019): Issue 4 (December 2019)
Previous article
Next article