Main objective of any business project is to deliver business value which for this project was defined as increase visibility on supply chain losses, to enable better root cause analysis, decision making and prioritization of actions in order to decrease and prevent those costs, increase productivity and effective time spent of PPM Teams. The objective and purpose of the project was achieved which is also reflected by the evaluation of pilot by business users.
In the first part of the paper we were defining business environment, mapping relevant business processes and understanding of main business challenges. From that perspective we have tackled 5 out of directly by implementing the pilot solution. Remaining challenges will be tackled in second phase of the project, but even from that perspective we can consider project as successful.
This paper presented also comprehensive end-to-end methodology and detail process for implementation of a BI solution which can be leverage in many different business areas. In the first part it focus on understanding business environment, understanding operating model and identification of main business challenges. Afterwards follow with capturing and formalizing of business requirements, defining practical approach to identifying data requirements and sources in complex environment with low data maturity and lack of knowledge and documentation. Following by designing solution architecture and implementation of solution and approach to define governance model. My framework offers a practical means for any project manager responsible for data & analytics project implementation using Agile Scrum project methods and leveraging best practices of different frameworks combined with my practical knowledge and experience gained in more than 7 years of project management in process improvement area.
Second part of the paper is dedicated to implementation of pilot solution and defining several data and text mining approaches to tackle common business reporting problem of defining reason codes to enable root cause analysis. One improvement that should be still worked on is the optimization of infrastructure and performance optimisation which despite the newly available functionalities of Microsoft platform still require more long-term sustainable solution. This will allow us to improve the frequency of reporting results and more real time reaction in business.
Last part of the paper is describing approach to define and set up of data governance process which is very common reason of failure of data and analytics related projects and big contribution to many problems.
42
List of Figures
Figure 1. Kimball Lifecycle Diagram ... 2
Figure 2 Management Model ... 3
Figure 3 International Supply Chain - Financial responsibility split ... 5
Figure 4 Data Mapping Methodology. Internal Methodology. ... 8
Figure 5 Data Discovery - What affects Supply Chain Losses ... 11
Figure 6 AI LEAN Canvas ... 12
Figure 7 Data management Maturity level ... 13
Figure 8 AS-Is Process of Supply Chain Loss reporting ...14
Figure 9 Global view Dashboard - 1st Mock up proposal ...16
Figure 10 Zone Dashboard 1 - First Mock- up ... 17
Figure 11 Main Offenders Dashboard - First Mock-up ... 18
Figure 12 Obsolescence Risk Dashboard - First Mock - up ...19
Figure 13 TO-BE conceptual model ... 21
Figure 14 Logical Architecture model for Proof-of-Concept Solution ... 22
Figure 15 implementation roadmap ... 24
Figure 16 Global view - Final dashboard design ... 29
Figure 17 Main Offenders - Final Dashboard Reasons ... 30
Figure 18 Main Offenders - Final Dashboard Products ... 30
Figure 19 Main Offenders - Final Dashboard Country view ... 31
Figure 20 Conceptual Data governance model ... 36
Figure 21 Documentation RACI model ... 39
Figure 22 Physical Data Model for Pilot implementation ... 47
43
List of Tables
Table 1 List of Analytical Use Cases ... 9
Table 2 Business Requirements - metrics definition and frequency ... 10
Table 3 Business Requirements - metrics and dimensions definition ... 10
Table 4 Data Management Maturity Assessment Logistics Costs... 13
Table 5 Analytical used cases and relevant Dashboards ... 15
Table 6 Global Dashboard: Metrics and Dimensions ...16
Table 7 Zone Dashboard: Metrics and Dimensions ... 17
Table 8 Main Offenders Dashboard: Metrics and Dimensions ... 18
Table 9 Obsolescence Risk Dashboard: Metrics and Dimensions ...19
Table 10 International Scope identification possible implementation options ... 26
Table 11 Reason Code determination approaches ... 27
Table 12 Roles & responsibilities ... 37
Table 14 Data Object - Source mapping ... 45
44
Annexes
Annex A: Reason Code determination code
45
Annex B: Data Source Mapping
Table 13 Data Object - Source mapping
Analytical Used Case Data Objects Source Zone Data Type
1. SCL – Enhanced Reporting
Breweries Excel ALL Master Data
Exchange Rates Excel ALL Master Data
Flows Excel ALL Reference Data
Packages SCFD ALL Reference Data
Products ERP ALL Master Data
VILC_Cost_Centres AB1 NAZ Transactional VILC_Cost_Centres BRP SAZ Transactional VILC_Cost_Centres CRP APAC Transactional VILC_Cost_Centres ERP EUR Transactional VILC_Cost_Centres PR0 MAZ Transactional VILC_Cost_Centres SOP AFR Transactional VLC_Cost_Centres AB1 NAZ Transactional VLC_Cost_Centres BRP SAZ Transactional VLC_Cost_Centres CRP APAC Transactional VLC_Cost_Centres ERP EUR Transactional VLC_Cost_Centres PR0 MAZ Transactional VLC_Cost_Centres SOP AFR Transactional
Zones Excel ALL Reference Data
2. SCL - Deep Dive and Focus Area Identification
Breweries Excel ALL Master Data
Customers ERP ALL Master Data
External new ALL Master Data
Flows Excel ALL Reference Data
Footprint E2Open ALL Master Data
Orders ERP ALL Transactional
Portfolio ERP ALL Master Data
Production ERP EUR Transactional Production Mainframe NAZ Transactional Production PR0 MAZ Transactional
Products ERP EUR Master Data
Products Mainframe NAZ Master Data
46
Products PR0 MAZ Master Data
Shipments BuyCo ALL Transactional
Tender Excel ALL Transactional
Zones Excel ALL Reference Data
3. Obsolete – Inventory Process Improvement using Obsolete flag
Demand Forecast E2Open ALL Transactional
DOI Excel ALL Transactional
Orders ERP ALL Transactional
PL_Capacities Excel ALL Master Data
Products E2Open ALL Master Data
Shipments BuyCo ALL Transactional Stock Level ERP EUR Transactional Stock Level PR0 MAZ Transactional Stock Level WMS NAZ Transactional Stock Policy Excel ALL Master Data TP_Capacities Excel ALL Master Data
47
Annex C: Physical data model
Figure 22 Physical Data Model for Pilot implementation
48
List of references
1. MYERS, Michael D. Qualitative Research in Business & Management. London : Sage Publication Ltd., 2013. Vol. 2nd edition. ISBN 978-0-85702-973-7.
2. Kimball, Ralph. The Data Warehouse Lifecycle Toolkit. s.l. : Wiley, 2007. Vol. 2nd edition. ISBN 978-0470149775.
3. Sutherland, Jeff and J.J., Sutherland. Scrum: The Art of Doing Twice the Work in Half the Time. 1st edition. New York : Crown Publishing, 2014. ISBN 978-0-385-34645-0.
4. ICC, International Chamber of Commerce. Incoterms® 2020. iccwbo.org.
[Online]
5. VOŘÍŠEK, Jiří, Josef BASL, Alena BUCHALCEVOVÁ, Libor GÁLA, Renáta KUNSTOVÁ, Ota NOVOTNÝ, Jan POUR a Eva ŠIMKOVÁ. Principy a modely řízení podnikové informatiky. Praha : Oeconomica, 2008. ISBN 978-80-245-1440-6.
6. Maoz, Michael. How IT Should Deepen Big Data Analysis to Support Customer-Centricity. s.l. : Gartner, 2013.
7. Institute, cmmi. ISACA®’s CMMI® maturity models. https://cmmiinstitute.com/.
[Online]
8. Slánský, David. Data and Analytics for the 21st Century: Architecture and Governance. Praha : Professional Publishing, 2018. ISBN 978-80-88260-16-5..
9. —. Data and Analytics for the 21st Century: Trends and Mega Problems. s.l. : Professional Publishing, 2018.
10. Aniket Rangrej, Sayali Kulkarni, and Ashish V. Tendulkar. Comparative Study of Clustering Techniques for Short Text Documents. Proceedings of the 20th International Conference Companion on World Wide Web. s.l., New York, NY, USA : Association for Computing Machinery, 2011. Vol. WWW '11, pp. 111–112.
11. Jablonský, Josef. Ranking of countries in sporting events using two-stage data envelopment analysis models: a case of Summer Olympic Games 2016. Central European Journal of Operations Research. 2018, Vol. 26, 4, pp. 951--966.
12. Hindls, Richard, et al. Statistika v ekonomii. 1. Příbram : Professional Publishing, 2018. 978-80-88260-09-7.
13. Radváková, Věra, et al. Metody vědecké práce. Praha : Oeconomica, 2018. p. 134.
ISBN 978-80-245-2249-4.
14. towardsdatascience.com. https://towardsdatascience.com/introducing-the-ai-project-canvas-e88e29eb7024. [Online]