4. Implementation
Dow is a very large global manufacturing company that conducts business in more than 160 countries. Dow’s supply chain utilizes a variety of modes of transportation such as: Air, Deep Sea, Inland Waterways, Pipeline, Postal Service, Rail, Road, and Sea [
13]. Dow is keenly aware of the ramifications that disruptive events have on its operations, its customers, and its overall profitability. In an effort to advance its existing supply chain event management processes, Dow (and partner organizations) proposed a research project to the Manufacturing x Digital (MxD) Institute (
www.mxdusa.org) aimed at improving and further automating the current SCEM process underway at Dow by creating a framework to digitize, integrate, and automate the information pipeline and action workflow, along with offering Dow users recommendations based on prior mitigation actions [
13]. The MxD Institute awarded project resulted in the development of five (5) modules that perform independent actions, but when linked together, deliver the desired results applied to outbound Dow shipments. The five modules are as follows:
Predictive Transit: This module provides an estimated shipment transit time for future shipments based on source, destination, planned shipment date, product type, weather, and event data.
Risk Assessment: This module provides a graphical user interface (GUI) to enable a user to document current and future events and automatically compute a risk for each individual outbound shipment in the affected geographical area.
Mitigation Planning: This module automatically sends a communication (text/SMS or email) to subscribed decision makers when an outbound shipment has been promoted by the RA module. The communication provides an overview of the shipment and the event impacting it. The user can then enter in mitigation information, which is stored. After enough mitigation decisions are collected, a machine learning method may be trained to then automate recommended mitigation actions.
SIMBA Chain Communications: This module integrates relevant data from different modules using a blockchain ledger and sends automated notifications to targeted individuals (Dow internal and external) when risk thresholds are met.
Performance Analytics: This module is a data enablement SQL table within the Microsoft Azure environment with direct connection to a dashboard that calculates and displays Key Performance Indicators (KPIs) which are derived from the various modules. This module provides aggregated data, along with an easy access point for monitoring and decision making.
Figure 4 depicts interactions among the modules.The focus of this article is the RA module, which received information from users directly (SCEM analysts and business unit managers); read additional information from decision-support data and the predictive transit module; and finally stored the information to Risk Mitigation/Performance database and sent an immutable record to the blockchain. Dow’s expected value from such a system as a result of reductions in disruption incurred freight costs and reductions in disruption related manpower costs is estimated to be 5,000,000 USD over three years in Dow’s Outbound Logistics space for North America [
13]. Although the modules described above were developed to be independent, each module was designed with interfaces to pass data to other modules as needed. For example, the RA module can read inputs from the Predictive Transit module, or directly from a human (supply chain event manager). And under the circumstance where the RA module informs the Mitigation Planning module regarding a specific shipment, the RA module and the subsequent modules all coordinate. The implementation of the described methodology for the RA module is depicted in the block diagram of
Figure 5.
For all shipments originating or terminating in the event-affected area, SPS
, computed from weighted customer variables, and SAL
, provided by the SCEM analyst, were mapped through a utility function to produce risk
r, which was compared to the acceptable threshold
. A shipment with
was recommended for mitigation. The RA framework is accessed through a GUI, which connects to the manufacturer’s database and is further described in the Code and Implementation section as well as in the
Appendix A. The additional figures in the
Appendix A describe how to use the GUI and gives an example of a multi-day geofence. For an event spanning multiple days whose geofences enclose hundreds of shipments, the entire risk assessment computation, including database communication, takes about 20 seconds.
Events with fewer affected shipments complete the risk assessment in less time. In general, an individual shipment’s processing takes a fraction of a second. This includes obtaining geo-location of the shipment, querying associated customer and business unit variables, the entire risk computation, database updates, and GUI updates.
5. Conclusions
A framework for risk assessment of potentially disruptive events, developed to facilitate a smooth transition from the existing, manual processes towards automation, was introduced. The initial implementation strongly depended on human participation: SCEM analyst provided input probability—SAL, humans also had capability to overwrite the model-estimated relative shipment importance—SPS, and even overall risk. Because the key inputs depended on people, probabilistic models were kept simple, to make the usage more efficient and avoid overwhelming the user.
The framework was illustrated in the Dow case study and implemented in the Jupyter scientific computational environment. MS SQL database tables stored the shipment data, model parameters, event-characterization parameters, and model results. The framework featured the data collection system designed for capturing human decisions that were difficult to articulate, paving the way to the ground truth dataset that will enable future machine learning solutions. In early tests, the initial semi-automated solution suggested great acceleration of the decision making process compared to the traditional manual process, from hours down to seconds.
The framework was designed to be used for outbound shipments in a business-to-business environment. These shipments can include several different types of modes, including air, ship, road, rail (and combinations, i.e., mixed-modes of transport), and can include both domestic and international routes. The intent of the project was to be able to use information that informs the likelihood of an event that could have an impact on outbound shipments, and then develop a decision support platform that allows business personnel to take appropriate actions to mitigate disruption to the manufacturer and/or its customers. Although this use case is fairly broad, the framework can not be applied to all types of transportation problems. Applications where expected transportation time is minutes (e.g., food delivery) would not be a suitable application since it would be unlikely that there would be enough time to determine and implement mitigation actions; however, it does seem likely that the framework could be applied to inbound shipments, as long as accurate data could be obtained regarding planned routes, in transit location, and other relevant data to implement the RA framework. One limitation is that the only uncertainty is related to weather, or similar event; the present framework does not include many other aspects of inbound logistics, such as operations of the suppliers and their dependencies.
Future work will examine the extension to develop additional aspects of inbound logistics. More work is needed in developing the cost models, which will pave the way to more traditional optimization. The next iteration of the implementation will test users’ adoption of the second interpretation of the geo-fence ellipses, as a Gaussian distribution as well as the mixture of Gaussian distributions. Finally, the captured human decisions that disagreed with automated assessments related to SPS, or overall risk, will be used to develop a better data-driven model over time.
Author Contributions
Conceptualization, N.G.N., M.K., and J.A.; methodology, N.G.N. and M.K.; software, N.G.N. and C.J.V.; validation, N.G.N., M.K., and J.A.; formal analysis, N.G.N. and C.J.V.; investigation, M.K., N.G.N., C.J.V., and J.A.; resources, J.A. and P.B.; data curation, J.A.; writing—original draft preparation, N.G.N.; writing—review and editing, M.K., N.G.N., C.J.V., J.A., and P.B.; visualization, N.G.N. and C.J.V.; supervision, M.K., N.G.N., J.A., and P.B.; project administration, M.K., N.G.N., and J.A.; funding acquisition, M.K., N.G.N., and J.A. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by MxD Institute grant number 17-02-01.
Acknowledgments
Effort sponsored by the U.S. Government under Agreement number W31P4Q-14-2-0001 between the MXD USA and the Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government. This material is based on research sponsored by Office of the Under Secretary of Defense for Research and Engineering, Strategic Technology Protection and Exploitation, Defense Manufacturing Science and Technology Program under agreement number W31P4Q-14-2-0001. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
GUI | Graphical user interface |
RA | Risk assessment |
SAL | Subjective assessment of likelihood |
SCM | Supply chain management |
SCEM | Supply chain and event management |
SCRM | Supply chain risk management |
SPS | Shipment priority score |
Nomenclature
| A customer weight |
| Geofence longitude coordinate |
| Geofence latitude coordinate |
a | Geofence major axis |
b | Geofence minor axis |
| Geofence tilt angle |
| Gaussian mean |
| Gaussian covariance |
| Gaussian variance component to |
| Gaussian mixture coefficients |
| Pearson correlation coefficient |
| Mitigation indicator |
| Subjective assessment of likelihood probability |
| Gaussian mixture components |
| Wishart distribution |
| Precision matrix, the inverse of the covariance matrix |
| Set of all shipments on within the specified time interval |
| Set of affected shipments |
V | Wishart parameter matrix |
| Gamma distribution, |
L | Loss function for mapping risk (implemented as a look-up table) |
| Customer variable category |
| Customer variable value |
| Customer variable normalized value |
r | Risk |
| Risk threshold |
| Shipment priority score |
References
- Vishnu, C.; Sridharan, R.; Kumar, P.R. Supply chain risk management: Models and methods. Int. J. Manag. Decis. Mak. 2019, 18, 31–75. [Google Scholar]
- Bearzotti, L.A.; Salomone, E.; Chiotti, O.J. An autonomous multi-agent approach to supply chain event management. Int. J. Prod. Econ. 2012, 135, 468–478. [Google Scholar] [CrossRef]
- Savage, L.J. The Foundations of Statistics; Dover Publications, Inc.: Garden City, NY, USA, 1972. [Google Scholar]
- Jaynes, E.T. Probability Theory: The Logic of Science; Cambridge University Press: Cambridge, UK, 2003. [Google Scholar]
- Theodoridis, S. Bayesian learning: Inference and the EM algorithm. In Machine Learning: A Bayesian and Optimization Perspective; Academic Press: Cambridge, MA, USA, 2015; Chapter 12; p. 586. [Google Scholar]
- Duda, R.O.; Hart, P.E.; Stork, D.G. Bayesian decision theory. In Pattern Classification, 2nd ed.; Wiley-Interscience, John Willey & Sons Inc.: Hoboken, NJ, USA, 2001; Chapter 2; pp. 33–36. [Google Scholar]
- Bishop, C.M. Mixture of gaussians. In Pattern Recognition and Machine Learning; Springer: Berlin/Heidelberg, Germany, 2006; Chapter 2; pp. 110–113. [Google Scholar]
- Pearl, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference; Morgan Kaufmann: San Francisco, CA, USA, 1988. [Google Scholar]
- Koller, D.; Friedman, N. Probabilistic Graphical Models: Principles and Techniques; MIT Press: Cambridge, MA, USA, 2009. [Google Scholar]
- Raiffa, H.; Schlaifer, R. Applied Statistical Decision Theory; Division of Research, Graduate School of Business Administration: Harvard, MA, USA, 1961. [Google Scholar]
- Bishop, C.M. Exponential family. In Pattern Recognition and Machine Learning; Springer: Berlin/Heidelberg, Germany, 2006; Chapter 2; pp. 113–117. [Google Scholar]
- Berger, J.O. Utility and loss. In Statistical Decision Theory and Bayesian Analysis, 2nd ed.; Springer: Berlin/Heidelberg, Germany, 1985; Chapter 2; pp. 46–73. [Google Scholar]
- Archbold, J. Supply Chain Risk Alert; Technical Report 17-02-01; MxD Institute: Chicago, IL, USA, 2019. [Google Scholar]
- Van Rossum, G.; Drake, F.L., Jr. Python Tutorial; Centrum voor Wiskunde en Informatica Amsterdam: Amsterdam, The Netherlands, 1995. [Google Scholar]
- Kluyver, T.; Ragan-Kelley, B.; Pérez, F.; Granger, B.; Bussonnier, M.; Frederic, J.; Kelley, K.; Hamrick, J.; Grout, J.; Corlay, S.; et al. Jupyter notebooks—A publishing format for reproducible computational workflows. In Positioning and Power in Academic Publishing: Players, Agents and Agendas; Loizides, F., Schmidt, B., Eds.; IOS Press: Amsterdam, The Netherlands, 2016; pp. 87–90. [Google Scholar]
- Mease, J. Bringing ipywidgets Support to plotly. py. In Proceedings of the 17th Python in Science Conference (SciPy 2018), Austin, TX, USA, 9–15 July 2018; Available online: http://conference.scipy.org/proceedings/scipy2018/ (accessed on 15 May 2020).
- Derrough, J. Instant Interactive Map Designs with Leaflet JavaScript Library How-to; Packt Publishing Ltd.: Birmingham, UK, 2013. [Google Scholar]
- Jupyter-Team. Jupyterhub Documentation. Available online: https://jupyterhub.readthedocs.io/en/stable/ (accessed on 30 September 2019).
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