Cloud Adaptation for an Agile Supply Chain – CAASC

Three complementary issues are investigated:
- Identifying uncertainties: Based on a history of plans, a similarity calculation and an ascending hierarchical classification are used to identify zones of the planning horizon with similar variations. An uncertainty model is then calculated for each zone.
- Planning under uncertainty: An actor receives 2 types of plans (customer requirements and supplier delivery plans) for which he has a fuzzy uncertainty model; this model depends on the zones of the horizon. For his decisions, we quantify the risks of material failure, customer dissatisfaction and rescheduling on the basis of indicators of robustness, severity, adaptability and frequency, using uncertainty measures adapted to the processing of fuzzy data (possibility measures and necessity measures).
- A knowledge compilation technique computes a condensed representation of a set of admissible solutions to the decision problem, thus retaining flexibility (alternative plans) while speeding up the response to online queries. Here, we deal with problems from the Lot Sizing planning literature. For the single-product case, a representation used in dynamic programming is adapted. For the multi-product context, crossover algorithms from the knoxledge compilation literature enable the intersection of compiled single-product forms, and one representing capacity allocation possibilities, is based on.

On industrial data, the modeling module explains counter-intuitive results: greater uncertainties in the short term; reduced uncertainties during the pandemic; sensitivity to the behavior of decision-makers.
The prototype for planning and negotiation integrates fuzzy models of the uncertainties inherited from the partners. It has been validated on real data and by simulation.
Evaluation of the compilation approach confirms the need for domain-specific adaptation to speed up the compilation and querying. An interest in sharing a compiled form has also been shown.

The project has demonstrated the value of modules to assess the impact of uncertainties, not as a replacement, but as a complement to, existing planning deterministic tools. In addition to improve the TRL of teh solution, a number of other points need to be explored in greater depth: the integration of uncertainty linked to the rolling horizon with other forms of uncertainty, the detection of model changes, collaboration between stakeholders on uncertainty models, the integration of decision-maker preferences and uncertainties, and improving the performance of the proposed compilation.

2 PhD theses and 6 international refereed conferences have resulted:
- uncertainties linked to the sliding horizon: Walid Khellaf thesis (chap 4 and 5), modeling (MOSIM'2020), evaluation by simulation(APMS 2021)
- risks linked to customer and supplier uncertainties in a decision support module: thesis by Sanaa Tiss, a serious game (PRO-VE 2019), a risk assessment model (MOSIM 2020), an interface (ILS 2020).
- Support for replanning by compiling «Lot Sizing« models: Walid Khellaf thesis, chapter 6, APMS2022

Barne, L. C.; Giordano, J.; Collins, T.; Desantis, A. Decoding Trans-Saccadic Prediction Error. Journal of Neuroscience. 2023, 43(11), 1933-1939.

Bonnet, E.; Masson, G. S.; Desantis, A. What over When in causal agency: Causal experience prioritizes outcome prediction over temporal priority. Consciousness and Cognition. 2022, 104, 103378.

Ficarella, S. C.; Desantis, A.; Zenon, A.; Burle, B. Preparing to React: A Behavioral Study on the Interplay between Proactive and Reactive Action Inhibition. Brain Sciences. 2021, 11, 680.

Desantis, A.; Chan-Hon-Tong, A.; Collins, T.; Hogendoorn, H.; Cavanagh, P. Decoding the Temporal Dynamics of Covert Spatial Attention Using Multivariate EEG Analysis: Contributions of Raw Amplitude and Alpha Power. Frontiers in Human Neuroscience. 2020, 14.

The CAASC project considers a supply chain where independent partners accept to share in a cloud platform a series of information about their mid-term plans. Maintenance and misalignment of these plans is a major source of problems (stock-outs, over-stocks, distribution over-costs, irrelevant capacities) in supply chains. Shared information concern agreed plans, but also the current status and current forecasts associated to sales, production, inventory, transports. In particular, CAASC is interested in the detection of deviations between the agreed partners’ plans and the current situation in order to look for, if required, supply chain agile adaptations to these deviations. CAASC takes advantage of the software and test infrastructures developed for the H2020 C2NET project.
But, while C2NET was interested in the import of data, their integration into a supply chain model and the detection of deviations, CAASC is focusing on the analysis of deviations and the development of agility capabilities. One major add-on of CAASC is the management of uncertainties in the monitoring process of a plan in order to reduce the nervousness of decisions, counter the bullwhip effect or avoid an irrelevant protection of decoupling points.
As a consequence, CAASC develops three research axis:
The first axis concerns the identification and quantification of uncertainties. The point is to take advantage of the flow of data in the solution, and the capabilities of automated learning algorithms. The goal is to model effective uncertainties in a supply chain: typify, classify, quantify or discover emergent uncertainties, while selecting the appropriate modelling approach (probabilities, possibilities, uncertain probabilities, ). This axis takes advantage of the knowhow of Linagora for machine learning algorithms, the experience in decision under uncertainty of IRIT/ADRIA and the knowledge in supply chain risks management of the whole consortium.
The second axis studies the projection of a series of uncertainties and deviations on a plan. It evaluates the capability of an actor to control the effects of uncertainties and deviations on his perimeter. It results in the identification of risks for the other partners. While bibliography is systematically focusing on one type of uncertainty, the goal here is to integrate various representations of uncertainties, in order to result in a more realistic risks characterisation.
Third, for critical risks anticipating adaptation strategies are mandatory. More than re-planning algorithms, we propose to provide a compact representation of risks management strategies for supply chain planning. This totally new approach in the domain of supply chain planning, based on the principles of knowledge compilation, will guaranty finding adaptations in polynomial time when risks occur.
CAASC considers two types of case studies adapted from C2NET and integrating supply chain uncertainties and risks. The first is a series of scenarios of various sizes that result from real data extracted from the Pierre Fabre Group information systems. They enable measuring the scalability of the solution. The second type is a serious game that enables to assess the dynamic behaviours. On these use cases, various managerial effects are looked for: the formalisation of the decision makers behaviours, the impacts of the proposed tools on the capacity of people to collaborate, and more globally, the organisational changes.
Finally, the tools and services developed during this project aim at being integrated in the OpenPaaS solution, the cloud collaborative open-source platform provided by Linagora. The objective is to develop differentiating services for industrial companies.
Overall, the funding will support 2 PhDs, a high scientific integration with Linagora and an industrial validation by the Pierre Fabre company.