A Data Model for Predictive Supply Chain Risk Management

Gabellini, Matteo; Civolani, Lorenzo; Regattieri, Alberto; Calabrese, Francesca

doi:10.1007/978-3-031-34821-1_40

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…According to the time-series regression conceptualization of the problem, a long short-term memory (LSTM) model [34] is proposed in the model selection step. Indeed, LSTM models have proven to be capable of dealing with time-series regression problems in several real SC applications [30,[35][36][37]. Regarding the training strategy, a local training strategy is proposed with the root mean squared error (RMSE) as the loss function to find the optimal internal parameters of the LSTM model.…”

Section: Model Learning Stagementioning

confidence: 99%

A Deep Learning Approach to Predict Supply Chain Delivery Delay Risk Based on Macroeconomic Indicators: A Case Study in the Automotive Sector

Gabellini,

Civolani,

Calabrese

et al. 2024

Applied Sciences

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The development of predictive approaches to estimate supplier delivery risks has become vital for companies that rely heavily on outsourcing practices and lean management strategies in the era of the shortage economy. However, the literature that presents studies proposing the development of such approaches is still in its infancy, and several gaps have been found. In particular, most of the current studies present approaches that can only estimate whether suppliers will be late or not. Moreover, even if autocorrelation in data has been widely considered in demand forecasting, it has been neglected in supplier delivery risk predictions. Finally, current approaches struggle to consider macroeconomic data as input and rely mostly on machine learning models, while deep learning ones have rarely been investigated. The main contribution of this study is thus to propose a new approach that for the first time simultaneously adopts a deep learning model able to capture autocorrelation in data and integrates several macroeconomic indicators as input. Furthermore, as a second contribution, the performance of the proposed approach has been investigated in a real automotive case study and compared with those studies resulting from approaches that adopt traditional statistical models and models that do not consider macroeconomic indicators as additional inputs. The results highlight the capabilities of the proposed approach to provide good forecasts and outperform benchmarks for most of the considered predictions. Furthermore, the results provide evidence of the importance of considering macroeconomic indicators as additional input.

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Section: Model Learning Stagementioning

confidence: 99%

A Deep Learning Approach to Predict Supply Chain Delivery Delay Risk Based on Macroeconomic Indicators: A Case Study in the Automotive Sector

Gabellini,

Civolani,

Calabrese

et al. 2024

Applied Sciences

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“…LSTM models are DL models designed to address the challenges of learning longterm dependencies in sequential data by capturing temporal dependencies over extended sequences. These features make them well-suited for tasks such as time series forecasting, and several studies have proved their advantages over other models in solving predictive tasks in the field of SCM [31][32][33][34].…”

Section: Predictive Modulementioning

confidence: 99%

Balancing Data Acquisition Benefits and Ordering Costs for Predictive Supplier Selection and Order Allocation

Regattieri,

Gabellini,

Calabrese

et al. 2024

Applied Sciences

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The strategic selection of suppliers and the allocation of orders across multiple periods have long been recognized as critical aspects influencing company expenditure and resilience. Leveraging the enhanced predictive capabilities afforded by machine learning models, direct lookahead models—linear programming models that optimize future decisions based on forecasts generated by external predictive modules—have emerged as viable alternatives to traditional deterministic and stochastic programming methodologies to solve related problems. However, despite these advancements, approaches implementing direct lookahead models typically lack mechanisms for updating forecasts over time. Yet, in practice, suppliers often exhibit dynamic behaviours, and failing to update forecasts can lead to suboptimal decision-making. This study introduces a novel approach based on parametrized direct lookahead models to address this gap. The approach explicitly addresses the hidden trade-offs associated with incorporating forecast updates. Recognizing that forecasts can only be updated by acquiring new data and that the primary means of acquiring supplier-related data is through order allocation, this study investigates the trade-offs between data acquisition benefits and order allocation costs. An experimental design utilizing real-world automotive sector data is employed to assess the potential of the proposed approach against various benchmarks. These benchmarks include decision scenarios representing perfect foresight, no data acquisition benefits, and consistently positive benefits. Empirical findings demonstrate that the proposed approach achieves performance levels comparable to those of decision-makers with perfect foresight while consistently outperforming benchmarks not balancing order allocation costs and data acquisition benefits.

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A continuous training approach for risk informed supplier selection and order allocation

Gabellini,

Mak,

Schoepf

et al. 2024

Production & Manufacturing Research

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A Data Model for Predictive Supply Chain Risk Management

Cited by 3 publications

References 14 publications

A Deep Learning Approach to Predict Supply Chain Delivery Delay Risk Based on Macroeconomic Indicators: A Case Study in the Automotive Sector

A Deep Learning Approach to Predict Supply Chain Delivery Delay Risk Based on Macroeconomic Indicators: A Case Study in the Automotive Sector

Balancing Data Acquisition Benefits and Ordering Costs for Predictive Supplier Selection and Order Allocation

A continuous training approach for risk informed supplier selection and order allocation

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