Managing supplier delivery reliability risk under limited information: Foundations for a human-in-the-loop DSS

Abstract

The potential impact of suppliers' delivery reliability issues in many industries requires a proper decision support system (DSS) that allows decision makers to analyze and reduce the delay's detrimental effects. Despite the relevance of the topic, companies are often confronted with the lack of historical, quantitative data and knowledge about a supplier's performance (i.e., when selecting a new supplier). In this paper, we address the problem of the scarcity of quantitative data by considering and extending the human-in-the-loop DSS concept, which accounts for an expert's knowledge and experience. In our concept, a human expert is involved in making and revising data provided by a computational model, with the aim of supporting companies in making decisions when dealing with unreliable suppliers, in order to minimize the costs related to external discontinuities. To deal with scant quantitative data, we developed a distribution-free model. Our findings positively support the distribution-free approach as an effective tool to be used when only a limited and perhaps unstructured base of data is available. The presented computational model aims at creating a solid foundation for developing a comprehensive human-in-the-loop decision support system.

Introduction

Recently, delivery reliability has shifted from an order winner to an order qualifier factor in many manufacturing and service industries [11], thanks to the wide diffusion and consolidation of operations paradigms, such as just-in-time and quick response distribution [7], [26]. In extended, complex, and highly collaborative supply chains, suppliers' delivery reliability – a specifically operational performance measure – is strongly affected by strategic and tactical decisions, such as selecting the “right” suppliers for conjoint design, development, manufacturing, and distribution. In fact, seeking the timeliness of all operations since the network design stage is of paramount importance when a large part of the production is outsourced. In this respect, suppliers are often perceived as a source of delay risk, affecting the company's delivery reliability either directly or indirectly [9], [27].

The potential impact of the suppliers' delivery reliability problems in many production industries requires a proper decision support system (DSS) to help in analyzing and reducing the delay's detrimental effects. Such a support system must be able to deal with highly complex environments, where both qualitative and quantitative elements play a substantial role.

Nonetheless, considering the quantitative perspective, companies are often confronted with the lack of historical, quantifiable data and information about a supplier's previous performance; thus, in today's volatile business environment decisions usually have to be based on incomplete or even non-existent information. Focusing on the delivery reliability dimension, for example, distributional information on the earlier delivery performance of a supplier may be limited or missing (i.e., in the case of a new supplier). Sometimes, such a lack of numerical data is compensated for by an educated guess of the mean and the variance of the data distribution. According to Moon and Yun [18], under these conditions the tendency is to use normal distribution in the computational models, even though such an assumption does not provide the best decision in cases when other probability distributions with the same mean and variance occur.

Furthermore, although DSS can assist decision makers in acquiring data, information, and knowledge regarding products, services, suppliers, and customers, not all of the possible factors behind an optimal decision can be algorithmically implemented, due to the complexity or the nature of the factors themselves. This aspect emphasizes the role and contribution of a human expert in integrating qualitative knowledge into the decision process.

Considering the relevance of the supplier's delivery reliability dimension and the scarcity of contributions to this topic, the purpose of this paper is to investigate the feasibility of an architecture for a DSS that supports decision makers in dealing with delivery delay risks when the available information about a supplier's performance is limited to the mean and the standard deviation (or their educated guess).

Specifically, we strive to address the following research questions:

• How is it possible to mitigate the delay risk? How may computational models support the decision maker when no historical knowledge of the supplier's performance is available?

• How can the computational model developed for delay risk mitigation be implemented within a DSS and integrated with qualitative information?

We see our contribution as a possible foundation for developing a human-in-the-loop decision support system (HIL-DSS), allowing quantitative data to be complemented with qualitative information provided by a human expert. In order to answer the proposed research questions, the remainder of the paper is organized in five sections: the next section explores the role of buffers in reducing delivery delays in manufacturing sectors. Then, we delineate the scenario in which we developed and tested the computational model. The computational results are discussed in the following section, along with an outline of the integration of qualitative information. Finally, we present our conclusions and recommendations for future research.