Artificial Intelligence in Supply Chain and Operations Management: A Multiple Case Study Research

Artificial intelligence (AI) is increasingly considered a source of competitive advantage in operations and supply chain management (OSCM). However, many organisations still struggle to adopt it successfully and empirical studies providing clear indications are scarce in the literature. This research aims to shed light on how AI applications can support OSCM processes and to identify benefits and barriers to their implementation. To this end, it conducts a multiple case study with semi-structured interviews in six companies, totalling 17 implementation cases. The Supply Chain Operations Reference (SCOR) model guided the entire study and the analysis of the results by targeting specific processes. The results highlighted how AI methods in OSCM can increase the companies' competitiveness by reducing costs and lead times and improving service levels, quality, safety, and sustainability. However, they also identify barriers in the implementation of AI, such as ensuring data quality, lack of specific skills, need for high investments, lack of clarity on economic benefits and lack of experience in cost analysis for AI projects. Although the nature of the study is not suitable for wide generalisation, it offers clear guidance for practitioners facing AI dilemmas in specific SCOR processes and provides the basis for further future research

IFIP advances in information and communication technology

Cross-functional coordination among engineering, sales and production departments is known to be beneficial for improving order fulfillment processes. In Engineer-to-Order (ETO) companies, sales, design and production activities are strongly interrelated and sometimes they overlap, thus requiring cross-functional coordination. In these companies, design and production activities can be both partially performed before the customer order arrival. ETO companies pursue different objectives and implement different managerial approaches before and after the customer order decoupling point (CODP). However, despite its relevance for company performance, how ETO companies manage cross-functional coordination and how departments are coordinated before and after the CODP is still understudied. This paper sheds light on this topic by investigating 12 case studies in the Italian machinery industry. Results suggest that the coordination mechanisms used before and after CODP are different, and vary depending on the CODP configuration chosen.30 August-3 September 202

A decade of engineering-to-order (2010-2020): progress and emerging themes

In 2009 a literature review on supply chain management in Engineer-to-Order (ETO) situations was published in the International Journal of Production Economics (Gosling and Naim, 2009). The paper has received more than 200 citations from over 100 international journals. The ETO body of knowledge has been particularly relevant to those seeking to mobilise operations and supply chain concepts within the context of complex innovative engineering work. These are all increasingly pressing concerns for many organisations in the contemporary global economy; hence, it is timely to revisit this body of knowledge. Consequently, this study performs a systematic review of the last decade (2010–2020) ETO studies to identify the major advances revealed and develop a future research agenda. The results show that literature, over the last decade, presented new emerging trends related to: (i) ETO definitions through conceptualisation of the engineering flows and integration of engineering/production flows via the two-dimensional decoupling point; (ii) strategies for decoupling positioning, supply chain integration, planning and control, uncertainty/risk management, industry 4.0, exploration of new business models and system design, design automation and engineering management in ETO situations; (iii) applicability of lean within ETO situations. Finally, the paper suggests guiding research questions in relation to linkages between different disciplinary areas, evaluation of the application of new technologies, guidance for managing transitions between decoupling configurations and understanding of the new servitisation trends in ETO situations. In conclusion, the study highlights four research challenges to address: positive science challenge, comparative research challenge, multidisciplinary research challenge, and prescriptive research challenge

Engineering and production decoupling configurations: an empirical study in the machinery industry

Engineer-to-order supply chains are traditionally considered to perform all engineering and production activities based on specific orders. However, in practice, some engineering and production activities can be speculatively undertaken to reduce the delivery lead time, thus leading to a range of decoupling configurations for both engineering and production processes. The literature rarely addresses this issue, mainly focusing on either the production or the engineering dimensions, which opens a gap between theory and practice. The purpose of this study is to reduce this gap and assess the potential impact of a unique two-dimensional customer order decoupling point (2D-CODP) framework that is inclusive of all the individual literature studies and to evaluate the managerial approaches employed in the different decoupling configurations. To achieve this aim, research using multiple case studies is conducted in the machinery industry. The key results flowing from the empirical analysis are the identification of 4 clusters of decoupling configurations chosen by the different cases and the classification of the managerial approaches employed in the specific decoupling configurations. The main contribution of this paper is that it adds insight regarding the debate on engineer-to-order definitions. Additionally, this paper enriches existing knowledge regarding the contingencies that drive the application of different managerial approaches upstream and downstream of the CODP. Finally, this paper provides cases that exemplify how to use the 2D-CODP framework, guiding managers in understanding the positioning of the product families and choosing how to manage and coordinate activities upstream and downstream of the CODP based on their positioning

Determinants for order-fulfilment strategies in engineer-to-order companies: insights from the machinery industry

Recent empirical studies have refined our understanding of engineer-to-order (ETO) situations, supporting the existence of different order-fulfilment strategies based on the degree of customer involvement in the engineering and production activities, which differs depending on the strategic fit with the environment in which the company operates. Despite the importance of this finding, limited attempts have been made to comprehensively understand the determinants for this strategic choice in ETO companies. To overcome this gap, this study aimed to investigate the sources of differentiation between the environments that ETO companies can face and the ways of reacting to strategically fit the order-fulfilment strategy. Therefore, this research analysed the existing literature through a contingency theory lens and performed a multiple case-study research in a specific ETO sector, i.e. the machinery industry. The study identified five different order-fulfilment strategies implemented in the machinery industry to provide different product families to the market. For each strategy, the different environment characteristics were defined, and the performance outcome was measured, explaining the rationale for the positioning of the product families in different strategies. The findings of this study have two main contributions. First, the study contributes to theory by deepening and refining the analysis of contingencies for choosing different order-fulfilment strategies in the ETO context. Second, the study provides practical guidelines to ETO companies that want to adapt their order-fulfilment strategies to the unexpected or planned changes in their environment

Forecasting cycle time in semiconductor manufacturing systems: a literature review

An efficient and effective forecasting of production cycle times (CT) is a critical success factor in semiconductor manufacturing systems (SMS): inaccurate CT forecasts can have a negative impact on production scheduling, causing late deliveries, as well as on the amount of inventories and work-in-progress, which rapidly lose value over time because of the high risk of obsolescence. Therefore, since the 80s, several quantitative techniques have been developed to face this problem. Furthermore, Artificial Intelligence (AI) techniques are gaining importance, despite their potential is still not fully exploited even in the most advanced manufacturing systems. However, a synthetic overview of the techniques to forecast CT in SMS is still missing in the literature. As a result, it is difficult for decision makers to orient themselves and choose, among the many existing ones, the best model for their specific situation, comparing the different performance in terms of accuracy, data required, speed and easiness to use. This paper aims at presenting an overview of the quantitative techniques developed to forecast production CT in SMS. Firstly, a description of the methodology with which the literature review has been carried out is provided. Secondly, a taxonomy of forecasting techniques is proposed. Subsequently, a synthetic description of analytical, simulation, time-series and causal methods is presented. Within statistical techniques, a special focus is deserved to AI ones, since their popularity has dramatically increased in the last years. In particular, the most recent applications of artificial neural networks (ANN) in SMS – namely, hybrid methods and Long-Short-Term-Memory recursive neural networks – are described. Finally, a table with a qualitative comparison between the different methods is proposed.11-13 September 201

Complexity reduction and kaizen events to balance manual assembly lines: an application in the field

Notwithstanding the existence of a broad research base on assembly line balancing (ALB), companies do not use the mathematical approaches developed in the literature to configure assembly lines. This article aims to fill the gap between research and application by presenting and testing in a real industrial context a methodology based on complexity reduction and kaizen events. First, the methodology supports reducing the complexity that affects real-life assembly systems in terms of the variety of, e.g. finished products, materials and parts. Next, the methodology proposes the conduction of kaizen events by using lean manufacturing tools, such as process analysis, time observation, waste identification, workstation standard documents and yamazumi charts. The methodology is successfully applied to a case study that describes its use in the confectionery process for a major chocolatier company along with the results of the application. The main contribution of this paper consists in presenting a method to manage the line balancing activity within everyday industrial realities, helping practitioners to improve and maintain the performance over time

Performance improvement of manual assembly lines in a context characterized by complexity

The researchers' difficulty at transferring the scientific knowledge to practitioners and the existence of a gap among the methods developed in literature and the real life problem are issues recognized by literature and seems that they have not been solved yet. The aim of the present work is to outline a methodology that is successfully applicable to the industrial context, i.e. that is able to both take in account of the difficulty of managing a high number of different components and materials and assure the correct line balancing to gain performance improvement of the assembly lines as well as the maintain of such better performance over time. Among the techniques presented by literature, the "kaizen assembly" is exploited to perform the line balancing and to be transferred to practitioners. As the methodology addresses a typical industrial issue, it is outlined through its application to the case of an Italian plant of a chocolatier and confectionery company, leader in the market of premium quality chocolate. In order to get such a difficult result, firstly, a methodology to reduce the complexity is proposed and applied to the case study, by identifying groups of components distinct based on commonalities in the job elements and cycle times, rather than on part-IDs. Secondly "kaizen assembly" are performed and lean manufacturing techniques are applied in order to enhance the assembly lines performance and reach a set target. This allowed the company to achieve the performance objective and to maintain changes steady over time, confirming the validity of the approach