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.

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.

The literature discusses data science (DS) as a very promising set of techniques and tools to support lean production (LP) practices. DS could aid manufacturing companies in transforming massive real-time data into meaningful knowledge, increasing process transparency and product quality information and supporting improvement activities through data-driven decision-making. However, no attempt has been made in the literature to formalise the links between DS and LP practices. Thus, this study aims to overcome this gap by clarifying the DS techniques and tools that can support LP practices and how to apply them. This study employs a quantitative bibliometric method-specifically, a keyword co-occurrence network analysis-on a set of papers extracted from Scopus. The results obtained allowed the researchers to identify a set of DS techniques and tools that can support LP practices and to develop a model to guide their implementation based on the typical improvement implementation stages of the plan-do-check-act cycle. The model shows how to use DS techniques and tools in LP for: identifying areas for improvement and subsequent implementation (plan); enabling a better knowledge and process management (do); identifying/predicting potential problems and employing statistical process control (check); providing remedial actions and effectively applying process improvement (act).

Thi s v e r sio n is b ei n g m a d e a v ail a bl e in a c c o r d a n c e wit h p u blis h e r p olici e s.S e e h t t p://o r c a . cf. a c. u k/ p olici e s. h t ml fo r u s a g e p olici e s. Co py ri g h t a n d m o r al ri g h t s fo r p u blic a tio n s m a d e a v ail a bl e in ORCA a r e r e t ai n e d by t h e c o py ri g h t h ol d e r s . Determinants for Order Fulfilment Strategies in Engineer-to-Order companies: insights from the Machinery Industry

No abstract