The benefits of additive manufacturing (AM) extend beyond the attributes of physical products and production processes they enable. Experience with AM can augment the way design is approached and can increase opportunities to pivot toward less familiar design tasks. We begin this qualitative study with a natural experiment made possible by an exogenous shock: the COVID-19 pandemic. Through a three-stage case study approach using a grounded theory-building method, we contrast AM usage among a set of firms, half of which pivoted their resources away from their traditional production and toward a response to this shock. We engage in an abductive reasoning approach to consider common threads in AM capabilities that facilitated this pivoting. Our analyses suggest that the advanced use of generative design (GD), a category of computational technologies enabling novel and optimized design, is a critical attribute of these firms that ended up pivoting to make COVID-related products. Specifically, firms with experience applying this capability demonstrated a unique ability to pivot during this shock and emphasized their valuation of AM-enabled agility. We revisited these firms 2 years after initial contact and found that GD was associated with higher levels of innovation and was largely viewed by designers as a mechanism driving double-loop learning. Overall, our study provides insights into the symbiosis between human and artificially intelligent GD, and the role of such symbiosis in advancing AM capabilities.

Advancements in the availability of sensor technologies and associated systems have given rise to new operational opportunities including enhanced preventive maintenance (PM). Notably, condition-based preventive maintenance (CB-PM), reliant on the richness and fidelity of signals concerning equipment quality, is poised to greatly benefit from the growing pervasiveness of these technologies. Yet the realization of these benefits depends on another factor as well: people. Even if optimal maintenance policies are devised, imperfect adherence to these policies can lead to avoidable expenses and more costly corrective maintenance, equipment downtime as well as increased safety hazards. In this study, we describe a normative model of optimal CB-PM policy determination; specifically, a generalized means by which to determine a value-maximizing quality threshold as a guideline for triggering PM. Motivated by data and observations from field cases, we consider the opportunity cost of not adhering to such optimal policies, and we highlight the tendency for individuals to prematurely order (or delay) preventive maintenance relative to the guidelines provided. With these tendencies in mind, we design and execute a controlled laboratory study, exposing workers to two critical manipulations that we theorize might influence adherence. The first is the presence of a supplemental secondary signal, of a type common to time-based preventive maintenance (TB- PM). The second is a pre-task priming intended to emphasize the value of discretized task completion, i.e., drawing attention to condition- and time-based signposts in otherwise continuous work. Our investigation shows that the combination of CB-PM and TB-PM signals, along with completion priming, significantly increases adherence to CB-PM guidelines. Returning to the normative model, we further demonstrate that individuals exposed to this combination of treatments forfeit far less value than those solely provided CB-PM-related signals. We conclude with implications for practice and future research.

Monitoring equipment conditions through abundant and accurate sensor signals for preventive maintenance (CB-PM) represents a critical application of the Internet of Things (IoT). However, the sociotechnical aspect of the system (i.e., the interactions between human actors and the equipment) cannot be disregarded. Additional investments made by equipment operators can also leverage sensor signals in ways, e.g., via user-facing dashboards, that reduce quality losses and equipment failures. This in turn can have notable impacts on optimal CB-PM policy selection. In this work, we model equipment quality degradation and failure using a Markov decision process, with CB-PM triggered when a maximum allowable level of equipment degradation is reached (𝑠̅). Our model lays the groundwork for identifying per-unit-time value-maximizing levels for such policies (𝑠̅∗). It also allows us to consider optimal variable investments in operator effort (𝑘∗), given a pre-existing user- facing technical infrastructure (𝑏). To investigate the model, we begin by analytically deriving some general dynamics anticipated with regard to our decision variables. The results motivate a numerical experiment to further assess the sensitivity of jointly (globally) optimal solutions. The results of that inquiry demonstrate infrastructural transition points beyond which a focus on optimizing operator effort dominates relative to efforts to optimize CB-PM.

The continued prevalence and growth in contingent worker staffing has raised various concerns regarding reliance on investments in this approach. Adopting a socio-technical theory perspective, we consider how operational practices, such as Total Productive Maintenance (TPM) might mitigate some of these concerns, and how they might complement specific high performance work practices (HPWP) among contingent workers. To address these questions, we conduct a multi-source field study encompassing a matched-sample of operations management managers and human resource managers from 76 manufacturing firms in Portugal. This data is paired with archival firm-level accounting and financial data from Informa D&B and IES (Informação Empresarial Simplificada) to develop insights into how combinations of TPM and HPWP are related to firm financial returns. We find that, under conditions of low autonomous TPM, firm investments to enhance contingent workers’ skill are positively associated with firm financial returns. Under these same low autonomous TPM conditions, efforts to enhance contingent worker empowerment are negatively associated with firm financial outcomes. Conversely under high autonomous TPM, contingent worker empowerment is significantly, positively related to firm financial returns. The inference based on these findings is that the benefits of either skill enhancement or empowerment are dependent on the nature of the TPM setting. Although autonomous and planned Total Productive Maintenance (TPM) can each have nuanced implications for firm financial performance, only autonomous TPM appears to consistency, positively moderate the financial impact of HPWP efforts. Implications of these results for theory development, practice and future research are discussed. Keywords: contingent work arrangements; total productive maintenance; higher performance work practices; firm financial returns, multi-source field survey.

Purpose: The integration of front-end technologies like automation, process simulation, and augmented reality with Just in Time (JIT) practices can yield unforeseen outcomes in manufacturing due to complex dynamics. This study examines the impact on this integration on the performance of manufacturing firms.

Design/methodology/approach: To conduct our investigation, we developed and administered a survey questionnaire among 353 Brazilian manufacturing firms. We utilized a combination of factorial and regression analyses within our final sample, consisting of 262 manufacturing firms. Our objective was to examine the impact of the alignment between front-end technologies and JIT on the operational and economic performance of these manufacturing firms. We adopted complexity theory as our theoretical framework, which recognizes and accommodates the intrinsic unpredictability and uncertainty inherent in complex scenarios such as technology adoption and its interplay with firm culture.

Findings: Our findings indicate that JIT influences the use of front-end technologies in both positive and negative directions. Regarding operational performance, JIT has a positive moderation effect on automation and a negative moderation effect on process simulation. Conversely, for economic performance, JIT positively moderates process simulation and negatively moderates automation. 

Originality: Our study advances complexity theory by elucidating the impacts of integrating front-end technologies with JIT practices within the unique scenario of the Brazilian manufacturing industry.

Practical implications: We provide empirical evidence for managers to carefully assess the compatibility of technology adoption strategies with existing operational practices to maximize performance outcomes.

A host of technologies help organizations keep an eye on the real-time state of their equipment investments. In equipment fleets, like those managed by warehousing and logistics firms, tracking the hours of equipment use, battery charge, and the flagging of mechanical and electrical issues are all facilitated through technology. Human judgment, of course, remains key to interpreting these signals. In this study, we consider the interplay of technology signaling and human judgment in a large fleet (warehouse forklift) setting. Notably, we consider how two types of signaling, both ostensibly associated with immediate perceptions of low risk, can contribute to a higher risk of equipment failure down the road. The first factor is the signaling generated when preventive maintenance (PM) on working forklifts is put off, such that it occurs after time-based planned points. The second involves signaling related to a forklift battery charge, and the risk that higher voltage might mask the severity of associated functionality deficits. In our analysis of approximately 6,000 maintenance events, we find that the postponement of PM is associated with less time afforded to PM, and that both postponement and battery charge predict increased risk of equipment failure after those PM activities.

Purpose: The provision of Industry 4.0 solutions demands a vast range of technology domains. To provide these solutions, small and medium-sized enterprises (SMEs) may need the support of different supply chain actors through an inbound open innovation strategy. The authors study the contribution of four types of supply chain actors for inbound open innovation: suppliers, competitors with complementary technologies, R&D centers and customers. The authors analyze how these four actors moderate the effect of integrated Industry 4.0 solutions on three main competitive strategies: cost, focalization and differentiation.

Design/Methodology/Approach: The authors conducted a survey on 77 SMEs from the automation sector, using OLS regression with moderating effects. They considered the integration of 15 technologies and 7 classic automation activities in the provision of Industry 4.0 solutions. The authors also studied three competitive outputs – technology cost reduction (cost), customer loyalty (focalization) and technology innovation (differentiation) – as well as four supply chain actors (moderators).

Findings: Expanding the provision of Industry 4.0 technologies increases customer loyalty and technology innovation. Collaboration with competitors (complementary technologies) leverage these results and reduce technology costs. Integration between customers and R&D centers elevates costs but R&D centers can foster long-run innovation.

Originality/Value: This study is the first to empirically investigate inbound open innovation in the supply chain for technology development in the context of Industry 4.0. The authors discuss how these actors contribute to four inbound open innovation activities: technology scouting; horizontal technology collaboration; vertical technology collaboration; and technology sourcing.

Purpose: Open innovation breadth (OIB) considers the diversity of external collaboration partners for innovation. The authors investigate the moderating effect of OIB on the relationship between industrial innovation activities (innovation inputs) and industrial innovation results (innovation outputs).

Design/methodology/approach: This study is based on secondary data from the Brazilian innovation survey, representing more than 30,000 innovative companies across 55 industrial sectors.

Findings: This study’s results show that OIB has different moderating effects regarding the several innovation input–output relationships. While OIB benefits some relationships, others are hampered by the diversity of collaboration partners.

Originality/value: Few studies have addressed OIB at the macro level. Using the perspective of transaction cost economics (TCE), the authors discuss the contributions and limitations of OIB at the industry level.

Some countries have chosen to focus on bottom-up initiatives to enhance the development of renewable energy systems (RES), using the local level (municipalities) as a pillar in this development. Municipalities need to expand their innovation policies to support such transition toward renewable energies. The triple helix (TH) model, based on university, industry, and government, can play an important role in supporting and establishing local policies for RES. We analyze the contribution by the TH actors to the development of three innovation policy criteria for RES development: Creation of cooperative systems, generation and transfer of knowledge, and development of municipal locational factors. Our results are based on a quantitative survey of 727 mid-sized and large municipalities from all regions in Germany. We provide empirical evidence of the relevance of the TH model to support these policy criteria. We also show that rather than treating the TH model as a single effect on RES development, each of the TH actors provides different contributions to RES policies. The government and the private sector have an important role in all three policy criteria. At the same time, we only found a contribution by universities to knowledge generation and transfer, but not to the two other criteria. Thus, in a developed context, the integration of government and private companies is a driving factor to create innovation conditions for RES development, while universities concentrate on creating structural knowledge for innovation in the RES context.

Purpose: The objective is to make a comparative analysis between different transportation modes used in the Metropolitan Region of Porto Alegre and to analyze its performance in relation to time, cost and carbon emissions.

Originality: Investigation of the urban mobility situation on the perspective of sustainability in a Latin American city, including the analysis of Uber pool, implemented in November 2018 in the metropolitan region analyzed.

Research Methods: A case study was carried out in the metropolitan area of Porto Alegre, as well as the definition of 4 different routes used as unit of analysis for different types of transportation modes.

Main Findings: Riding bicycle is a very convenient transportation mode in terms of its environmental perspectives and costs. However, the time of locomotion using bicycle is superior in relation to other modes of transportation. Therefore, riding a private car still stands out in the comparisons between different types of transport, since it is a faster mode of transportation.

Implications for Theory and Practice: As riding bicycle is a very convenient transportation mode, we highlight the importance of encouraging its use. People can benefit from practicing physical exercise while moving from one place to another and also benefit the environment from lowering carbon emissions.

Goal: The purpose of this paper is to present an analysis of Industry 4.0 concepts and technologies through the lens of practitioners and scholars with considerable expertise in this field.

Design / Methodology / Approach: A qualitative research was conducted based on seven semi-structured interviews guided by an open questionnaire, which was developed with the main goal of understanding the concept of Industry 4.0 and the technologies that compose this industrial phenomenon.

Results: Based on this methodology, a cognitive map is presented as the result and final product of this study. This cognitive map is composed of five different clusters, each one represented by different colors, which relate to each of the questions in the questionnaire. Our analysis provides a better understanding of (i) the main concepts of Industry 4.0; (ii) the implementation stages for companies; (iii) the main enabling technologies; (iv) the concept of M2M (machine-to-machine); and (v) the scenario in Brazil. Each one of the clusters enables a discussion by bringing what is in the literature on the topic.

Limitations of the investigation: The main limitations of the article are found in the subjectivity of the results and also in the scarcity in the literature related to some topics covered in the clusters.

Practical implications: This research can be potentially useful for practitioners, since it sheds light on the Industry 4.0 concept and how technologies are used to integrate processes.

Originality / Value: The clusters answers of the cognitive map allowed comparisons with the literature and a discussion about Industry 4.0, making it possible to direct and check gaps in the research.

Goal: This study aims to assess the impact of using the method of real options in investment analysis through a case study on a retail firm.

Design / Methodology / Approach: It was targeted the applications of the real options method in a different type of environment and it was compared to another method more commonly used, the discounted cash flow method (DCF). The implementation and assessment of the real options method was investigated by means of a case study conducted in an investment analysis in a retail units firm.

Results: The use of the real options method showed a more concise applicability over the DCF method. The results show that the project’s value, after the inclusion of managerial flexibility, increased significantly, which indicates that the analysis of the discounted cash flow undervalued the investment in question, since it disregarded the flexibility to expand or abandon the project.

Limitations of the investigation: The presented method is proper to long-term processes where it is possible to make changes during the project. Investments in this sector usually are more related to short and medium-term decisions, making the application difficult due to the short decision-making period available to the managers.

Practical Implications: The study provided the incorporation of flexibility through different pathways during the building project in a retail units firm. It was showed different scenarios where practitioners could decide among expanding, proceeding, reducing or abandoning the retail units based on the characteristics of their investments.

Originality/value: The results obtained are an indication of this methodology to industrial businesses that are relatively volatile and that need a certain degree of flexibility in order to burgeon, such as the case of the retailing sector.

Paper aims: This paper aims to understand the relationship between Information & Communication Technology (ICT), collaborative New Product Development (NPD) and customer satisfaction (NPD performance). 

Originality: We target the relationship between ICT, collaborative NPD and NPD performance. ICT is assessed as a set of specific tools adopted by the companies. 

Research method: We test the mediating role of collaborative practices in the effect of ICT tools on customer satisfaction (as NPD performance) by means of a survey of 105 Brazilian firms. 

Main findings: Collaboration with customers and suppliers has an important role for customer satisfaction and the use of ICT has significant effect on NPD performance through the mediating role of collaborative practices. 

Implications for theory and practice: Implementing only ICT tools is not enough to achieve higher level of success in NPD. Managers should first strength the relationship between stakeholders and then adopt ICT tools to support the cooperation. 

This paper proposes a methodology for the planning and execution of a Kaizen event in a manufacturing assembly line. The method consists in 3 steps each one divided into 2 others: Planning (Project + data collection); Execution (participants training and in loco analysis) and results (benefits and future propositions). The methodology was tested in 2015 during a Kaizen event conducted on a manufacturing lighting fixtures firm located in the industrial district of Milwaukee, WI, and applied to an assembly line whose problem consisted in excessive lead times and high mismatch times between workstations. The results show a successful application of this methodology. The Kaizen event promoted a lead time improvement of 17.8% and a reduction of 91.13% mismatch time between workstations, besides improvements regarding human aspects. In this way, we provide evidence of a powerful tool that can be used to help firms to get their own human resources to solve problems and improve the work environment.