Abstract

     The mission of the Institute of Industrial and Systems Engineers (IISE) is to serve those who solve complex and critical problems of the world. Notably, the research–practice gap in Operations Management (OM) marginalizes the value and relevance of the IISE. To maintain and enhance the impact of the IISE, we identify major bottlenecks that limit the industrial installation of OM research outcomes. Ranked by the relative importance, the three bottlenecks are verifying the performance improvement, building trust with practitioners, and balancing model accuracy and simplicity, respectively, in the stages of value verification, implementation and development. We propose potential research opportunities and illustrate the challenges and opportunities using real case studies from three Fortune Global 500 companies. In particular, we emphasize the role of data-driven decision methods in dealing with the three bottlenecks.

Introduction

     It is crucial for the Institute of Industrial and Systems Engineers (IISE) to guide Operations Management (OM) practitioners toward achieving OM practice excellence. The mission of the IISE is to serve OM practitioners who solve complex and critical problems encountered in the world, and often need to deal with complicated and integrated systems that suffer from customer dissatisfaction, excess inventory costs, and low product quality. Therefore, it is imperative for the IISE to capture the full potential of successful OM practice.

     However, the academia that studies OM has generally been following, rather than leading, business practices (Simchi-Levi, 2014). The inability to lead OM practices is often due to numerous failed implementations of OM models. After many years of OM projects with industry collaborators, we cannot emphasize enough how important a successful implementation is for a research project. Further, we are astonished by the difficulties associated with ensuring an OM implementation with precise and unified standards across different departments. Companies have been appropriately cautious about new OM models because they cannot fully reap its benefits (Ibanez et al., 2018; Sun et al., 2021).

Conclusions

     The future is promising as more OM research tends to bring the theoretical studies close to practical issues. The authors have extracted the data of all the 300 OM-related projects funded by the National Natural Science Foundation of China (NSFC) from 2016 to 2021. Table 1 categorizes the subjects of all the projects by new business model, new technology & Supply Chain Management (SCM) finance, product & behavior, green & health-care & agriculture, and traditional SCM. It is surprising to see that almost all the subjects are driven by real practices, and few project proposals even involve preliminary plans for industrial implementations.

     The academic community has always emphasized novel and mathematically provable methods and reproducible results, yet none of which are required for complex realworld issues (Sodhi and Tang, 2008). To thrive, the ISE research community should focus on overcoming the challenges that hinder successful practical applications and embrace cooperation with data-driven decision methods so that our research community will stay relevant and impactful. Combined with real case studies, we list and analyze the key challenges. Ultimately, these challenges can stimulate research opportunities such as user-centric algorithms and explainability. We hope that this article will encourage more ISE researchers to embark on research that can help close the gap when it comes to implementing new OM research ideas into practice.

1- Introduction

2- Conceptual framework

3- Methods and material of the literature review

4- Synthesis of study results

5- Discussion and conclusions

References

Abstract

Consumers play an important role in the market penetration of environmentally-friendly food packaging because it is they who decide whether or not to buy a particular product. The objective of this paper is to analyse the state of the art regarding consumers’ response to environmentally-friendly food packaging in order to identify existing barriers to purchase and potential measures to overcome these barriers. The paper is based on a systematic synthesis of 46 scientific journal articles on consumer studies related to environmentally-friendly packaging. The literature review applies a conceptual framework regarding the ways consumers respond to product stimuli and the psychological processes involved. Three important barriers to purchasing environmentally-friendly packaging are identified. First, consumers need guidance in recognizing environmentally-friendly packaging; for while consumers primarily consider the packaging material itself and any eco-labels, they also consider other packaging design elements such as colours and pictures of ‘nature’ that can be misleading. Second, it became obvious that consumers lack knowledge, in particular about new packaging materials like bio-based packaging. Third, many of the studies reviewed provide evidence that other product attributes such as price and product quality are more important to consumers than environmentally-friendly packaging. Nevertheless, some studies recorded a significantly higher willingness on the part of consumers to buy and pay for environmentally-friendly packaging and products with reduced packaging compared to products with standard packaging, signalling an overall positive attitude. The literature review revealed many research gaps. For example, it became obvious that consumers’ response to environmentally-friendly food packaging is not yet well understood, in particular with regards to purchasing behaviour (in the real world as opposed to in a survey setting) and measures for overcoming existing barriers.

Introduction

Human activities are causing irreversible environmental effects, such as climate change and loss of biodiversity (Rockström et al. 2009). A large part of each individual’s ecological footprint stems from their consumption of products. The production and consumption of more environmentally-friendly products is an important step towards achieving more sustainable lifestyles. At present, however, environmentally-friendly or ‘green’ products are still a niche market. Gleim et al. (2013) estimate the global market share for green products at less than 4%. According to FTSE Russell (2018), the green economy accounts for 6% of the market capitalization of globally listed companies. A serious side effect of product consumption is the generation of packaging waste. The global packaging market was estimated at 4,300 billion packaging units in 2015, of which 73% were for food and drinks (ALL4PACK 2016). In the European Union, 1,130 billion packages were used for food and drinks in 2018 (Fuhr et al. 2019). Since 2010, waste production has grown at an annual rate of 4.2% and is expected to continue at the same rate to 2024. Rigid and flexible plastic is the packaging material with the largest market share, at 47% in 2015 (ALL4PACK 2016). One way to tackle the waste problem is to introduce environmentally-friendly food packaging (Geueke et al. 2018).

1- Introduction

2- Literature review

3- The model

4- Comparative analysis

5- Conclusions and future research

References

Abstract

This paper is among the first that proposes a synchronization measurement model for the distribution centre operation synchronization (DCOS) problem, which aims to ensure the E-commerce order’s punctuality and synchronization at the same time. The main motivation of DCOS is that the intensified competition in E-commerce market makes efficient E-commerce logistics service extremely important, which means saving logistics cost and ensuring customer service at the same time. The synchronized operation may be a possible solution to ensure efficient order transhipment in the distribution center and to save cost. We thus introduce a measurement approach that is able to address the distribution center operation synchronization (DCOS) problem such as the trade-off relationship between synchronization and punctuality. In order to get persuasive conclusions, we adopt data from a real practice case and apply CPLEX to get the optimal solution. Our computational results show that considering the asynchronous cost in the total cost objective function will greatly improve the operation synchronization in the distribution center, by saving the storage space, the equipment, and the labour resources. And if the storage cost is in a reasonable range, the synchronized operation can be realized while the punctuality is also optimized. It is found in our case that the most efficient way to improve distribution center operation is expanding inbound operation capacity.

Introduction

The fierce competition in the E-commerce business has triggered a strict service-oriented market. As a result, the E-commerce logistics service provider has to face the challenge of providing reliable and customized delivery service to meet diverse requirements from the huge E-commerce customer base while keeping operation costs low. And the logistics service for e-commerce order fulfilling becomes a bottleneck in the industry because of followed reasons: The logistics providers have to deal with enormous order fulfillment tasks at one time as millions of simultaneous order buttons are clicked online; the logistics providers need high flexibility of processing capacities to meet the explosive order demands which occur during festival and special selling days; the logistics providers are faced a complex order fulfilling scenario, as the ecommerce suppliers and customers are geographically dispersed and the e-commerce orders are always small but with high varieties. Successful E-commerce businesses rely heavily on efficient logistics services because of the enormous number of small-batch orders relating to geographically dispersed customers and suppliers who have high expectations regarding service [27]. And it is hard for logistics providers to expand their capacity at a short time to adapt the quick growth order processing requirements. So, how to use existing resources more efficiently is a more feasible solution. In this paper, we provide synchronize as a new solution to make more effective operation and fluent order transshipment in distribution center. The distribution center is one of the key transhipment nodes within the logistics service network [17,4]. As shown in Fig. 1, bulky packages are transported from separate suppliers to the distribution center [61]. Here the packages are broken down into smaller units at a receiving area. These are then processed in a sorting area where products are picked according to the order information. According to their destinations, the unit products are now recombined in a consolidation area and loaded onto a specified outbound truck in a shipping area for last mile delivery. Most research points out that synchronization is the critical factor in finding an optimal solution for distribution center operations [60].

1- Introduction

2- Literature review

3- Problem formulation

4- Solving method

5- Model implementation and numerical results

6- Conclusion

References

Abstract

Medical Supply chain network design is one of the crucial planning problems that if solved efficiently and effectively, can alleviate the dangers arising from the accumulation of wastes. Furthermore, the harmful effects of medical waste, with their infectious nature in the environment, have increased justifies the significance of this research. In this article, forward and reverse supply chain network design for consumer medical products is considered taking into account the biological risks. The proposed model has two objectives: first, maximizing the profit obtained from subtracting the costs from the revenues of the supply chain. Revenues are gained from selling medical supplies to hospitals and medical wastes to recycling centers, as well as getting the permission to use sterilize services from clinics lacking a sterilization center. The costs include activation cost of inventory, sterilization and collection centers, transportation cost, purchasing cost of medical supplies, municipality cost for destruction of medical waste, etc. The second objective is to reduce biological risks. The primary source of infectious medical supplies is from disposable supplies and equipment. Thus, the second objective function is to minimize the distance and travel time between the clinic and the sterilization center. The proposed model is implemented in hospitals in Tehran’s 4th municipal district using the Bounded De Novo Programming approach which initially attempts to redesign rare sources of the problem. The issue has two types of resource and policy constraints. The policy constraints of the problem cannot be changed, which is why the bounded De Novo Programming approach is used to solve the problem. Redesigning the capacity of the sterilization center leads to the decrease of biological risks, and in addition to can causes to increasing profitability. Also, by raising the productivity of medical waste in the recycling process, the distribution center can receive more profits.

Introduction

In the past, producers and consumers were not responsible for the products used. In other words, they were careless of the post consumption process. Many of the consumed and expired products were out of place, regardless of the extent of their return. They saw a fate of including burial, burning, or destruction ahead of them. Each of the above operations would undoubtedly impose destructive effects and damage to the surrounding environment. Now, the point of view of the advancement and development of countries, people, and the government are expecting to pay attention to their funds (Guo et al. 2019). Therefore, the producers plan to reduce the production of their defective goods. Thus, organizations are working to prevent more of the waste by devising an effective and efficient system in distribution network management. In turn, it includes determining the role of existing or potential supply chain resources as well as the interactions between these entities to manage the physical flows related to the recycling of products (Rajendran and Ravindran, 2019).

1- Introduction

2- Literature review

3- Problem definition

4- Model assumptions

5- Modeling

6- Solution approach

7- Conclusion and future research

References

Abstract

In recent years, Supply Chain Management (SCM) has been the main focus in many industries in order to decrease costs and improve efficiencies. In today’s supply chains, sale determination for a product in a specific time period and for a specific customer is highly considered. The importance of this efficiency is increased when dealing with perishable products, such as blood, vaccine, foods, etc. These products not only have various customers, but they also must be used before perishing. This paper represents a model to determine the optimal sales level of perishable products in a two-echelon supply chain, using a Vendor Managed Inventory (VMI) policy. Based on the literature most studies have considered two-echelon supply chain for perishable products with one buyer. The proposed model is formulated based on one vendor and multiple buyers. Considering buyers’ requests in various periods, this model aims to optimize the sales profit by exact and meta-heuristics methods. Three efficient meta-heuristics including GA, PSO and CPSO are utilized to solve the problem and the results showed the good consistency with the results obtained from exact method. The obtained results show that applying VMI policy on two-echelon supply chain for perishable products is an effective approach to optimize the profitability of the proposed network.

Introduction

Perishable products have made companies consider different factors for chain management and network logistics because of their soon expiry date. Moreover, the costs of perishable products and the subsequent failure to deliver them to customers are very high. In addition, many expenditures before the product reaches to the final consumer are done, including costs relating to the manufacture, warehousing, inventory, and transportation. However, these perishable products will no longer be usable after their expiry date. To illustrate, in 2010, one state in Canada suffered a two-million-dollar financial loss over a six-month period of time because of the perished influenza vaccine H1N1. The deterioration of perishable products makes these products to be consumed within their shelf time period. This characteristic result not only in direct loss of these products, but also reduces the potential customers and the acceptability of these items. In addition, the results of studies have shown that there is a significant demand for these products in recent years and its growth (see figure 1). Fig. 1. shows value of U.S. product shipments of perishable prepared food to customers. According to the report, the value of U.S. product shipments of perishable prepared food amounted to approximately 14 billion U.S. dollars in 2016.

1- Introduction

2- Literature review

3- Research methodology

4- Results, analysis, and discussion

5- Concluding remarks, limitations, and future research

References

Abstract

Little is known about the effects of lean manufacturing practices on the process innovation performance of manufacturing organisations. This research aims to fill this gap and explore the aforementioned interdependency. A research framework consisting of 22 measurement scales and three pairs of hypotheses was developed based on an extensive literature review. A large-scale self-administered questionnaire was distributed among appropriately selected industrial experts. Datasets obtained from 340 usable responses were analysed through confirmatory factor analysis, descriptive statistics, correlations, and multiple linear regression models. The findings suggest that both technical and human lean practices have a moderate to strong positive impact on the input and occurrence of incremental and radical process innovation in manufacturing organisations. In turn, as an output of process innovation, this appears to enhance companies’ operational performance. Thereby, the results dispel the scholarly and managerial misconception that LM and innovation are difficult to coexist.

Introduction

Over the last 30 years, lean manufacturing (LM) has provided some of the most popular and effective practices and tools for striving towards operational excellence (Albliwi, Antony, & Lim, 2015). In essence, LM aims to eliminate any non-value-adding activities through continuous incremental improvements (Abolhassani, Layfield, & Gopalakrishnan, 2016). Global megatrends have accelerated the pace of technological developments, increasing the importance of dynamic operations (Westkämper, 2014). In this environment, merely optimising the exploitation of innovation is not enough. Companies are challenged to enhance their explorative innovation capabilities to ensure sustainable competitive advantages (Chen & Huang, 2009; Kafetzopoulos, Gotzamani, & Gkana, 2015). In addition to managing existing products and services, they must incrementally and radically innovate in technology and processes to outpace global competition (Westkämper, 2014). On one hand, exploitation and exploration seem contradictory (Berente & Lee, 2014; Jones & Linderman, 2014; Pakdil & Leonard, 2017). By principle, standardised lean environments appear to be the opposite of increased worktime flexibility and colourful creativity rooms. Similarly, structured and streamlined workflows associated with LM seemingly contradict the need of freedom to innovate. Pakdil and Leonard (2017) outline a paradox of LM: while employees are encouraged to continuously innovate autonomously, guidelines and work regulations provide strict rules for their operations. Spear and Bowen (1999) also describe the Toyota Production system as a paradox between rigid processes and flexible responsiveness. Jones and Linderman (2014) call this a trade-off between innovation and efficiency of manufacturing plants. However, LM is centred on improvement (Jasti & Kodali, 2015), and increasing innovativeness is occasionally presented as an important mechanism for LM (Lyons, Vidamour, Jain, & Sutherland, 2013). In addition, various human resource (HR) oriented LM practices (e.g. diverse training, teamwork, and flatter hierarchies) (Olivella, Cuatrecasas, & Gavilan, 2008) are often listed among general attributes of innovation fostering environments (Dul & Ceylan, 2014; Fiates, Fiates, Serra, & Ferreira, 2010). Together, the outlined contradictions raise the question as to whether, and in which way, the implementation of LM affects the ability of manufacturing organisations to generate process innovations.

Introduction

Experimental Procedures

References

SUMMARY

The significant capital expense of photovoltaics manufacturing has made it difficult for new cell and module technologies to enter the market. We present two technoeconomic models that analyze the sustainable growth of perovskite manufacturing for an R2R single-junction technology and a perovskite-silicon tandem module, focusing on the impacts of economies of scale and average selling price on profitability. We establish a cost range of $3.30/W to $0.53/W for flexible modules manufactured in factory sizes ranging from 0.3 MW/year to 1 GW/year. In addition, we model the cost to manufacture a tandem module consisting of a single-junction perovskite cell stacked in 4-terminal configuration onto a silicon cell and show how an existing manufacturer can grow at a faster rate by co-investing in tandems. Our analyses highlight potential routes to market for perovskite photovoltaics and the possibility to sustainably grow a photovoltaics manufacturing company even in markets with higher labor rates.

INTRODUCTION

To mitigate the impacts of climate change, tens of terawatts of solar power must be deployed over the next decades. With 500 GW of photovoltaics (PVs) installed globally to date, silicon photovoltaics remains the incumbent technology with its cost now at 0.25 $/W and declining capex. The rapid growth of installed photovoltaics continues to surprise even the experts, but for solar power to become the primary source of electricity globally, a large-scale and truly global manufacturing base is required that will not be able to rely on one region or technology. In light of this, perovskite photovoltaics offer a strong alternative photovoltaic technology with the potential for extremely low manufacturing costs through solution processing that could compete with silicon. However, new cleantech technologies have historically struggled to scale-up with their capital intensity resulting in long timelines for commercialization that are incompatible with traditional venture capital funding models, that lead to lower success rates for cleantech startups compared to software and medical ventures. In this paper, we use bottom-up cost modeling to explore economically sustainable strategies for one new cleantech innovation, solution-processed perovskite photovoltaics, to scale-up and enter the mature solar power market. Our goal is to help illuminate one or more pathways that could enable this groundbreaking technology to successfully scale-up and navigate the journey from lab bench to market.

1- Introduction

2- Related works

3- Ramp-up Decision Support System

4- Experimental procedure and results

5- Discussion

6- Conclusions

References

Abstract

Production ramp-up is a key phase during the introduction or changeover of a production system. Process calibration and tuning are inevitably required to make such a system fully operational and let it reach its maximum production yield. A complex decision-making process takes place in order to optimally tune the system and requires a long time for testing and experimenting that will determine the system behaviour. This work considers the sequential nature of ramp-up and proposes a Cyber-Physical Systems approach based on data capturing, learning mechanisms and knowledge extraction, leading to an Industry 4.0 compliant Decision Support System (DSS) for human operators. The proposed system is implemented as an online DSS and also supports offline learning using previously gathered knowledge. A number of experiments have been carried out on a micro scale assembly station, validating the expected benefits of the proposed DSS. Results show a reduction of over 40% in the number of ramp-up steps required when using the DSS.

Introduction

Nowadays, production systems (PSs) are subject to rapid technological changes, which led to very short product lifecycles and high variety of products, to meet the growing demand of product customization (Kalir and Rozen, 2018). In addition to that, PSs becomes more modular and able to change from one configuration to another (Colledani et al., 2018). The rapid introduction of new products, mostly complex, and changeover cause a prolonged and more frequent production ramp-up (Letmathe and Rößler, 2019). Ramp-up phase is very important for PSs because having high outputs with good quality and fast cycle time are the key factors for increasing profits, where at the beginning of product life-cycle product prices are the heights and competition is scarce (Kalir and Rozen, 2018). Production ramp-up is a decision-making process where human experts decide on the best actions to fine-tune the process. It is a highly complex parameter tuning process with many interrelated factors leading to a well-defined goal. Studies on ramp-up report long lead times of up to a few months from the initial sys-tem built to full production (Terwiesch and Bohn, 2001). Since the product, PS, and the supply chain are new, uncertainty is very high, making the ramp-up process very difficult to manage (Hansen and Grunow, 2015). Also, ramp-up process is unstable, expressed in the unpredictability of the system at this stage, which makes planning proactive steps to avoid some problems ineffective for many ramp-up problems (Schuh et al., 2015).

1- Introduction

2- Uncertainty theory

3- Models

4- Equivalence proof of uncertain models

5- Numerical experiments

6- Conclusions

References

Abstract

In the present study, a closed-loop supply chain network for production and recovery of button batteries is investigated under uncertainty. The environmental impact of button batteries is taken into account in the design of the supply chain network. Since there are many uncertainties in reality, the demand, cost and capacity are considered uncertain variables. To explore the impact of these uncertainties on the supply chain network, two multi-objective mixed integer programming models under uncertainty are developed, i.e., the expected value model and the chance-constrained model. The aim is to reduce the multiple environment effects on the total cost and weigh the pros and cons. A method based on life cycle assessment is proposed to evaluate the environment effects on the supply chain network. The two models can be converted into crisp models by the uncertainty theory. Lastly, numerical experiments are used to verify the feasibility of the proposed models and method.

Introduction

An efficient supply chain network is the embodiment of an enterprise’s ability, which makes it competitive in today’s market. The impact of commercial activities on the environment has led to government legislation and the enhancement of consumer awareness of environmental protection. Therefore, the people and government exert pressure on enterprises to reduce the impact of production and operation on the environment. In recent years, supply chain management with environmental protection has attracted great attention from researchers and scholars owing to these factors. This kind of supply chain incorporates environmental pollution into the design process, from product design to scrap. Obviously, supply chain network design plays an important role in the environment. For instance, scrapped products have a significant impact on the environment, numerous literature were devoted to the management of these products. Research showed that it is necessary to establish reverse supply chain model. It involves determining the number of required collection, recycling, recycling and disposal centers, their location and capacity, and the material flow. Mathematical models were used to studied this issue.

1- Introduction

2- Theoretical background

3- Conceptual model and hypotheses

4- Methodology

5- Data analysis and results

6- Discussion and conclusions

References

Abstract

While an increasing number of firms outsource their logistics activities, the failure rate of logistics outsourcing is still high. To address such issue, this study draws upon transaction cost theory (TCT) and extended resource-based view (ERBV) to examine the mechanism through which transaction attributes (i.e., 3 PL providers’ asset specificity and logistics technological uncertainty) affect logistics outsourcing success. We argue that top management plays an essential role in mediating transaction attributes to exercise their influences on logistics outsourcing success. However, such a mediating mechanism is effective only when trustworthy alternative 3 PL providers are available in the market. The data from 250 manufacturing subsidiaries in China are used to test our model. Important implications for research and practices are discussed.

Introduction

With the globalization, more manufacturers outsource their logistics activities to third-party logistics (3 PL) providers, so that improve their logistics performance (Lai et al., 2008). By doing so, 3 Pl user firms expect to focus on core competencies, save logistics costs, improve customer services, and thus enhance competitiveness (Chu and Wang, 2012; Lai et al., 2008; Zhu et al., 2017). However, along with such advantages, such outsourcing risks as losing control of logistics activities, information leakage, and additional efforts and costs on outsourcing management also exist (Gadde and Hulthén, 2009; Lau and Zhang, 2006; Yang et al., 2016). Logistics outsourcing does not always succeed; rather, the failure rate of logistics outsourcing is relatively high (Yang et al., 2016). Due to the high risk of failure, some large firms in China (e.g., JD.com and Alibaba) even abandoned logistics outsourcing but internalized logistics activities by building their own logistics systems (Zhu et al., 2017). Therefore, how to achieve logistics outsourcing success is an important issue for both 3 PL users and providers. Previous studies suggested that building quality logistics outsourcing relationship can mitigate outsourcing dependence and risks, as well as help firms improve operational and financial performance (Chu and Wang, 2012; Huo et al., 2015; Lai et al., 2013a; Qureshi et al., 2017). However, operational and financial performances are not the direct outcome of logistics outsourcing. Those two measures are distant from logistics outsourcing and thus could not accurately capture the outcome of logistics outsourcing. Rather, as Han et al. (2008) suggested, logistics outsourcing success, referring to the overall advantage attained from logistics outsourcing, should be assessed. Logistics outsourcing success could be assessed from a broader perspective, including strategic, technological, and economic benefits. While most previous studies only focused on a sole and isolated aspect (see Appendix A), further studies are needed to examine logistics outsourcing success from a broader perspective. Therefore, the present study aims to examine how to leverage transaction attributes in logistics outsourcing for overall outsourcing success.

Little is known about the effects of lean manufacturing practices on the process innovation performance of manufacturing organisations. This research aims to fill this gap and explore the aforementioned interdependency. A research framework consisting of 22 measurement scales and three pairs of hypotheses was developed based on an extensive literature review. A large-scale self-administered questionnaire was distributed among appropriately selected industrial experts. Datasets obtained from 340 usable responses were analysed through confirmatory factor analysis, descriptive statistics, correlations, and multiple linear regression models. The findings suggest that both technical and human lean practices have a moderate to strong positive impact on the input and occurrence of incremental and radical process innovation in manufacturing organisations. In turn, as an output of process innovation, this appears to enhance companies’ operational performance. Thereby, the results dispel the scholarly and managerial misconception that LM and innovation are difficult to coexist.

Introduction

Over the last 30 years, lean manufacturing (LM) has provided some of the most popular and effective practices and tools for striving towards operational excellence (Albliwi, Antony, & Lim, 2015). In essence, LM aims to eliminate any non-value-adding activities through continuous incremental improvements (Abolhassani, Layfield, & Gopalakrishnan, 2016). Global megatrends have accelerated the pace of technological developments, increasing the importance of dynamic operations (Westkämper, 2014). In this environment, merely optimising the exploitation of innovation is not enough. Companies are challenged to enhance their explorative innovation capabilities to ensure sustainable competitive advantages (Chen & Huang, 2009; Kafetzopoulos, Gotzamani, & Gkana, 2015). In addition to managing existing products and services, they must incrementally and radically innovate in technology and processes to outpace global competition (Westkämper, 2014). On one hand, exploitation and exploration seem contradictory (Berente & Lee, 2014; Jones & Linderman, 2014; Pakdil & Leonard, 2017). By principle, standardised lean environments appear to be the opposite of increased worktime flexibility and colourful creativity rooms. Similarly, structured and streamlined workflows associated with LM seemingly contradict the need of freedom to innovate. Pakdil and Leonard (2017) outline a paradox of LM: while employees are encouraged to continuously innovate autonomously, guidelines and work regulations provide strict rules for their operations. Spear and Bowen (1999) also describe the Toyota Production system as a paradox between rigid processes and flexible responsiveness. Jones and Linderman (2014) call this a trade-off between innovation and efficiency of manufacturing plants.