Advances in Production Management Systems. Towards Smart Production Management Systems

Solid waste management has been an interesting topic for researchers in few last decades. This paper studies a price-sensitive demand for the waste disposal service of two disposal facilities who are dealing with a contractor to gain more profit. The waste disposal process is studied in a supply chain structure where a contractor manages to collect the waste from the producers and transport them to the facilities for disposal. Two scenarios are proposed where at the first one the disposal facilities lead a price Stackelberg game over the contractor, and in the second scenario, both disposal facilities and the contractor cooperate on the chain decision variables in an integrated framework. A numerical example is performed to illustrate the efficiency and applications of the proposed model.

This research improves understanding of the impact of specific types of truck driver behaviour and temporal scheduling on the effectiveness of a terminal appointment system. A discrete event simulation model of a bulk cargo marine terminal is developed to analyse parameters related to driver behaviour (punctuality and proportion of planned appointments) and temporal scheduling (appointments per time window and time window spacing) on truck flows and turnaround times at the terminal. The model is based on an Australian wood chip export marine terminal currently experiencing significant truck congestion. The terminal operator and stakeholders have expressed interest in the implementation of an appointment system to address this issue. The modelling presented in this research was used to inform their investigation into developing an appointment system solution.Simulation results indicate that the proportion of planned appointments, used as a proxy for the appointment system use, has a significant impact on truck turnaround times. Greater truck arrival punctuality only marginally improves truck turnaround times. Interestingly most optimization approaches continue to focus on improving punctuality through service rules or financial penalties in order to achieve optimal turnaround times. However, the additional cost in terms of complexity or assumptions for optimal solutions against non-optimal approaches are rarely weighed in terms of dividends of the marginal improvements generated. By involving terminal users (drivers and transporters) in the design of an appointment system and its scheduling parameters, terminal operators can significantly improve appointment system use and effectiveness by increasing the probability of positive users’ behaviours.

This paper presents the use of advanced simulation modeling to optimize the operation of a fully automated modular conveyor system in a large-scale warehouse. At its peak capacity, the smooth flow of material in the system was greatly impaired due to the appearance of bottlenecks. A full-scale 3D discrete event simulation (DES) model of the system was built and time-dependent statistical models were carefully designed and implemented in the model in order to capture the randomness and complex dynamics of the operation. The model was verified and validated, and several scenarios have been analyzed. The paper demonstrates a practical example of how data-driven simulation modeling provided a cost-effective solution to enhance efficiency. The paper highlights the crucial aspects that must be taken into account in the modeling of the system in order to create a reliable standalone decision support system. Moreover, the paper highlights the identified key steps that are yet to be taken from dynamical modelling towards a Digital Twin.

Decision making in production network design is complex due to a large number of influencing factors, options and uncertainties. Furthermore, the agility in production networks and therefore the decision demand increases while made decisions are often hard to revise. Hence, a fast yet holistic decision-making process is key for sustainable production network development. While many existing approaches target the overall network optimization, few of them include a systematic approach to cover uncertainty and barely any approaches cover the uncertainty of information and models used for the decision-making. In praxis, these approaches result in unsystematic and time-consuming decision-making processes. This paper presents an approach to take uncertainty systematically into consideration and splits it into internal uncertainty, which can be reduced by the decision maker, and external uncertainty, which has to be considered in the sensitivity analysis. The method was applied for the site selection of an automotive supplier.

Current supply chain structures in the spare parts logistics are changing profoundly due to the influence of digitalization and additive manufacturing (AM). In particular the Logistics Service Provider (LSP) is influenced by the change, as the physical transport of goods could become redundant due to the digital transmission of production data. This leads to a reduction of the LSP’s share in the value chain. Conceptualizing a new role for the LSP for additively manufactured spare parts is necessary. Therefore, five different scenarios are identified in which the LSP serves as a transport carrier, digital distributor, an AM decision maker, a selector of the manufacturer and as an AM service provider.

Recent research underlines the crucial role of supply chain optimization, in terms of maximize profit and minimize cost. Today the stakeholders are also empowered and the organizations are becoming stakeholder-centered, relates to the main objectives of a supply chain are availability and inventory control so the particular aim for availability must relate to stakeholder satisfaction. The implementation of supply chain optimization in tire industry nowadays not only focuses on profit, but also on the environmental and societal effect that is considered as ways to achieve the sustainable supply chain and stakeholder satisfaction. Currently a wealth of literature on supply chain optimization with maximize profit and minimize cost, to the best of our knowledge there is limited state-of-the art review on supply chain optimization considering with economy, environment and stakeholder satisfaction. This manuscript analyze research stream on supply chain optimization with economy objectives such maximize profit and minimize cost, environmental effect and stakeholder satisfaction with the aim to relate the existing optimization methods to empirical research and reveal the conceptual framework. The paper classifies existing research streams and application in tire industry areas with different optimization subject. The results of this study gives outlook which optimization methods are available for supply chain managers and give a conceptual framework in tire industry considering sustainable supply chain factors from economic, environmental and societal effect.

Reducing the volume of trucks carrying empty or below capacity loads on road networks are both socio-economic and environmental sustainability issues for the logistics industry. Planning concepts for a collaborative logistics exchange based on real-time data are described as well as the benefits in terms of optimizing load capacity utilization, minimization of empty running, reducing costs, traffic congestion, and truck emissions.

This paper presents a methodology combining a flow optimization and a cost models in order to, simultaneously, realize a tactical planning of a productive system, and evaluate the financial performance of the proposed plans. The system addressed is a multi-site, multi-product supply chain structure with finite capacities of production, storage and transport. In order to model physical flow, we propose an optimization model taking into consideration all the physical system’s constraints. It calculates production and transport plans while maximizing demand satisfaction rate. Then, in order to financially evaluate the solution found by the optimization model, we propose a cost model using Activity Based Costing (ABC) as a valuation method using cost drivers mechanism. Finally, in order to couple both optimization and cost models in a global integrated model, we use an approach called PREVA for PRocess EVAluation, generally used to set up a supply chain’s management control system using financial and physical metrics.

Recent technology allows UAVs to be implemented not only in fields of military, videography, or logistics but also in a social security area, especially for disaster management. UAVs can mount a router and provide a wireless network to the survivors in the network-shadowed area. In this paper, a set covering problem reflecting the characteristics of UAV is defined with a mathematical formulation. An extended formulation and branch-and-price algorithm are proposed for efficient computation. We demonstrated the capability of the proposed algorithm with a computational experiment.

After a large-scale disaster, the emergency commodity should be distributed to relief centers. However, the initial commodity distribution may be unbalanced due to the incomplete information and uncertain environment. It is necessary to rebalance the emergency commodity among relief centers. Traffic congestion is an important factor to delay delivery of the commodity. Neither the commodity rebalancing nor traffic congestion is considered in previous studies. In this study, a two-stage stochastic optimization model is proposed to manage the commodity rebalancing, where uncertainties of demand and supply are considered. The goals are to minimize the expected total weighted unmet demand in the first stage and minimize the total transportation time in the second stage. Finally, a numerical analysis is conducted for a randomly generated instance; the results illustrate the effectiveness of the proposed model in the commodity rebalancing over the transportation network with traffic congestion.

Rapid population growth and increasing demand of transportation necessitate more efficient transportation and logistics processes. Efficient logistics processes in a supply chain can help the supplier selection procedure be more proficient in terms of delivery time. This paper studies a three-stage supply chain which enables truck sharing for delivery. All suppliers and the manufacturer have a time window for loading and unloading the material. A nonlinear programming model is developed to find the optimal truck share among different suppliers. A numerical example shows the applicability of the proposed model.

This paper proposes a new manufacturing business model that is enabled by a novel scheduling method based on an auction, called Scheduling Auction. The proposed scheduling method and the associated business model aim to balance expanding customization and quick delivery to better satisfy customers. It accepts bids from customers, captures their preferences in terms of product type and due date more extensively than before from the bids and reflects those preferences in the production schedule. The fundamental framework of the method and its working are illustrated with a simple case of a factory with a single machine. Future research directions include conducting validation experiments with human subjects and extending the method to more complex production systems.

Even providing the means for human displacements, passenger transport causes disadvantages that can be called disutilities, such as time and money spending, insecurity and discomfort, and, negative impacts on communities. From the National Transportation Statistics, it is possible to measure passenger transport disutilities and reaches some conclusions that can help planning and public policies of the country. The results show that Americans are wasting more time and spending more money on their cars since the 1990s. Insecurity related to traffic in all modes of transportation has decreased significantly, and the discomfort in automobiles may have experienced an increase due to improvements in the infrastructure. America is lowering its per capita emissions of local gases, but there is insufficient data for conclusions regarding the greenhouse gases.

The continuous increase in global population and living standards, is leading to an increase in demand for food and feed resources. The world’s oceans have the largest unlocked potential for meeting such demands. Norway already has an extensive aquaculture industry, but still has great ambitions and possibilities to develop and expand this industry. One of the important topics for improving the value chain of Norwegian aquaculture is to secure the access to feed resources and to improve the environmental impacts. Today, most of the feed-protein sources used in aquaculture are imported in the form of soy protein. The research project Energy efficient PROcessing of MACroalgae in blue-green value chains (PROMAC) aimed, among other research questions, to investigate cultivated seaweeds as a potential raw material for fish feed. This paper assesses Life Cycle Analysis (LCA)-perspectives of scenarios for future seaweed production of feed-protein for fish and compares this with today’s situation of imported soy protein for fish feed. The insights from the LCA are very important for the configuration of the entire production value chain, to ensure that the environmental aspects are taken into account in a holistic fashion.

The manufacturing industry has to withstand an increasing competition requiring customization of products, shorter time to market and a transition towards more sustainable operations and products. There is a need for a transition to business models that incorporate sustainability while keeping business activities profitable. Leveraging the advantages of new technologies within the concept of Industry 4.0 is seen as an important factor to maintain competitiveness while responding to the sustainability challenge. Changing the way businesses operate is not easy as is evident from studies that have identified many barriers, including costs, lack of competence, loss of jobs, and process, product or production facilities not suitable for Industry 4.0. Due to these barriers, firms are slow to make a transition towards customized products, shorter lead times and more sustainable operations and products. The commitment for sustainability includes a shift towards Circular Economy (CE) that poses additional barriers like geographic dispersion, product complexity, and lock-in to the contemporary linear ‘take-make-consume-dispose’ model of operation. This paper addresses how manufacturers perceive Industry 4.0, what motivates their investments in Industry 4.0, and what barriers they see in adapting Industry 4.0 followed by a literature review identifying barriers for adhering to CE in the manufacturing industry sector. The study offers empirical insights identifying a need for a roadmap for implementation of Industry 4.0 to support CE as well as providing directions for future research.

Business model has the potential to create value and capture value for companies, which is critical for their sustainable development [1]. The concept of eco-efficiency can be a useful concept to link an enterprise’s business with sustainable development as well as achieving long-term profits [2, 3]. Extant literature reveals that there is a need to study business model innovation and eco-efficiency under one text to achieve a win-win rationale to increase profits while reducing environmental impact [4, 5]. This empirical study conducted 8-in-depth case studies with manufacturing companies across UK and China. The author synthesized the cases and concluded the measures of business model innovation for eco-efficiency in five categories, namely (1) Selling of service model, (2) Direct selling model, (3) Collaboration strategy, (4) Whole system design strategy, and (5) Technology renovation strategy. The empirical finding suggests the adaptation of strategy and exploitation of the technologies are essential to business model innovation when manufacturing companies seeking to implement eco-efficiency.

United Nation’s 2018 Water Development Report estimated that more than 2 Billion people all around the world lack access to clean drinking water. In addition, many of our freshwater sources are declining. Therefore, exploring new methods of collecting clean drinking water is vital. Use of Atmospheric Water Generators (AWG) is one of these methods with the potential to contribute towards the salvation of our water problems. However, AWG’s performance is quite volatile in different environmental conditions and its economic feasibility is questionable. In this paper, an indicator model is developed to predict AWG’s performance in different conditions. This model is then used to examine the performance of AWG in Austin, Texas during a 4 year period. An economic analysis is carried out on the performance of the AWG system for a 4 year period from a single users’ perspective that exhibited an NPV value of $5964. This analysis showed that AWGs may indeed be financially feasible when utilized in Austin’s environment.

This is an environmental study on concrete that follows the standard protocol of life cycle assessment (LCA) for two types of concrete, ordinary and frost-resistant concrete, with a focus on the superplasticizers used as admixtures. The use phase is not included in this study and the concrete is assumed to be inert during this phase. The results show that production of the raw material (especially cement) and the transports involved in the life cycle of concrete are the main contributors to the total environmental impacts. The environmental impact of frost-resistant concrete is between 24–41% higher than that of ordinary concrete due to its higher content of cement. Superplasticizers contribute with approximately 0.4–10.4% of the total environmental impact of concrete. Also, we have concluded that the low amount of leakage of superplasticizers from concrete leads to a low risk for the environment and humans.

The objective of this study is to optimize the lot sizes for three different products based on storage cost, set up cost and logistic key performance indicators (KPIs) such as delivery reliability. Two methods including a mathematical model and the static method of Andler’s lot size were originally used to solve this problem. However, both methods produce lot sizes that underperform according to logistic KPIs. For that reason, a simulation considering dynamic behavior and logistic performance is developed to heuristically optimize the lot sizes while being restricted to a minimum standard of delivery reliability. The study indicates that modifying the lot sizes will improve the logistic performance without increasing the total costs drastically. Compared to Andler’s static method, the heuristically-optimized lot sizes show an average increase of the delivery reliability by 7% and a reduction of the total cost by 13%. Throughput time was raised by more than 25% and the utilization elevated by 4%.

Manufacturing system includes multiple business organizations having different decision criteria such as factories, salespersons and customers. It is important to maximize the overall profit while considering the objectives of each organization by appropriate adjustment. In this study, we propose the order planning method using the credibility about salespersons. This proposed method adjusts the due date between the salesperson and the customer by considering the margin time, and then derives effective production schedule by solving the optimization problem that minimizes the weighted sum of the tardiness from due date and makespan. Several computational experiments are conducted so as to evaluate the effectiveness of the proposed method.

Most researches of facility layout planning (FLP) have aimed at finding a layout with which evaluation indices based on distance are minimized. Because temporal efficiency has not been considered in this stage but in post stages, the resultant temporal efficiency may not be optimal enough. The authors have developed an FLP method considering temporal efficiency, in which facility layout is optimized using genetic algorithm (GA), and have enhanced it so that robustness against changes in production environment can be taken into consideration. However, the enhanced method involves a large computational load, since numerous production scenarios need to be considered. This paper provides a method for reducing computational load in the robust FLP based on the sampling approach where each layout plan is evaluated with only a limited number of production scenarios in the optimization process by GA. Numerical experiments showed the potential of the proposed method to efficient robust FLP considering temporal efficiency.

Systematic and stochastic variations, both endogenous and exogenous to companies, are a constant challenge for decision makers struggling to maintain a competitive advantage for the business. In response the decision maker introduces buffers to absorb variations but this does not target the source of the problem. The first step should instead be to focus on how to reduce variations and then to handle the remnant variations. In summary the first step should be to perform variation management and then as the second step buffer management should be applied. The combination of these two subprocesses represent service performance management and within this context is buffer dimensioning a key challenge. Input data, decision maker and process logic are identified as three key aspects of buffer dimensioning which are integrated and resulting in six scenarios. These scenarios unravel different conditions for performing buffer dimensioning and facilitate an awareness of a match or mismatch between current and desired situation.

Since its introduction, postponement as a supply chain strategy has received a lot of attention in the operations management and the supply chain management literature. Nevertheless, there are still mixed answers about the meaning of postponement and as such, about its operational benefits. For instance, while some scholars argue that postponement results in a shorter delivery lead time, others claim the contrary. To reconcile these apparently conflicting findings, the purpose of this study is to establish a typology that highlights the three key properties of displacement, which is a collective term for preponement and postponement. By breaking down postponement into the three dimensions of form, place, and time, as well as introducing its antithesis preponement, a typology for displacement is presented and illustrated using a well-known postponement case.

Efficient scheduling of heat-treatment furnace (HTF), a batch processor (BP), is very important to meet both throughput benefits as well as the committed due date to the customer, as the heat-treatment operations require very long processing time in the entire steel casting manufacturing process and accounts for large part of the total casting processing time required. In the recent time, there are good number of studies reported in the literature related to scheduling of BP associated with many discrete parts manufacturing. However, still there is very scant treatment has been given in scheduling of HTF problem, which has one of the unique job-characteristic: non-identical job-dimensions. This characteristic differentiates most of the other BP problems reported in the literature. Thus, this study considers a scheduling HTF, close to real-life problem characteristics, and proposes efficient heuristic solution methodologies.

In this paper we describe and solve a scheduling problem taken from the slaughterhouse industry. In an on-going research project, a new concept for slaughtering and cutting of pigs is developed. The idea is to replace the traditional production line with a number of meat factory cells, where an operator slaughters and rough-cuts the pig, assisted by a robot. In order to minimize non-productive operator time, a solution approach is presented together with computational results.

In an environment of constantly growing market dynamics and associated increasing complexity of company structures and their production processes, manufacturing companies are forced to adapt to this environment. Information technology is thereby a key for manufacturing companies to regain sovereignty over their own production processes. Digital networking via their own company and as well, the overall supply chain, can only succeed if digital planning reflects reality as accurately as possible and if production control can react to deviations in real time. In essence, this leads to a development of process control towards process regulation. While long-term production and resource planning is usually mapped by Enterprise Resource Planning (ERP) systems, detailed planning, including short-term deviations and real-time data at the production level, is increasingly supported by Manufacturing Execution Systems (MES) at the production control level. However, in order to bring the underlying system concepts into line with Industry 4.0 efforts in a standardized manner, mutual functional integration within the framework of interoperable production planning and control is of crucial importance. For this purpose, studies were carried out in particular into cause-effect relationships. Thus, the overarching research objective is a valid design model to increase the controllability of production planning and control systems (PPC) in the context of Industry 4.0.

The concept of Supply Chain Management has become increasingly important in healthcare and notably in hospitals. Information along the supply chain is the key element for analysis and improvement purposes. The aim of the study is to analyze and visualize the material flow at a Norwegian hospital to identify the possibilities and benefits in current and future planning and operation. The integration of IT enables combining material and information flow. Statically analyses of the material flow can support in planning and control of the logistics activities. The visualization of the material flow can support to take long-term decisions e.g. for distributing departments at the hospital.

In order to match financial sustainability with the delivery of high-quality healthcare hospitals need to seek efficient ways of managing inventories with a large variety of medical supplies. To find a balance in the trade-off between cost and service levels that ensures on time and high-quality patient treatment is a challenge. It is especially crucial in the hospital setting, where the consequence of a stock-out can be much more severe than lost revenue. The process of ensuring that the required supplies are available at the right time is a particularly important supporting role within hospital logistics. The scope of this study is inventory control at the point-of-use inventories in hospitals. It concerns the short-term planning and control area, which focus on coping with actual demand and making necessary changes in order to match efficiently to plans. This study aims to model the inventory control process and discuss how technology can support high availability of medical supplies in hospitals.

A non-instantaneous deteriorating item refers to the product that its deterioration starts after a specific period time rather than starting instantly of its arrival in stock. In this paper, we study the inventory control policy for a non-instantaneous deteriorating item subject to pricing and advertising decisions. The demand function is price- and- time-dependent and shortage is allowed and partially backlogged. The retailer aims to maximize its total profit determining the optimal selling price and inventory control variables. We formulate the proposed model and develop an algorithm to indicate the optimal solution. Finally, we extend a numerical example with discussion to show the efficiency of the proposed model.

The paper studies an engineer-to-order (ETO) manufacturing firm. A novel approach is used to assess the fit of capacity planning methods in the planning environment of the firm, and towards delivery date setting, which is of strategic importance for ETO firms.

Producing companies are challenged by competition in global markets, in which customers have in general a strong negotiation position. In order to improve their competitive situation, companies constantly attempt to decrease production costs. However on the one hand, it can be observed that companies often do not only have an issue in decreasing their production costs but also in the determination of current production costs as basis for improvements. On the other hand, producing companies face an increasing volume of production data in the course of Industrie 4.0. This data is expected to be potentially usable as additional sales asset. Yet, especially traditional companies do not know how to translate generated production data into incoming cash flows. In order to tackle both named issues, this paper presents both an overview of data-based business models for producing companies and a tool for increasing transparency of production costs in global production networks.

Production logistics systems consist often of a number of low value-added activities combined with a high degree of manual work. Therefore, increasing effectivity and responsiveness has always been a target for production logistics systems. Sharing data in real-time may have a considerable potential to increase effectivity and responsiveness. The first step to realise real-time data sharing is to have a clear understanding of current state of PL systems and their requirements. This work is performed an ‘as-is’ situation analysis of an industrial case aiming at identifying which areas and applications would benefit most from real-time data sharing. The findings take a step closer to have a better understanding of CPS and Industry 4.0.

The industrial food production is currently caught between the increasing demands of numerous stakeholders, economic profitability and the challenges of digitization. A solution to face these various challenges can be seen in the aggregation of data into higher-value, independent data products that can be offered and sold on a buyer’s market. Large amounts of heterogeneous data are already available in the value chain of the industrial food production, e.g. throughout the data-driven harvesting of primary products, further processing by interconnected production facilities and the information-intensive product distribution to end consumers. However, the data is usually only evaluated and used locally for the optimization of internal processes or, at the most, within comprehensive partnerships. The purpose of this paper is to identify new revenue opportunities for current and future players in the industrial food production by using data as an independent economic good (data products). For this purpose, scenarios for the development and use of data products via Industrial Internet of Things platforms are developed for a food technical reference process, the industrial chocolate production and its value chain. On this basis, examples for different types of data products and their value propositions are derived. The results can not only serve food producers and relevant stakeholders but all industrial producers as an input for the future, yield-increasing orientation of their business models.

Due to a growing number of product variants, shorter lead times, and global supply chains, planning and launching production systems is becoming increasingly important. Therefore, managing the period of production ramp-up becomes a competitive advantage. To handle the increasing complexity and uncertainty in this special phase, data availability is necessary in terms of efficient decision making. However, in this early phase of the product and production system lifecycle, data quantity and quality are not guaranteed. This is due to the degree of novelty of processes, technologies and human behaviors in this special phase. In this paper it will be analyzed that selected data from the factory planning phase as well as the factory operation phase needs to be jointly processed by ramp-up involved personnel as value adding information. Finally, the presented use case, as well as the derived data management approach, will help companies to better manage production ramp-ups in the future.

This concept study focuses on technological process planning (TPP), taking into account the application potential of open access digital tools (OADT) in small- and medium-scale manufacturing enterprises (SMMEs). It presents the authors’ classification of digital tools (DT) used in the SMMEs and available groups of OADT. It also proposes possible scenarios’ potential for future TPP by taking into account the developments in artificial intelligence (AI) and immersive technologies, i.e. virtual and augmented realities (VR/AR). It also focuses on challenges and procedures regarding the implementation of DT in specific SMMEs’ environments, focusing on how open access tools play a crucial role at the first stages of SMME development, as these tools enable minimization of resource wastage. Although the capabilities of these tools are limited, it is vital to develop implementation strategies within a SMME, based on specific need(s).

Industry 4.0 is a subject that has attracted the interests of researches worldwide for its ability to achieve productivity gains and to provide competitiveness to the companies. Although much research has been done on technical studies, little attention has been paid to the challenges that decision-makers, executives and managers face to implement the concepts of Industry 4.0 in their companies. This research was based on secondary data, involving a research made with 246 companies in Brazil, 287 in Germany and 72 in Portugal, which studied the internal and external obstacles and expectations of these 605 companies. The originality and practical implication of this research is to compare these three countries, studying common and different points to implement the concepts of Industry 4.0, so researchers can conduct their studies to try to provide answers to practical expectations, linking research to practice.

Logistics systems have a key function to meet competition criteria like delivery time, punctuality or flexibility. Industry 4.0 technologies are considered as an important key to master increasing requirements like individualization, shorter product lifecycles or global competition. However, bringing the complex structures and processes of a logistics system to a higher level of maturity is not an easy endeavor. The actions to be planned and implemented need to be rooted in the overall digitalization strategy of the company. Furthermore, they need to be interlinked with the development of other corporate functions like production, quality or planning and they need to be based on current capabilities. To support such a systematic development process, maturity models seem to be the method of choice, and there is already a considerable amount of such models available. As those models are mainly focused on the company as a whole or specifically on production systems, we identified the need for a specific support for logistics. Therefore, in this paper we describe the relevant background as well as the components of a maturity model for an Intralogistics 4.0.

The 4th industrial revolution generates a high potential for smart production systems. Many manufacturing companies considering therefore the application of cyber-physical systems in the sector of intralogistics. The aim is to achieve better logistics performance or lower costs. However, small and medium sized enterprises (SME) are hesitant about introducing Industry 4.0 technologies. They fear high implementation costs, low benefits and the lack of know-how increases the reluctance of the companies.This paper presents a procedure which enables SME’s to assess the benefits of Industry 4.0 technologies by themselves. The model follows the recognized principle: First improve your processes, then automate them: Methodical basis is a process model intralogistics, which also considers self-controlling cyber-physical systems. In addition, the benefit aspects are assigned to the individual process steps. In the specific application, the company first determines the digitization potential of the individual activities and then the associated benefits of Industry 4.0 technologies. The procedure reduces on the one hand the uncertainty regarding of wrong decisions and creates on the other hand the possibility for companies to select Industry 4.0 technologies goal-oriented. The described procedure was validated with SMEs.

The concept and vision of Industry 4.0 has been around for almost a decade and gain a lot of momentum and attraction globally. Central to the vision of Industry 4.0 is the concept of a “Cyber-Physical system”, linking the IT elements of an enterprise (Cyber) with the physical system (man and machine) of an enterprise. This vision is well known and promoted as crucial in radically transforming todays manufacturing industry. While there is a plethora of papers and studies of the various “cyber” aspects, the concept, visions, benefits as well as the downsides of Industry 4.0, few papers have much to say about the actual implementation. Based on a digital maturity mapping of ten front line manufacturing enterprises in Norway this paper analyses implementation at shop floor level of both cyber and physical system and their interaction. From the survey data a clear picture emerges of the development of a cyber system, as well as worker usage and benefit of the system. However, the two systems don’t interact very well, worker interaction is limited to plain old keyboard usage, instead of employing more mobile, handsfree, voice based or similar interaction methods. Currently there is no cyber-physical system, rather a burgeoning cyber system poorly linked to the physical world. If the cyber-physical system is to be realized there is a need for a rethinking and upgrading of man-machine interaction.

It became more than evident that the era of Industry 4.0 is upon us, where industrial manufacturing companies are facing strong demand to increase their productivity and profitability by realizing or upgrading to smart factories and resource-efficient manufacturing processes. Having this in mind the aim of this paper is to provide insight regarding best practices in implementing Industry 4.0 concepts and their implications on manufacturing energy and environmental management systems and overall manufacturing energy efficiency. Our analysis used the Serbian dataset from the European Manufacturing Survey conducted in 2018. Furthermore, non-parametric correlation (Spearman’s) analysis of the introduction of technologies on the one hand and EN ISO 50001 and EN ISO 14001 on the other was carried out. Results indicated significant correlation among Industry 4.0 concepts and both manufacturing energy and environmental management systems.

The design and development of new vessels is a cost and time-intensive effort, which is greatly reliant on expertise and experience. The prototype building and testing are, especially for small producers who do not sell on volume, often at the same time the production of the first vessel. This further increases the need for other means of reliable and accurate prototype experimentation. This paper presents a procedure for the virtualization of sea trials in which the vessel prototypes are tested, thus generating a concise and reliable data model of the trial, which can be used in simulation and other product development tasks.

This paper focuses on introducing measurement technologies into manufacturing sites regarding the worker-oriented part of 6M, which consists of Man, Machine, Material, Method, Mother Nature, and Money. First, we introduce indoor positioning and work motion recognition systems that we have developed as key components of Internet of Humans (IoH) technologies. Next, we briefly report on two case examples of manufacturing sites where worker behavior measurement, analysis, and visualization are promoted. Then, we conclude this paper with discussion about the costs and benefits on the introduction of indoor positioning technologies into manufacturing sites.

Recently, many manufacturers have recalled their products owing to quality issues. It is increasingly difficult to determine the cause of quality issues because of the complexity of the supply chain. Thus, it is essential to share manufacturing information throughout the product life cycle. However, small and medium-sized enterprises (SMEs) often lack the necessary infrastructure and information systems.This research proposes an open-source system allowing the 3D visualization of production history and simulation results. The production history includes products’ time stamps and inspection results, defect information, and a status of each facility. This information is then used to construct a product workflow and simulation model. Further, it is possible to compare simulation results for up to three alternative scenarios. The system is developed using open-source libraries for easy diffusion and application to SMEs in the automobile industry. A method for the implementation of this system to Korean auto parts companies is introduced.

In the current dynamic and competitive market, contemporary manufacturing systems must be constantly adapted to meet the requirements for a more agile and smart production. The advent of Industry 4.0 comes as a reference on development of applications and technologies for manufacturing process innovation. Among the pillars of Industry 4.0, a noticeable relevance is given to Cyber Physical Systems, Internet of Things and Internet of Services. In parallel, new technologies as Blockchain and Smart Contracts are important innovations also coined by the Information Technology domain. More specifically, Internet of Services is characterized by a service-oriented computing model enabling a diversity of software-based services through the Internet, among them the Blockchain solution. The paper explores these technologies bringing their intersection as well as their possible applications in the shop floor level. Through the interlock of such concepts, the paper aims to propose an architecture that promotes the utilization of Blockchain for the validation of some service demands in an advanced manufacturing scenario of the Industry 4.0. Lastly a hypothetical case study is presented for illustrating the proposed architecture.

Industry 4.0 is a fast growing concept which has started to gain ground over the last few years and strives to achieve a higher and more efficient production rate through the usage of automations. This concept is directly correlated with Business Process Management because its implementation concerns the improvement of business processes. Business Process Modeling is a tool of Business Process Management which can depict the processes of an organization in order to be elaborated and improved. For that reason models are widely used for the better understanding of processes and as a first step of new concepts insertion, such as Industry 4.0, in an organization. Hence, a comprehensive framework of a modeling architecture is essential for a company which desires the transition to new concepts according to its needs, its processes and its structure. In this paper, a complete architecture which proposed in a company activating in gas industry is presented including the appropriate models for the recording of business processes and how Industry 4.0 principles could be incorporated to them.

Global markets for food products are changing towards accommodating larger product variety while experiencing shorter lifespans of products. These market trends impose pressure on the existing manufacturing infrastructure, however the food industry lacks investment in manufacturing technology. This research illustrates how the robustness of the existing production system can be enhanced using sensor data, including a case study of a Danish food company. The paper presents an outline of an iterative process performed in a case company to identify and carry out the potential of utilize sensor data from the existing production setup, and an example of the potential and return of investment is introduced. Several challenges are recognized when implementing sensor data to enhance the production system robustness. The in-house competencies are lacking in order to install the setup of sensors and operating in a database with a big amount of data. In addition, the challenge of continuously maintaining the data and the analyses is present. The research points to the importance of involving operators to better understand the context of the production. In conclusion, the case company has achieved information of the production based on data and not only running adjustments, based intuition of the operators and has thereby enhanced the production system robustness.

Objective quality inspection of products in manufacturing process is inseparable from sensor technologies. Inspection methods using analysis of vibration signals have advantages such as being non-destructive, accurate, fast for in-process application. This paper presents recent developments and applications of in-process product inspection which use vibration and acoustic data in various industry. In detail, the inspection system developed with accelerometer, laser vibrometer, laser ultrasonic sensor, acoustic emission sensor, and microphone are presented. An in-process noise detection system for car body parts inspection is introduced as a case study.

The aim of this research is to provide an example of the importance that integrated Product Lifecycle Management (PLM) and Manufacturing Operation Management (MOM) systems have in realizing the Digital Manufacturing. The research first examines what the Digital Manufacturing involves and then identifies Digital Twin and the related Digital Thread as key elements. PLM and MOM solutions support the Digital Twin and the Digital Thread allowing the exchange of product-related information between the digital manufacturing model and the physical manufacturing execution. A Digital Twin of a wing box and its assembly process is created in PLM by building the bill of material and bill of process. Then it is shown how in MOM system the production phase is facilitated by managing production operations, advanced scheduling and supporting the execution of the processes and how the analysis of the manufacturing performance is possible. The result integrating these systems is to have the right information at the right place at the right time along with the related benefits in terms of costs, time and quality. The activity has been developed in Siemens Industry Software under the European Project AirGreen 2, an integrated research action of the REG IADP (Regional Innovative Aircraft Demonstration Platform) part of the Joint Technical Programme, the steering and coordination of LEONARDO Aircraft. The AirGreen 2 project is an Innovation Action funded by the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme, under Grant Agreement N°807089 REG IADP).

In manual assembly systems, there is often little transparency and great potential for optimization, especially in assembly systems with small batch sizes. In this paper, a model is developed that supports an approach to automated assembly optimization. For this optimization, actual data is collected in manual assemblies. Based on the data, the optimized assembly sequence is derived by developing a best practice. Best practice describes a combination of assembly processes performed by the workers during the data collection. The model shows the relationships and the dependencies in the assembly systems and allows to improve it.First, the considered assembly system is defined as a socio-technical system and general modeling principles are prepared. After presenting the benchmark approach to derive the best practice, the requirements for the model are identified. Then, the model is developed in four steps: The system boundary is defined, the features are described, and the model is formalized. Finally, the model is applied and tested in an example project and its purposefulness is shown.

A controlled vocabulary list that was originally developed for the automotive assembly environment was modified for home appliance assembly in this study. After surveying over 700 assembly tasks with the original vocabulary, additions were made to the vocabulary list as necessary. The vocabulary allowed for the transformation of work instructions in approximately 90% of cases, with the most discrepancies occurring during the inspection phase of the transfer line. The modified vocabulary list was then tested for coder reliability to ensure broad usability and was found to have Cohen’s kappa values of 0.671 < κ < 0.848 between coders and kappa values of 0.731 < κ < 0.875 within coders over time. Using this analysis, it was demonstrated that this original automotive vocabulary could be applied to the non-automotive context with a high degree of reliability and consistency.

Digitalization is no longer about finding data or inputs to different business processes as advances in technology now enables us to capture and/or retrieve all needed data. The challenges lie in the quality of the data, not only in the narrowest technical sense of the term, but also in relation to the extent of what e.g. product developers really need to know in order to support their processes and how this should be presented. In four industrial R&D projects, financed by the Norwegian Research Council and the EU, these challenges have been addressed. In the first project, a traditional approach was chosen. Here overall objectives and use-cases were defined, and the project quickly jumped to what everybody (including industrial users and research scientists) believed what the users needed and what could be provided to users from pre-defined data sources. However, it became clear that this did not necessarily match what the users really needed. This is in line with experiences from more traditional process mapping. Through the four R&D projects, a methodology focusing on what the product developer really needs, named “Major Design Elements” was developed, tested and implemented. The approach is to identify the most important design elements, then find what kind of knowledge is required, including relevant analyzes, data and sources. This together with an understanding of the relevant processes form the basis for a Design Dashboard enhancing the product development process.

Research has shown that design changes cost more in later stages of product development. Therefore, companies adopt Design-for-X methods to optimize product designs for many aspects in the early design stage. Despite such efforts, products often encounter several design changes during the commission of the production, a principal reason being failure to meet target costs. Accurately estimating cost in the early design stage is difficult due to insufficient information. In particular, as production becomes more distributed cost estimation is also more difficult because information is more distributed. This paper introduces a cost estimation method to address this problem. It describes a distributed manufacturing situation and a cost breakdown framework. A use case is provided to illustrate how the framework allows for supply-chain cost negotiation and design adjustments in the early design stage.

The activities to introduce Industry 4.0 and Digitalization into manufacturing environments imply a multitude of new systems and technologies which change work environments and tasks that are existing there up to now. In order to cope with the increasing complexity, employees in production must be transferred into knowledge workers. This requires new approaches of work organization, training and education, and the active involvement of employees in shaping future workplaces and processes. To achieve these goals new approaches and methods for collaborative design and reorganization of workplaces and processes involving employees by means of participatory design need to be developed and implemented. Due to the large number of available methods, targeted support for the workers is required for selecting suitable methods. In this paper, the motivation and reasons to use participatory design are explained, an approach to support method selection is developed, and a computer-aided selection procedure for empowering the responsible persons on the shop floors is presented. Following, this is the basis for applications in an industrial context, where the solution can be validated and improved based on practical experiences.

Knowledge management environments are being developed for product development activities to help companies reuse their knowledge. This trend has been identified in manufacturing companies, which operate product design departments at various locations. Investigating how these companies can configure their knowledge management environments to fulfil engineers’ knowledge needs in design activities opens up a research topic for us. A well configured knowledge management environment (KME) will require a clear understanding of what key features the KME shall have. The research focuses on the structures and operations of knowledge sharing for product development. A case study of four manufacturing companies was conducted to understand their KMEs.The study contributes to theory by providing an understanding of the structure of KMEs in companies. Researchers in the domain of knowledge management can develop a good understanding of how engineers interact with KMEs so that researchers can propose knowledge management systems or methods that make tangible improvements. Chief engineers or managers in companies who are in charge of knowledge management can benefit from the understanding of their own KMEs.

The design of innovative systems involves a complex decision making process spanning over different criteria and stakeholders. The complexity of the design process is heightened at its early stages by data scarcity, involving high uncertainty and vagueness. Product-Service Systems (PSS), which are bundles of products and services designed to fit complex customer needs, are an example of those innovative systems. PSS design can be thus approached as a multi-criteria and multi-stakeholder decision process. The aim of this research is to provide a consistent framework for decision aiding in the early stages of collaborative PSS design. The framework was built within a collaborative project involving a French company, interested in innovative solutions for managing their safety clothing system. At the methodological level, the Analytic Hierarchy Process (AHP) was used.

Manufacturing Execution Systems (MES) have evolved to alleviate the drawbacks of Enterprise Resource Planning (ERP) systems by providing real-time information exploitation from the shop floor. In parallel with the increasing number of companies adopting MES, MES vendors have exponentially increased over the past two decades. While companies tend to focus merely on the technological aspects of the MES implementation, the adoption of MES implies an organizational transformation process that needs to be properly addressed by companies for implementation success. This is important because the new functions, services, and operability offered by the MES needs to be aligned with existing business processes and practices. Considering the human, technological, and organizational aspects holistically, this paper outlines critical success criteria and proposes an organizational readiness model for MES implementation.

Manufacturing execution systems (MES) enable the detailed control of manufacturing operations, i.e. they facilitate digital and integrated shop-floor systems as envisioned by Industry 4.0. Yet, many manufacturing organizations struggle to integrate MES and demarcate it from other information systems (IS) in manufacturing. Therefore, this paper explores how MES can be functionally and technologically distinguished from other IS to determine its (future) role in the IS landscape. To provide an answer, this research applies the conceptualization of IS into five application functionalities and underlying enabling technologies. They are referred to as transaction processing, interactive planning, analytics, document management and process monitoring and control systems. We found that MES merges different types of application functionality into one system through its diverse functional requirements, and therefore can be characterized as technologically heterogeneous, in contrast to other ‘classical’ systems. MES then also takes on a central integrating role in the IS landscape. The findings offer an explanation for the challenges associated with the adoption of MES functionality, and highlight the importance of addressing integration questions in light of Industry 4.0.

Search processes are omnipresent. In the field of industrial production they occur whenever material or information is needed. While searching is a fundamental activity within production processes, existing models and methods in the field of production management are not designed for modelling or analyzing industrial search processes. This paper presents a generic phase model that can be used to describe industrial search processes. Furthermore, an analysis method is proposed to determine and prioritize fields of action for the optimization of search processes.

The rapid change that is affecting the society together with the rising of new technologies are impacting the manufacturing sector as well. Moreover, this change has also an impact on the skills that operators and managers should master. Companies, on their own, must be always updated in order to keep high their competitive advantage. For these reasons, we carried out this study which aims at searching for a new methodology to assess the current level of companies’ workforce in terms of skills needed for taking advantage from the Industry 4.0 paradigm. Starting from an analysis of skills assessment methods, we created DREAMY4Skills, a skills 4.0 assessment model focused on the specific job profile within a company operating in the manufacturing sector. This model is based on a maturity model which enables to make companies be aware of their current status in terms of skills and thus it helps companies in implementing a transformation path to pursue a continuous improvement strategy. This work has two purposes, on one side we would like to have a model useful in practical terms to enable the skills 4.0 assessment held by the workforce, on the other side there is the scientific purpose which is to create another small brick in the literature.

The concept of collaborative networks has been encountered very frequently these days as the reply when trying to adapt and enhance enterprises in this tremendously competitive commercial environments. A lot of knowledge has been gathered for collaborative networks so far, from defining network kinds to levelizing partnerships and also proposing models for partnership developments. But most of these efforts didn’t tackle a very vital obstacle which is detecting and predicting collaboration possibilities between enterprises. In this paper, a new enterprise characteristics classification is proposed, which will be used as a profile for characterizing enterprises susceptible to take part in a collaborative network. The proposed detection approach is based on the enterprise characteristics concept as well as collaboration network types. Also a hypothesis to rank the potential partners using KPIs is shown along with the big picture of this approach accompanied by the future work that has to be done.

The construction of factories is based on the factory planning process. A new construction of factories or their expansion represents a significant investment decision for companies. Therefore, it is necessary from an economic point of view to allow a trouble-free flow. Current trends, such as urban factories, increase the likelihood of conflict with external stakeholders. In many cases, this means high additional costs and risks for the companies concerned. The evaluation of current case studies shows that ignoring individual stakeholders during planning can lead to delays or, in the worst-case scenario, to the shutdown of the factory project. The aim of this article is to present the current state of stakeholder integration in factory planning, construction, and factory operations in research and practice. Based on the results of a research project in Germany, studies are presented and necessary fields of action identified. Subsequently, a concept is derived that facilitates the systematic and project-specific integration of stakeholders into the factory planning process.

Since the field of service engineering emerged in the late 20th century, the service industry has undergone drastic changes. Among the reasons for these changes is the increasing digitalization, which has made it difficult for companies to successfully develop new service offerings. While numerous service engineering models are available to provide guidance during the design of new services, many of them cannot keep up with the requirements of today’s economic environment. The present paper examines the requirements that service engineering models need to meet in order to be suitable guidelines for the digital age. To this end, the introduction illustrates how digitalization has changed the service industry. Afterwards, selected service engineering models and related norms are presented. Finally, a set of requirements for modern service engineering models derived from best practices from recent years is introduced.

The current global situation increases the exposure of organisations to their environment. As a consequence, companies have to consider a variety of topics that they had managed to a limited extent before. An organisation should now go beyond its limits in order to maintain its viability over time, this means being sustainable and making its related environment better by enabling collaborative working. In addition, there are not only challenges in the management of external relationships but in the management of internal ones. These are a consequence of the pressure for decision-making in short periods of time and a lack of coordination between different parts of organisations, effectively operating as silos with divergent goals. Even with common goals, due to a lack of adaptability or flexibility, some areas do not see the benefits of coordinating work for securing a long-term USP (Unique Selling Proposition) of an organisation. In this context, for both external and internal challenges, researchers often fail to holistically consider an organisation as being an agent, interacting and creating ecosystems of cooperation. Thus, the aim of this study is to propose a holistic approach as to how organisations can interact with their environment, as well as internally. Hereby, the Viable System Model and System Dynamics were applied. In conclusion this proposed approach enables companies to interact within its area of influence with an efficient approach. In order to prove the conceptual model, a simulation for a manufacturing supply chain and its area of influence was performed.

Currently, manufacturing industries are faced by ever-growing complexities. On the one hand, sustainability in economic and ecological domains should be considered in manufacturing. With respect to energy, many manufacturing companies still lack energy-efficient processes. On the other hand, Industry 4.0 provides large manufacturing datasets, which can potentially enhance energy efficiency. Here, traditional methods of data analytics reach their limits due to the increasing complexity, high dimensionality and variability in raw data of industrial processes. This paper outlines the potential of deep learning as an enabler for energy efficiency in manufacturing. We believe that enough consideration has not been given to make manufacturing efficient in terms of energy. In this paper, we present three manufacturing environments where available DL approaches are identified as opportunities for the realization of energy-efficient manufacturing.

Promotions at retailers are an effective marketing instrument, driving customers to stores, but their demand is particularly challenging to forecast due to limited historical data. Previous studies have proposed and evaluated different promotion forecasting methods at product level, such as linear regression methods and random trees. However, there is a lack of unified overview of the performance of the different methods due to differences in modeling choices and evaluation conditions across the literature. This paper adds to the methods the class of emerging techniques, based on ensembles of decision trees, and provides a comprehensive comparison of different methods on data from a Danish discount grocery chain for forecasting chain-level daily product demand during promotions with a four-week horizon. The evaluation shows that ensembles of decision trees are more accurate than methods such as penalized linear regression and regression trees, and that the ensembles of decision trees benefit from pooling and feature engineering.

With regard to the recommissioning of damage caused inoperable complex capital goods, a high logistics efficiency is a very important competitive factor for regeneration service providers. Consequently, fast processing as well as a high schedule reliability need to be realized. However, since the required regeneration effort for future damages may vary and is usually indefinite at the time of planning, capacity planning for the regeneration of complex capital goods has to deal with a high degree of uncertainty. Regarding this challenge, the evaluation of prior regeneration process data by means of data mining offers great potential for the determination of load forecasts. This paper depicts the development of a data mining approach to support capacity planning for the regeneration for complex capital goods focusing on rail vehicle transformers as a sample of application.

Future manufacturing companies require smarter solutions to compete in the economy. Smart supply chain management systems are one of the most effective solutions. Use of previous information can help companies to predict the demands of the market and react in an agile manner to sudden changes. Google receives over 63,000 searches per second on any given day. This huge amount of data provides us with the opportunities to investigate researches in multiple subjects and extract useful information from the raw data that is available through Google Trend. In this research, we investigate the possible relationships between searches that are made in Google for two manufacturing capability terms, namely, Precision Machining (PM) and Electric Discharge Machining (EDM). Time-series oriented research is conducted on these two datasets in order to find the dynamics characteristics as well as interesting hidden relationships between these two search items to help us build a smarter supply chain management system. Two different methods namely ARMA and ARMAV models are be applied to fit a representative model to these datasets. The order of the both models are evaluated based on AIC statistic. In addition, multiple seasonal trends are detected in the datasets. Finally, Using ARMA model, we predict the datasets for one-step ahead in order to validate our models. Recognition of seasonalities and correlations between two datasets could lead to better prediction and smarter supply chain creation and management.

Nowadays, manufacturing companies collaborate with universities in innovation projects to sustain or achieve competitive advantage. However, fundamental differences between the industrial and academic worlds hamper the utilization full innovation potential of such collaboration. As a countermeasure, industry stresses the need for the development of knowledge management tools that can increase the value of collaborative innovation projects. This paper covers a qualitative study of research-based innovation projects owned by manufacturing companies and partly funded by government, where the academia has the role as research provider. We seek to answer two research questions: (1) how can the strategies and objectives for collaboration to meet both partners’ expectations be defined? (2) how to facilitate the projects to enhance the creation and exploiting of knowledge? The study identifies that a modified version of Nonaka’s so-called five-phase model of organizational knowledge creation is applicable for the given context. Based on this, we propose a conceptual knowledge management model of university-industry collaboration in innovation projects. The proposed model provides (1) management initiatives that intensify knowledge creation and exploiting processes (2) ensures partners’ commitment to collaboration along with the continuing improvement of university-industry collaborative concepts. It is proposes that the model will support knowledge managers of industry and university in conducting innovation projects more effectively and efficiently, as well as deliver even more innovation values to partners and society. The model can also assist national and federal research/innovation councils in decision-making when assessing industrial research project applications.

The updating of the judicial reorganization and bankruptcy legislation, law 11.101/2005, resulted in an increase 63.7% per year of judicial recovery from 2005 to 2018, but with a success rate of only 1%. The speed of launching new technologies tends to contribute to the crisis in companies, and to emerge from this crisis, companies must be aware of the financial indicators and, when necessary, request for judicial recovery at the same speed of technological changes.

In recent years technological advancement makes it possible to connect heterogeneous systems at hierarchical levels, such as macro level where strategic decisions are made, and micro level where organizations interact with the users. Modeling of these connections alongside with systems is one of the problems, which can be solved by modeling techniques that take hierarchical nature of the systems into consideration. In this paper, we propose a multiscale modeling approach for social systems. We suggest to design a model by adopting certain entities: decision makers, resources, actions and propagation variables. The proposed approach is evaluated on an example of collaboration between two systems: electricity suppliers and manufacturers. Results of the computational experiments demonstrate the effectiveness of the proposed technique.

Interoperability among systems is a challenge that requires several regards and infrastructure often complex. The best worldwide reports and frameworks say that this can also improve health care and bring the best outcomes for stakeholders. Implementing Interoperability in developing countries is less affordable even it can also promote quality care and save lives. The best models and guidelines could offer protocols for sharing health data allowing to build a system that can deliver at the same time quality, transparency, and social value. This paper addresses an interoperability problem providing the steps built in a pilot to enable a conceptual framework for exchange healthcare data through EHR, and presents the first step and overview of a platform build using rules of PDCA. The experiment was built in a small Brazilian town intends to be a standard for deliver interaction between local government and citizens and also to offer to patients to control own medical data records through a mobile application.

Projects is the preferred model for one-of-a-kind production. Projects may be difficult to manage due to complexity and many involved stakeholders. Stakeholders are a major source of uncertainty. Uncertainty may be both positive and create opportunities and negative giving risks. Risks and opportunities are either operational, strategic or contextual. The traditional approach to managing risk comprise identification and analysis of risks as well as response planning and control. There is a need for a shift in mindset for managing risks. Rather than regarding risks as “evil”, they should be managed because uncertainties also create opportunities. The Bermuda Risk Triangle is the intersection between operational, strategic and contextual risks. Project risk navigation is about how project leaders can navigate in this triangle to reach their objectives.Opportunities are often more or less neglected in projects. At the most, just a few are identified.A framework for managing opportunities is suggested. It builds on the project control variables: time, cost and scope of work. It contains a classification of eight opportunity types. Using this classification in dedicated workshops has shown to produce far more opportunities than usual.The framework is verified in a case study. The case is the construction of the new National Museum in Oslo, Norway. Through the framework a total of 246 opportunities have been identified representing an estimated cost reduction of about 64.2 million USD.