**2. Literature review**

The digital transformation for increasing the company performance requires the focus on theories and concepts that could be exploited for improving the enterprise. Indeed, organizational and management methodologies are essential, but Industry 4.0 concepts, robotic collaboration concepts, and big data analytics could also be used in this transformation. Some of these theories and concepts are presented in the following part in order to choose those that are adapted to the sustainable Industry 4.0 framework and the intelligent tool for supporting the company transformation.

#### **2.1 Organizational methodologies**

The improvement of the company production system requires to exploit the adequate methodology. Numerous methodologies are used in companies for increasing their performance such as GRAI Methodology [2], CIMOSA [3], or PERA [4] for structuring the global company, lean manufacturing, DMAIC (Define, Measure,

#### *Production Systems Performance Optimization through Human/Machine Collaboration DOI: http://dx.doi.org/10.5772/intechopen.102036*

Analyze, Improve, Control) method, design of experiments for focusing on the production system, and solving specific problems. The performance criteria mostly used are quality, cost, and delivery time (QCD).

Based on the theory of systems, GRAI Methodology, contrary to CIMOSA (technological aspects) and PERA (human aspects), focuses on all the company aspects: decisional, informational, and physical systems. Five models (physical, functional, process, informational, and decisional) have to be used for transforming a company and improving its performance. This methodology is adapted to the objective to integrate Industry 4.0 concepts in SMEs but a zoom on the physical system is necessary to increase the efficiency of the company transformation. Indeed, lean manufacturing methodology is the most exploited because of its objective to structure the supply chain organization around customer demand satisfaction in terms of quality, cost, and delivery time.

The concept of lean manufacturing has been firstly described in [5] and applied in Toyota Company. Lean manufacturing is a methodology used for reducing seven wastes such as transportation, overproduction, motion, waiting, defects, inventory, over-processing. Lean principles allow optimizing the company efficiency. As exposed in [6], "sustainable manufacturing and lean practices focus on creating an intelligent network system that improves productivity, quality, and customer orientation, while eliminating waste." Many tools could be used in the frame of lean manufacturing methodology implementation such as Value Stream Mapping (VSM), Single Minute Exchange of Die (SMED), Failure Mode Effects Analysis (FMEA), 5s, Total Productive Maintenance (TPM), Kaizen, Kanban.

As explained in [7] lean manufacturing not only contributes to optimize added value for satisfying customer expectations but also increases environmental benefits. Indeed, SMEs could not improve their performance by ignoring the actual environmental context. In addition, in the use of lean manufacturing, the cadence of the production line that is called takt time could be considered by mistake (bad application of lean concepts) as nonsocial and nonsocietal. For instance, if the takt time is not well defined, it could create stress for operators because of their desire to succeed. Indeed, the positive impact is defined in the company through the elimination of waste time and non-added value, giving operators more value time for well finishing their tasks. The use of lean tools such as SMED, for reducing the external operations, allows to implement tools near machines optimizing by this organization the ergonomics and the useless moving.

This continuous improvement method integrates physical and informational flows in an industry with aiming at the identification and elimination of wastages that affect lead time, material cost, and quality of products [8, 9]. But for a SME the digital transformation of its processes needs to solve specific problems (use cases) for ensuring the feasibility and efficiency of solutions that will be implemented. Both DMAIC and DOE are able to manage these specific cases (use cases).

Six sigma is a method based on DMAIC (Define, Measure, Analyze, Improve, Control) principles. Six sigma allows to improve the quality of the production system and not only the product quality. DMAIC is used as a problem-solving method for increasing the quality level in the company.

Then, a combination of GRAI methodology, lean manufacturing DMAIC, and DOE methods will allow to define an efficient and well-adapted framework for Industry 4.0 concepts implementation in SMEs. The following part details the concepts of Industry 4.0 and the necessity to integrate sustainability in the digital transformation approach.

#### **2.2 Industry 4.0**

Industry 4.0 concepts use a multitude of technologies to improve company performance. They are involved in logistics, production, data management, and communication between systems. These technologies meet the modernization needs of businesses. Several technologies and tools must be taken into account to integrate the concepts of the industry of the future such as the verticality of production systems, the horizontality of the integration of production chains, the optimization of the value chain, and the use of disruptive technologies. These technologies are cyberphysical systems (CPS), internet of things (IoTs), human/machine interface (HMI), cloud computing, big data, artificial intelligence, advanced robotics, immersive (augmented or virtual) reality, simulation, and cybersecurity [1]. All of these technologies have enabled great advances in the company's digital transformation. For instance, a usual application of these technologies could consist in the exploitation of sensors for collecting information on production lines, big data system for capitalizing these data in a server for analyzing them, and for taking good decisions. Then, according to the production processes and by using these data, operators could interact in the production environment with collaborative or mobile robots. Cyber-physical system stations could be deployed to control all the production systems by integrating programmable logic controllers with different inputs and outputs and various communication modules such as IO-Link modules or Ethernet interfaces.

As presented in [10, 11], based on lean manufacturing, Industry 4.0 could make the company "processes smart by enabling digitalization, modern information-sharing technologies, smart machines, which, in amalgamation, help in fast, effective, and efficient decision-making".

#### *2.2.1 Robotics in Industry 4.0 concepts*

Autonomous intelligent vehicles (AIV) are used in warehouses for transporting products. In addition to problems of flow management and optimization, specific problems could be presented such as the management of the robot autonomy or prioritization. They could be exploited for transporting raw materials or final products from production lines to storage and vice versa. Indeed, these mobile robots are equipped with internal tracking and embedded system. They are able to move in warehouses to carry stocks of products. The robots transport their material to the exact location at a specific time [12]. They are able to move in a fleet and to recognize each other [13]. Moreover, they are not separated from traditional operators and move in a common environment. They have a camera and/or laser system to prevent possible collisions. Then, the problem of safety could be pointed out and different safety systems could be integrated into the robot for insuring the production system efficiency. If the robots detect a force, they could stop and activate their safety. They are also able to open doors that are connected to cyber-physical systems. Thanks to this, logistics are smoother, and productivity is increased. All these transport times, which are no longer provided by man, allow him to work on other issues. Development robots allow workers to concentrate on creativity, productivity, and other dynamic processes, which lay the basis for growth and prosperity [12].

Cobots are collaborative robots able to interact with humans. Cobots are used to support operators on repetitive or specific tasks. In [14], the top-3 reasons to choose to implement cobots (operational efficiency, innovation, and ergonomics) have been repeated and the automation of production lines with cobots has been promoted with

#### *Production Systems Performance Optimization through Human/Machine Collaboration DOI: http://dx.doi.org/10.5772/intechopen.102036*

the use of lean manufacturing methods. The cobots are affiliated with the methods of Lean Manufacturing as they promote the automation of production lines [14].

Cobots have cameras to effectively visualize their environment and adapt accordingly. They are able to recognize and identify shapes, bar codes, QR codes or even colors. Some of them are used for quality control thanks to their high precision. Vision has become the key to Industry 4.0. Cobots are lightweight and easy to configure, unlike conventional robots [12]. Cobots are less costly than their counterparts and can yield more stable effects [12]. On top of that, they have a safety system that physically blocks them when they detect a collision. They are able to switch from a collaborative mode to a cooperative mode. In addition, cobots can work with each other. They are able to communicate *via* the network dedicated to them.

#### *2.2.2 Internet of things*

Industry 4.0 is a concept-driven by IoT, allowing interconnection between objects, equipment, and computers. Robots can be controlled remotely by a central computer using logical programming. Human intervention is then no longer necessary. It is only used for maintenance or recalibration. The classic IoT also includes the internet of robotic things (IoRT). The robots are also equipped with inputs and outputs to control external systems. The data and values used by the robots *via* their sensors are stored in databases in real-time. They can then be operated by external systems. The combination of local computational power and IoT has turned ordinary sensors into intelligent sensors such that the measured data are calculated locally in a sensor module in a complex manner [15]. Robotics and the internet of things have been motivated by several ambitions that are all interconnected. IoT focuses on ubiquitous sensing, control, and recording services, while robotic societies concentrate on development, engagement, and autonomous behavior [12]. The combination of the two technologies makes it possible to limit errors on production lines and increase their efficiency.

All the sensors (cameras, force sensors, etc.) present on the robots allow the inspection to be carried out and participate in quality controls. The IoT is revolutionizing not only production methods but also maintenance strategies [16]. They must be able to exploit robot data in order to prevent possible problems [16]. New skills are expected for operators in the industry of the future. This concept of Operator 4.0 aims to present the transition of the Human-CPS interaction toward a Human-Automate symbiosis for a balance within the production chain [16]. IoT focuses on services promoting pervasive sensing, surveillance, and recording and the emphasis on processing, engagement, and independent activity in robotic societies [17].

As presented in [18–20], IoT "refers to intelligent physical and virtual objects which are integrated in a global (or local company) network, which have identities, and which communicate between themselves or with other internet-enabled devices."

In [21], IoT is defined as a technological concept that utilizes sensors, microcontrollers, and other embedded terminal devices through which real-time data can be collected from manufacturing machinery and facilities.

#### *2.2.3 3D printing*

3D printing is an important technology in the concept of Industry 4.0. It is used for rapid prototyping of mechanical parts. The accuracy and quality of the prints are high enough to make tools for robotics. It is possible to produce different parts whether they are rigid (support) or flexible to adapt to needs [17]. In particular,

companies are developing grippers for their robot arms using this technology. Robotic-enabled 3D printing represents a more sustainable manufacturing method [12]. Thanks to 3D printing, companies are making reliable mechanical implementations to deploy new use cases. The flexibility of this technology encourages them to constantly develop their robotic solutions.

#### *2.2.4 Cyber-physical systems*

Robotics allows better optimization of variable productions and therefore better economic performance. Automatons and computers allow robots to evolve and be smarter. Application flexibility allows computers to optimize production according to schedule demand or markets [12]. CPS is defined in [22] as a "system that integrates computation, communication, and control." It uses sensors for obtaining data, which will be capitalized and analyzed with computing devices and a decision-aided system for taking good decisions concerning the physical system. CPS contributes to improve productivity by remotely controlling physical machinesinputs and outputs. This concept is used to qualify sensors in production lines.

CPS is described in [23, 24] as physical and engineered systems whose operations are monitored, controlled, coordinated, and integrated by a computing and communicating core. The concept of a cyber-physical system is presented in [25] as based on emerging technologies such as additive manufacturing, advanced robotics, augmented and virtual reality, big data, cloud computing, and the internet of things.

As explained in [26], lean manufacturing and Industry 4.0 could be combined for transforming a manufacturing system into a Cyber-Physical Production system (CPPS) with advanced productivity capabilities.

As noted in [27, 28], the "successful integration of SMEs into Industry 4.0 is a relevant social challenge and thus, specific policies and programs should be designed accordingly.

#### *2.2.5 Big data analytics*

With the implementation of the CPS, big data are obtained from the physical and informational systems and need to be analyzed for taking good decisions in the company. The objective is to increase the company's performance.

Big data correspond to the collection of massive data from various sources. These data are structured, non-structured, or semi-structured but must be analyzed and exploited to taking decisions. A typology of data sources has been proposed in [29]:


Seven features have been defined in [30] for characterizing big data: volume, variety, velocity, veracity, value, variability, and volatility. As explained in [31] the level of production standardization, operation network, and service precision could

#### *Production Systems Performance Optimization through Human/Machine Collaboration DOI: http://dx.doi.org/10.5772/intechopen.102036*

be substantially revamped by using big data analytics. In industrial and manufacturing systems, big data promote enterprises to accurately perceive changes in the system and facilitate scientific analysis and decision making for optimizing the production process, reducing cost, and improving operational efficiency [32]. Two main paradigms could be used for big data analytics:


These data contribute to the elaboration and the efficiency of an intelligent manufacturing system in a company. The intelligence of the production systems is based on their ability to accumulate and analyze big data [34]. This analysis can improve customer service, enhance product quality, and create more value in the enterprise as expected by SMEs. Indeed, technological problems have to be solved in this case such as data quality management, data security and privacy protection, the generality of the conceptual framework in actual production, data integration processing in industrial manufacturing systems, and accessing primary manufacturing data. Then, the intelligent system (including the big data analytics) that will be proposed needs to integrate these aspects.

All the concepts of Industry 4.0 that have been presented above are required for digitally transforming the company but their efficient utilization in SMEs needs to focus on the brakes on Industry 4.0 implementation in SMEs and to find levers for accelerating their use in the SME performance improvement. Indeed, sustainability, including social and societal dimensions such as the place of the human in the future production process (human/robot collaboration), but also environmental demands (people expectations, the earth preservation, and official rules), appears as the parameter to integrate for obtaining the membership to Industry 4.0 philosophy.

#### **2.3 The context of the robotic collaboration**

Robots have revolutionized the manufacturing process of companies. They have enhanced the automation of systems and the execution of repetitive tasks at low prices [12]. The potential of robots is great, and the resulting applications are numerous. In addition, some robots use AI to improve their performance. They are able to learn from their environment and from their experience. Several working methods have been established so that human and machine can collaborate.

Human-robot collaborative systems are presented in [35] as a solution with a shared workspace, which mixes the dexterity and cognitive faculties of human operators and the accuracy, in addition to the repeatability skills of robots.

The coexisting mode results in the distinct separation of workspaces between human and machine, no interaction is possible in this configuration. The advantage is that the robots can operate in difficult conditions for humans (e.g., high temperature, risk of intoxication.) The cooperation mode is manifested by the work of human and machine in the same space of work on different objects or tasks. Finally, the collaborative mode results in the simultaneous work of human and machine on common objects or tasks.

Unlike operators, robots are not distracted, and they work without interruption and have a low error rate thanks to their precision and repeatability. Robots are able to predict their maintenance. To do this, they collect data throughout the production lines.

If robots are destined to perform fast and dangerous gestures, safety zones are set up to limit access. Operators are not left out of this technology, because they are able to control them *via* portable systems such as computers, tablets, or even phones. They thus ensure the supervision of the robots. These increasingly intelligent robots, in cases where human intervention is difficult, may be able to make decisions based on their visions, productions, or knowledge of the state of the production line.

A detailed risks analysis has to be done by exploiting the safety guidelines and standard documents such as:


Contrary to the classical robot, collaborative robots (cobots) are contained with intuitive interfaces that support human operators in the physical workload of manufacturing tasks [40]. In addition to the safety required for ensuring the good production and the optimization of the manufacturing system, the human-cobot collaboration needs the elaboration of an interface


They are no barriers between cobots and human operators, but safety mechanisms to prevent harming humans are endowed [41]. They also provide solutions to ergonomics problems, by being alternative solutions to awkward postures and repetitive movements [42].

Robot programming also needs the user to be familiar, but operators in SMEs have no knowledge about the programing language. However, they have to be informed about the physical and computational action the robot can carry out [43]. Then, a new programming environment called CAPIRCI has been developed. It allows nontechnical users to create typical programs executable by COBOTTA, the collaborative robot by DENSO WAVE Ltd.

This chapter proposes to exploit the idea of this programing interface, for developing an interface that aims for operators to manage with button the new technology tools integrated in the production manufacturing such as robots, cobots, mobile robots, IoTs, and also to take good decisions.
