A proposed integrated manufacturing system of a workshop producing brass accessories in the context of industry 4.0

Industry 4.0 is a basic step in the upgrade of manufacturing companies into digital enterprises. It allows more flexibility in manufacturing, as well as mass customization, provides better quality, and enhances productivity. As a result, it empowers companies to meet the challenges of smart manufacturing which is increasingly individualizing products with shorter time-to-market span and improving quality. Smart manufacturing has an important part in Industry 4.0. This study aims to characterize and analyze a smart manufacturing process designed for a company specialized in the production of brass accessories: spherical bushels. We basically set up a simulation tool to develop a numerical production platform for Industry 4.0 which is able to efficiently operate and manage the production and procurement through material requirement planning (MRP, master production program), logistics warehouse, and cyber-physical production system (CPPS). The findings have been optimized by a new redesigned approach of MRP 2: load-capacity adjustment for smart workshop and Industry 4.0 manufacturing planning. It is the setup of an integrated manufacturing system process, which resulted in reducing the spherical bushels’ assembly time and controlling the production and the assembling process. It allowed us to increase the equipment utilization rate by comparing it with the company’s former equipment running time prior to switching to smart manufacturing. The proposed model proves that we can successfully optimize the practical application of Industry 4.0 and therefore significantly increase the production efficiency. We are confident that this is the first work addressing the implementation of a simulation platform controlled by a dedicated cyber-physical production system (CPPS) and a master production program (MPP). A case study of a company manufacturing brass accessories is presented in this paper. The developed simulation platform offers the possibility to create a future digital twin of the company.


Introduction
Industry 4.0 and the associated digital transformation refer to the advent of new digital technologies such as internet of things (IoT), artificial intelligence (AI), cloud computing, cyber-physical production systems (CPPS), and big data and analytics. These technologies imply a profound change in processes and activities, skills, and business models [1]. The emergence of these technologies generate a multiplicity of challenges and opportunities for manufacturers and notably disrupts the organizational ecosystem through automation and smart factory development [2]. Modern businesses have never experienced such speed in terms of technological change although functional and emerging technological innovations are often referred to as disruptive. Since the original drivers of "Industrial Revolution 4.0" are innovation scope, impact, and transformation speed, it is consequently important to highlight the contextual and chronological elements in order to characterize the constitutive foundations of "Industrial Revolution 4.0" (Fig. 1).
Smart manufacturing allows companies to produce customized products while sustaining low costs and reducing time-to-market interval in order to achieve a competitive approach and conquer an increasingly globalized world [3,4]. Kusiak [5] affirm that smart manufacturing is a wide concept of manufacturing with the goal of optimizing production and product transactions by maximizing the use of advanced information and manufacturing technologies. It is seen as a new manufacturing model based on smart science and advanced technologies that significantly enhance the design, production, management, and integration of a typical product entire life cycle [5]. The entire product life cycle can be made easier with the help of various smart sensors, adaptive decision-making models, advanced materials, smart machines, and data analytics [6]. This will allow manufacturing companies to improve their production efficiency, product quality, and service levels [7]. In fact, the competitiveness of a manufacturing company can be optimized by its ability to deal with the dynamics and fluctuations of global market. This situation presents tremendous challenges for manufacturers as they seek to implement new technologies to achieve their goals while expecting a return on investment [8]. In fact, the intelligent manufacturing system (IMS) allows to meet the challenges of manufacturers, which is considered as a new generation manufacturing system obtained by adopting new models, new forms, and new methods aiming to turn the traditional manufacturing system into an intelligent one. In 4.0 era, an IMS uses a serviceoriented architecture (SOA) via Internet to deliver collaborative, customized, flexible, and reconfigurable services to end users, enabling a highly integrated man-machine manufacturing system [9]. This high level of human-machine cooperation aims at setting up an eco-system of different manufacturing elements involved in the intelligent manufacturing system (IMS), in a way that the organizational, managerial, and technical echelons can be seamlessly combined.
Thus, several countries have developed projects aiming to help manufacturers adapt new production technologies into their industries. In 2011, the German government proposed Industry 4.0, a strategy that integrates new digital technologies to build a digital Industry 4.0 system [10]. Several researchers and famous companies like SIEMENS have formed development teams, trying to create a sophisticated and perpetual industrial system [11]. Among the 4.0 industrial projects, we mention the project of Professor D. Zuehlke who set up a business strategy for a smart and automated plant: production of intelligent products, remote control, wireless delivery, radio frequency identification, Bluetooth connection, tablets, and other technologies, mentioning that this is the first transformation model in 4.0 era [12]. In another approach, Doctor Ruth, manager of industrial activities in SIEMENS, maintains that the first stage of Industry 4.0 is the incorporation of a network of interfirms production. In second place, establishing virtual reality (use of simulation models and collection of real behavior numerically and in third place operating cyber-physics. In 2015, China offered a plan called 2025 made in China that intended to stimulate energy and innovation. It is a Chinese version of Industry 4.0 [13,14]. This plan led to worldwide significant financial support for manufacturing research,for example, by 2020, the European Union invested 7 billion euros in factories of the future [15].
Lee et al. [16] analyzed the innovation of service of Industry 4.0 and its huge databases. In fact, they explained the relationship between the creativity of service and Industry 4.0 based on the information flux collected from production lines, from predictions of the state of equipment, industrial model, and other aspects [16]. Luthra and Mangla [17] established a questionnaire of 96 questions to discover challenges and opportunities of India in Industry 4.0 [17]. Zhang [18] mentions two significations of Industry 4.0, namely, intelligentization and greenization, as well as the incorporation of network and CPS in the productive lines.
It provided examples of Chinese firms facing challenges of Industry 4.0 [18].
In the era of Industry 4.0, the advanced platforms for communication, computing, and control systems are integrated into cyber-physical systems (CPS) that incorporate a network of multiple production systems. CPS provides us with a new paradigm of smart factory, known as cyber manufacturing. Additionally, conventional smart manufacturing has relied on traditional data-driven decision-making methods aiming to achieve a better performance in the manufacturing process, such as statistical process control of individual manufacturing systems. In fact, smart manufacturing  is also called cyber manufacturing, which is handled by a CPS that constitutes a physical entity [19].
Cyber physic systems (CPS) have the potential to be further developed for managing big data and maintaining the interconnectivity of machines thus reaching the goal of intelligent, resilient, and self-adaptable machines [20]. By integrating a cyber-physic system into production, logistics, and services in the current industrial practices, we could transform today's factories into an Industry 4.0 factory with significant economic potential [21]. For that, Kang and his colleagues set up challenges of modeling and analysis in cyber-physical production which is a review from machine learning and computation perspective [22].
Modern companies are implementing cyber-physical production systems to meet fluctuating production demands. Indeed, we talk about an intelligent manufacturing system that is developed during the past decades and gaining nowadays further momentum thanks to the potential brought by the Industry 4.0 vision. We cite the study of A. Barari (2021) who makes an editorial of short historical and future perspective on intelligent and smart manufacturing systems, underlining its main characteristics [23].
Practically, G. Li-xiong suggested a simulation model of motorcycle-coating product line based on FlexSim [24]. In addition, he proposed a simulation platform for automobile mixed assembly line in the context of Industry 4.0 based on FlexSim software, which is controlled by a cyber-physical production system [25]. By to the literature, the number of research teams developing enterprise digitalization and Industry 4.0 platform has been increased. Ji et al. [26] introduced a big data analytics-based optimization method for enriched distributed process planning that focused only on machining. Woo et al. [27] proposed a smart manufacturing platform that is only dedicated for machining. In addition, Wan et al. [28] proposed a methodology to develop and implement a manufacturing big data system of active preventive maintenance.
In our paper, we present a simulation platform of brass accessories production workshop controlled by a cyberphysical production system and based on FlexSim. It is the setup of an integrated manufacturing system process, which resulted in reducing the spherical bushels' assembly time and controlling the production and the assembling process. It allowed us to increase the equipment utilization rate by comparing it with the company's former equipment running time prior to switching to smart manufacturing.
Nowadays, modern manufacturing industries have to incorporate an integrated manufacturing environment. To achieve this goal, the integration of material requirements planning (MRP) is essential to fulfill the needs of industrial system requirements (ISR). To develop a window-based application that helps the manufacturing industry to reach the best procurement practices and support the operation of optimal total cost procurement activity in the best conditions, due to the customized production, shorter product life cycle and frequent process reengineering give a rapid response to changing requirements, reduction in both time and cost of the product realization process [29]. Material requirements planning (MRP) philosophy is still employed by the majority of manufacturing enterprises for production planning [30]. MRP has been an effective way to consider the relationships between end items and various components and subassemblies. MRP systems determine the quantity of each material that will be used in the production of a mandated volume of finished goods. It determines when each of these materials must be purchased or manufactured to meet the prescribed due dates of the finished product. MRP systems are highly detailed and are an excellent means for determining and tracking material requirements through a master production program (MPP). In fact, MRP is generally considered as the main part of the production planning system. It defines the batch size and start time of all intermediate products or supply to guarantee the execution of the master production schedule.
MRP only provides the means to make broad scheduling decisions; it does not encompass short-term scheduling decisions like machine loading and operations sequencing [31]. Once the MRP has set due dates for each period, it is the responsibility of the shop floor scheduling system to fulfill these deadlines. This is a critical activity because the workstation load changes over time. There may be unexpected events such as machine breakdowns, shortage of raw materials, scrap, and rework; these complications could result in the actual lead-time differing from the planned lead time. The research on MRP is one of the focal points in the industrial field as well as production and supply operations. Bogataj and Bogataj [32] offered a review of 50 years of research achievements on MRP theory and discussed some possible directions in Industry 4.0. It has been used extensively in companies to get the right components to the right customers at the right time [33].
However, the method of material requirement planning (MRP) is not an optimization technique.
The aim of this paper is to propose an MRP prototype consisting of a simulation platform which is controlled by a cyber-physical production system and a master production program. The results of this platform are optimized using the load-capacity method of MRP2. It is the first approach that couples a cyber-physical production system with material requirement planning in the context of Industry 4.0.
This study's innovations consider both production process and the integration of MRP's concept in an integrated manufacturing system (simulation platform controlled by a dedicated CPPS and MPP). More specifically, the main contributions of this paper are: 1. Modeling and simulation of spherical bushels production and assembly workshop: building of a production and supply workshop simulation platform controlled by a master production program (MRP) and managed by a CPPS 2. Smart workshop platform optimization through Industry 4.0 restyled production planning: load-capacity adjustment method (MRP2) The rest of this article is organized as follows. In Sect. 2, we propose the conceptual context: approach introduction of modeling and optimization of brass' accessories production workshop. In Sect and the frame and characteristics of production lines and mixed assembly in Industry 4.0. In Sect. 3, we introduce the detailed design flowchart of our proposed method. In Sect. 4, we present the simulation platform for the production and mixed assembly workshop for smart factory. In Sect. 5, we present an adjustment load-capacity method for platform optimization. In Sect. 6, we present our findings and discussions, and in Sect. 7, we give our conclusions and project future works.

Conceptual context
Our research is part of a digital transformation project that we are carrying out in collaboration with a brass accessories manufacturing company. Across this paper, we aim to introduce a modeling and optimization approach of a brass accessories production workshop for a manufacturing company, ongoing digital transformation by adopting new digital technologies. Thereof, it is critical to insert one prism of analysis, which allows us to identify transformation levels and highlight the characteristics of technologies 4.0 needed in this manufacturing company.

Framework and characteristics of mixed production and assembly lines in Industry 4.0
The implementation of Industry 4.0 made it possible to produce intelligent products that perfectly meet customer requirements. Consequently, it is necessary to have an efficient manufacturing execution system (MES) that meets the requirements of smart factories: a flexible mixed product production and assembly line system; also known as the mixed assembly line. The mode of operation and function is almost the same in the mixed assembly line when different products (manufactured on site or purchased) are assembled continuously. This mode is widely applied for several types of products. The obvious difference between a mixed assembly line in Industry 4.0 and another traditional line is as follows: the first is a highly automated system through MES and controlled by CPS computer. For our case study, the CPS's mission is to control the assembly process: transmitting and processing information. Among the fundamental characteristics of a CPS are automation and interconnection of equipment, machine configuration, and intelligent process. Figure 2 illustrates the framework.
Our objective in this paper is to set up a simulation platform controlled by a cyber-physical production system and a master production program (Algorithm 1) for a brass accessories' production and assembly workshop. The developed simulation platform provides a basis for a future digital twin of the company.
The results of the simulation platform, and especially those of the master production schedule, have been optimized by a new redesigned approach to MRP 2 (Algorithm 2). Figure 3 presents a global view of our contribution. Algorithms 1 and 2 present respectively the production planning/material requirement planning (MRP) and the master production program optimization through the load-capacity adjustment method.

Proposed method
This study treats a real case of a mechanical company producing brass accessories. This company has started a digital transformation project to be hyper-connected and digital. This study consists of creating an Industry 4.0 simulation platform of their production and assembly workshop for spherical bushels (type A BS ¾ " FF and type B BS ½" MM) which is controlled by a CPS computer and a manufacturing execution system (MES). This company has a work rate of 8 h/day, with 12 operators per team. In this paper, our work will be presented in two parts as follows: • Modeling and simulation of spherical bushels' production and assembly workshop: building of a production and supply workshop simulation platform controlled by a master production program (MRP) and managed by a CPPS • Smart workshop platform optimization through Industry 4.0 restyled production planning: load-capacity adjustment method (MRP2) Figure 4 below illustrates the flowchart of the proposed method in this paper.

Simulation platform for production and mixed assembly workshop in Industry 4.0
The simulation platform is designed for production and assembly of accessories workshop ongoing a 4.0 transformation. In fact, spherical bushels are composed of  It should be mentioned that the products produced in the workshop are personalized and controlled by CPS, starting from the raw material stage to the assembly unit. While the master production program (MRP) supervises the supply of other products, it is integrated into the simulation platform via a code. Thus, the CPS and the master production program allows us to manage intelligently the production. Figure 5 below shows the spherical bushels' mixed production and assembly shop model for in Industry 4.0.

Spherical bushels' production process and operating range
In the production workshop, the cyber-physical production system (CPPS) controls the manufacturing process of the accessories and the assembly of the bushels. The latter is the heart of intelligent production. It integrates MRP, ERP, MES, and other function which are responsible for the entire life cycle of the production process.
For our case study, the workshop made the production of two types of spherical bushels: type A (BS ¾" FF) and type B (BS ½" MM). Tables 1 and 2 respectively illustrate the nomenclature of both products, as well as the operating ranges of accessories manufactured in the workshop.

Modeling of spherical bushels' production workshop for FlexSim software in Industry 4.0
The entire life cycle of products in the 4.0 era is integrated into the production system. In fact, the CPS controls and personalizes any production process. The process of producing accessories and assembling bushels is a complex system; an assembly station formed by two units can complete the multiprocessing. Referring to Table 2, spherical bushels are products made from products manufactured in the same shop   and others that are purchased. The simulation platform is made from three production lines: body A1, body B1, and cuff A2 and two assembly units for the bushels (type A and B). If all processes are treated together, the simulation model will be extremely complex. The main processes are modeled in this paper.
We note that our case study considers a company that has started a digital transformation project. For this, we model accessories' production: A1, B1, and A2 by a master production program (MPP) in the MRP context. 4.0 production planning requires a certain amount of data for its implementation. These are essential to establish such a master production program (MPP) which define the component requirements planning.
FlexSim is a three-dimensional simulation software, objectoriented for discrete systems. It supports C + + language. It is widely applied for manufacturing [24].
We recently built the bushels' production workshop model according to the FlexSim process. The simulation model consisted of more than 150 elements, 300 connections, and more than 1500 code lines. Now, we define the main entities of the simulation model as shown in Table 3.
Moreover, Fig. 6 shows the inputs and outputs of the simulation platform.
Hence, Fig. 7 below shows the simulation model of the FlexSim software of the spherical bushel production workshop.
According to Table 2, manufactured accessories respectively body A1, body B1, and cuff A2 are sent to the respective stocks A1, B1, and A2 (pre-assembly stocks). Upon arrival of the purchased accessories A3, A4, A5, A6, B2, and B3, all the accessories will be sent to the mixed assembly units according to the nomenclature mentioned previously in Table 1 (quantities). Finally, the two types of products will be checked by sampling before being sent to storage: PFA (storage for bushels type A) and PFB (storage for bushels type B).
The simulation modeling attributes of the FlexSim software and the production and assembly parameters of spherical bushels accessories are defined as follows:

The physical parameters of the source (raw material)
We want to apply a radical modeling transformation to the production workshop so that it can take the digital road; we proposed the MRP method in order to control the production and achieve a good management by using a master production program. Our new code-integrated program in our FlexSim simulation model will control production management automatically. Algorithm 1 details how to set planning requirements for the A1, B1, and A2 accessories. We introduced it in the simulation platform source. In order to find our component requirements, we introduce the gross need (BB) and the expected reception (RP) (lines 2-3). Thus, we can find our component requirements: projected stock (SP), net requirement (BN), start of order (DO), and end of order (FO) (lines [10][11][12][13][14][15][16][17][18][19][20][21]. This way, we receive valuable information about the production and supply planning. if

Queue parameters and processors parameters (machines)
In the workshop, the queue of raw materials is in the form of large boxes located in front of each production line.
Component requirements are planned using master production program (MPP) already introduced by the source module ticket. There will be no more congestion problems.
In Table 2 operating ranges, we have configured the parameters of the different machines (setup time, process time…). We identify the setup time in the machine settings: time per unit of product (configuration, raw material, positioning, etc.) and the process time is production time. These times can be achieved with or without human intervention depending on the operating range.

Setting the model stop time
In order to get the working status and FlexSim software data of the spherical bushel assembly system accurately, the production management in this simulation platform is programmed with a master production program. This program has been developed during an 8-week period (just for an example). Table 4 below shows the production orders for bushels type A and type B.
The model will be automatically stopped while the assembly of the bushels is finished (respectively for type A and B). The code is as follows.
If ((get output (courant) = 12,000)  And (get output (courant) = 11,500)) Stop () The parameters of system-simulated model are set and connecting each entity, and spherical bushels' mixed assembly system simulated model based on FlexSim software is built up.
Until now, we have reasoned hypothetically as if the production capacity was infinite to calculate the component requirements. We have proceeded to the calculation of the batches without worrying about their realization depending on the capacities of the available production resources. Thereof, it was necessary to optimize our simulation model before uploading the production program.

Platform optimization through the adjustment load-capacity method
In this section, the optimization of the master production program (MPP) is done through the load-capacity adjustment method. In fact, it will only focus on spherical bushels type A (BS ¾" FF) which is composed of accessories as listed in Table 1 (respectively in a similar way for type B bushels). It is advisable to calculate the generated loads first, which affect the batches to be produced and estimate their realization compared to the real capacity of the production resources.
With reference to the production department, there is a major problem of capacity for the accessories manufactured body A1, body B1, and cuff A2, which require a passage on production lines. However, since the components are purchased, they do not generate any load on the machining stations.
To calculate the loads, we refer to the manufacturing range file in Table 5.
It is important to point out that a work center is a virtual production unit, consisting of one or more activity centers, including machines and tools necessary for the execution of tasks. This unit of production is used in particular for the planning of capacity requirements. Table 6 below displays the workstation file where some information have to be defined; namely, the theoretical capacity is the maximum number of operating days (per week). Additionally, the minimum coefficient in percent is the percentage of time that stations are not operating.
We mention that the real capacity (actual capacity) is the number of days of (actual operation) in Eq. (1). where: • AC: actual capacity • C: minimum coefficient • TC: theoretical capacity We know that the processing station (TRF1, TRF3) has a real capacity of 4.8 days/week and the unit manufacturing time (Mt) of body A1 and cuff A2 are respectively calculated by referring to Table 5 as below: In order to obtain the loads on the various periods, we multiply the unit manufacturing time (Mt) by the production order (orders beginning) for each period, recently calculated by the master production program and sum it up for total loads.

Master production program: adjustment by stock
In this subsection, we present an MRP2 approach to optimize our master production program via an inventory adjustment method which takes into consideration the capacities of the production lines. Figure 8 is the simulation platform optimization model of the master production program through the load-capacity adjustment method by stock.
In order to have an agile and flexible simulation platform, the optimization of the master production program will take place. It consists of adjusting the production of the products manufactured in the workshop respectively body (A1) and cuff (A2). Our objective function is to minimize H(X, Y) , where X and Y represent respectively the production's orders of body A1 and cuff A2. We present the function and its constraints as below:

3
In the case where the function H(X, Y) is negative, i.e., the costs are higher than the production capacities (4.8 days), we anticipate the production of the product whose production cost is more than the other.
This anticipated production implies a storage that we plan to keep as cheap as possible; this leads to the choice of the cheapest item to store. Consequently, we must evaluate the unit accessories' storage cost of body and cuff, based the hypothesis that the storage cost of the article is proportional to its value (or its cost price).

Min H(X, Y)
For the development of a production adjustment method by stock, we adopt the following notations: • C i : the cost of the accessory i including the cost of its production as well as the cost of its components. We note Eq.  If P(i) = Φ ; the component is purchased; its cost price is calculated by Eq. (3) as below. It is simply its purchase price.
If P(i) ≠ Φ ; the component is manufactured; its cost price is defined by Eq. (4) below.
In the following, we present the tree structure of the spherical bushels type A (BS ¾" FF) as shown in Fig. 9.
In order to calculate costs and in reference to Table 1, it is advisable to start with the purchased items (A3, A4, A5, A6) and gradually move up the other levels until we reach the stage of finished product (BS ¾" FF).
For example, the accessory body A1 needs an axis unit A3 and two seal units A6 to compose a sphere unit A4 while the accessory cuff A2 uses brass for its manufacture which is purchased in diameter 23 bar (according to Table 2 of the operating range).
As a result, we can calculate the production costs of accessories body A1 and cuff A2 by using Eq. (4) as follows: We found that the cost of the body A1 is higher than the cost of the cuff A2.
The findings are no longer surprising given that the body A1 is more elaborate than the cuff A2. According to the adjustment by stock method, we will anticipate the production of the cheapest item to stock, such as the cuff accessory. We could also correct the start of the production orders while anticipating the production of the accessory cuff A2.
Hence, Algorithm 2 details how to optimize the master production program through the load-capacity adjustment method by anticipating production in the simulation platform source. (3) In order to optimize the master production program, we introduce some inputs such as orders' first-stage manufacture of accessories body A1 and cuff A2 (lines 2-3). Indeed, we calculate the generated loads by the produced accessories in (lines 9-11). Then, we calculate the sum of the generated loads (line 12) and our function objective H already defined in the above (line 13). We look to minimize our objective function and find the updated loads (lines [15][16][17][18][19][20].
In the next section, we present our findings and our optimization results through the load-capacity adjustment method. 6 Results and discussions

Statistical data of accessories' production lines and material requirement planning
Since the compilation of the simulation platform is finished, i.e., the production of 12,000 spherical bushels type A (BS ¾" FF) and 11,500 of type B (BS ½" MM) has been assembled.
Once the operation of the software is finished and the virtual dashboard installed in the simulation platform, we observe the following: Tables 7 and 8 below display respectively the component requirements planning as a result   Table 7 Component requirements planning for bushel type A: BS ¾" FF  0  1500  3000  1500  1000  1000  1000  1000  1000  Orders' beginning 3000  2500  1500  1000  1000  1000  1000  1000  0  Orders' end  0  1500  3000  1500  1000  1000  1000  1000  1000 displayed by the master production program for the product type A and type B from the simulation platform. In fact, this planning allows the determination of the components' quantities necessary for the elaboration of the master program: bills of material and storage reports. We obtain the firstlevel component requirements starting from processing the production order until reaching the finished product (level 0). We determine the second-level requirements from those of the first level. We keep on repeating this process at each level until we reach purchased accessories. We mentioned that the master production program processed economic quantity without taking into account neither the rates of the machines nor the generated loads of manufactured products: body A1; cuff A2, and body B1. In the following section, we will propose an approach to optimize the simulation platform: method of load-capacity adjustment.
Clearly, the findings of the master production program such as the orders' beginning reflect the adequate moment when we should launch production in order to answer costumers' orders. We have also noticed that the supply of produced quantities is done with a one period delay. We mention the example of period three order of 1500 BS ½" MM; this quantity should be produced in period two.
The supply of accessories produced in the workshop and other purchases are integrated into the master production program with a delay of 1 week (period). This allows to have an agile and flexible simulation platform that meets the requirements of 4.0 era.
The data and performance indicators of the accessory production's lines body A1, body A2, and cuff A2 are required, as shown in Table 9. In addition, we pick up the statistical data of the mixed assembly units PMO1 and PMO2 listed in Table 10.
In Figs. 10, 11, 12, 13 and 14, we outline the production lines' performance diagrams of manufactured accessories body A1, body B1, and cuff A2 respectively and those of the mixed assembly units PMO1 and PMO2.

Optimization of master production program through load-capacity adjustment method
As stated above, according to stock adjustment method, we will anticipate the production of the cheapest item to stock, such as the cuff accessory. We correct the first stage of production orders while anticipating the production of accessory cuff A2 as follows.
In order to optimize our MPP, we will need to calculate the loads generated by the manufactured products body A1, body A2, and cuff A2. For this purpose, it is enough to multiply the unit manufacturing time (Mt) by the production order (orders beginning) for each period, recently calculated by the master production program and sum up the total loads. Table 11 and Fig. 15 below illustrate loads on the manufacturing station generated by the manufactured accessories body A1 and cuff A2.
We note a deficit in capacity in period 4 and 5 of 0.87 + 0.52 = 1.39 days, which is possible to solve according to stock adjustment method (Figs. 3 and 8) of the master production program (MPP). Algorithm 2 generates automatically the new beginnings of production order of cuff accessory A2.
In order to anticipate article production of periods 2 and 3, where there is an excess capacity of 0.8 + 0.8 = 1.6 days, we use 0.8 days in period 3 and complement 1.39 − 0.8 = 0.59 days in period 2 (which is possible since the surplus of this period is 0.8 days).
In period 5, the deficit of capacity is of 0.52 days, which presents 0.52/0.0017 = 306 units to be entrenched from the beginning orders; that is, 1000-306 = 694 units. In fact, this deficit can be filled by using the surplus of capacity of 0.8 days in period three. Therefore, these 306 pieces are more to produce in period 3 than initially planned.
In period 4, the capacity deficit is 0.87; this presents 0.87/0.0017 = 512 units to be entrenched from the beginning orders; that is, 900-512 = 388 units. However, this deficit can be filled by using the surplus capacity which is now equal to 0.8-0.52 = 0.28 days in period 3. The 512 units are to be produced as follows: 0.28/0.0017 = 165 units in period 3 and the 347 units (512-165) remaining in period two. Since we have a surplus of 0.8 days during this period, we will be able to produce up to 0.8/0.0017 = 471 units. Thus, the beginning orders in period three will be 1000 + 306 + 165 = 1471 and in period two it will be 1000 + 347 = 1347 with an excess capacity of 0.8-0.59 = 0.21 days. Table 12 below provides an update of the beginning production orders of body A1 and cuff A2 in order to optimize the master production program (MPP).  1000  0  0  0  0  0  0  0  0  Net requirements  0  500  2500  1500  1000  1000  1000  1000  1000  Orders' beginning 2500  2500  1500  1000  1000  1000  1000  0  0  Orders' end  0  500  2500  1500  1000  1000  1000  1000  1000  Table 9 Statistical data and performance indicators of the production's lines   The optimization of master production program (MPP) through load-capacity adjustment method allowed us to manage the planning of component requirements for the various commands over an 8-week horizon.
By optimizing the MPP, we noticed that the time required to produce 12 k bushels of type A (BS ¾" FF) increased from 3072 to 3289 min and we also registered a time increase by 191 min (from 3624 to 3815 min) in the production of 11.5 k bushels of type B (BS ½" MM). In fact, the times found are not surprising, given that the planning and management of the production were well optimized.
Thus, it is stated that the optimization of the master production program and its integration into the simulation platform   has been interesting and had remarkable results. According to the statistical data of mixed assembly, the planning of component requirements allows the company to deliver these 8-week commands from the beginning of the second week. The BS ¾" FF bushels, the 12,000 items, are produced in a time of 3289 min, which is worth 54.8 h and then 6.85 days. Given that, the company works 8 h/day and for 6/7 days. The BS ½" MM bushels and the 11,500 articles are produced in 3815 min, which is worth 63.58 h and then 7.94 days. Figures 16 and 17 illustrate performance indicators of assembly units PMO1 and PMO2 flowcharts, after compilation of the model with the updated master production program (MPP), as below.

Conclusion
With the emerging technologies, such as big data, cloud computing, and cyber-physical production systems (CPPS) coupled with simulation and modeling technologies, we could implement a 4.0 smart factory. The smart configuration of machines and products communicate and negotiate with each other to reconfigure themselves for a flexible production of multiple types of products.
In this paper, we proposed a successful future implementation of Industry 4.0. This technology saves the company time and money and results in huge economic benefits. During this study, we elaborated a workshop's simulation platform whose production and supply are handled by a master production program (MPP) and controlled by a CPS. In this platform, we model and simulate production and mixed assembly of spherical bushels. In order to achieve an intelligent production and scheduling, we optimize the simulation platform through a smart workshop and Industry 4.0 approach of MRP2 for production planning: we are talking about the load-capacity adjustment method.
In fact, simulation and modeling technologies can be very useful for multiple objectives and complex processes and also for making a road map for digital transformation projects in the context of Industry 4.0.
The optimized findings show that the platform simulation and method proposed in this paper are a good guide and reference for a company digital transformation to the 4.0 era. We mention that this method can be generalized for any sector and even for any manufacturing factory.
The smart platform helps implementing the sustainable production mode to cope with the global challenges. It could lead to novel business modes and even affect our lifestyle.
As a perspective, this simulation platform handled by a CPPS and MPP presents a basis for a future digital twin of the spherical bushels production facility.
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Competing interests
The authors declare no competing interests.