**Contents**



### **Preface to "Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools"**

Over the last twenty years, most companies and researchers have tended to employ a company-centric view of the supply chain. From this perspective, the supply chain (SC) is perceived to consisting of the enterprise as a central entity, potentially together with some peripheral partners— typically, first-tier suppliers and customers. These views involve integrating production and logistics planning across the enterprise, value-chain management, global network planning, and investment appraisal. Research on the "**extended**" **supply chain**—where the view is much broader, e.g., **encompassing the suppliers' suppliers and the customers' customers**—**is far less prevalent**. This is almost certainly due to **(i) the relative infancy of the discipline, and the fact that considerable benefits can be achieved simply using company-centric views of the supply chain; and (ii) a wariness of supply-chain "partners" and a lack of data sharing.**

The aim of creating this research topic is to a) achieve enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation, and optimization methodologies, and b) create computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from local basic control and supervisory modules to the scheduling/planning formulation. This makes it possible to dynamically react to incidents occurring in network components at the appropriate decision-making level, reducing the amount of resources required, emitting less waste, and facilitating better responsiveness to the changing market requirements and operational variations. This reduces the cost, waste, energy consumption, and environmental impacts, increasing benefits.

Moreover, the increase in globalization has significantly increased the scale and complexity of current businesses. Businesses have become global networks of multiple business units and functions. Operational functions include R&D, production networks (continuous, discontinuous, and discrete), and supply networks. These functions are supported by financial planning and marketing strategy functions. Furthermore, businesses are also subject to internal and external uncertainties. Internal uncertainties include the unpredictable success rate of R&D projects; the technological risks involved; and disruptions to production, such as production failures and unforeseen stoppages. External uncertainties include the fluctuating cost of raw materials and products (unless they are subject to monopoly fluctuations in the exchange rate), and uncertainties in market size and demand due to competition and macroeconomic factors. Businesses control their operations through their decisions regarding capital expenditure, company finances, growth strategies, and operations. Strategic decisions on capital spending and planning include the technology used, the choice of R&D projects, and decisions regarding infrastructure and supply-chain managemen<sup>t</sup> (SCM). Financial decisions are made by identifying the assets and liabilities required from the working capital for larger projects and operations, assessing, and protecting the company from, change risk. Examples of tactical production decisions include planning activities in plants which run on a discontinuous basis to respond to an anticipated demand, making decisions about the sources of energy used following market prices, and increasing production capacity in response to the demand-related pressures.

The current solutions in the field of process systems engineering (PSE) and operations research tend to only consider subsets of such decisions, even though a business must act as a cohesive body in which its various functions are, to a certain extent, coordinated. Therefore, from the perspective of a company, the overall performance will be suboptimum if strategic and tactical decisions are made independently, as has been the case to date. However, it is significantly more complex for a company to make decisions that involve its overall interests than it is to make decisions about specific functions. This explains why research with integral modeling that reflects the overall operation of companies is scarce.

The PSE community faces an increasing number of challenges; to address these challenges, enterprise and SCM remain subjects of significant interest because they offer multiple opportunities. It is anticipated that further progress in this area will bring a unique opportunity to demonstrate the potential of the PSE approach to enhance a company's value. As previously mentioned, one of the critical components of SCM and EWMO is decision-making coordination and integration at all levels. Most recent contributions offer models that separately address problems arising in the three standard supply chain (SC) hierarchical decision levels (i.e., strategic, tactical aggregate planning, and short-term scheduling).

More recently, integrating new technologies through semantic models in formal knowledge models allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge of comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have directed the attention of manufacturing enterprises toward intelligent manufacturing systems. This Special Issue also invites contributions from these advanced areas.

In summary, this Special Issue addresses the following concepts:

	- ◦ **The network design problem**, such as the location of the plant, warehouses, and distribution centers, capacity and technology selection, etc.;
	- ◦ **The supply chain planning problem**, including distribution planning, inventory control, and product demand forecasting;
	- ◦ **The integration of production, financial and environmental aspects, risk, and uncertainty.**

The expected models will tackle a **multi-objective** view of achieving the necessary trade-off between often contradictory benefits in terms of economic and environmental benefits, customer satisfaction, and increased response to dynamic market changes:


**Luis Puigjaner, Antonio Espu ˜na Camarasa, Edrisi Mu ˜noz Mata, and Elisabet Cap ´on Garc´ıa** *Editors*

#### *Editorial* **Expanding the Horizons of Manufacturing, towards Wide Integration, Smart System, and Tools**

**Luis Puigjaner 1,\*, Antonio Espuña 1, Edrisi Muñoz 1 and Elisabet Capón-García 2**


This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. Such an integrated approach incorporates information from the local basic control and supervisory modules into the scheduling/planning formulation, making it possible to react dynamically to incidents occurring in the network components at the appropriate decision-making level.

A wide-integrated solution should allow enhanced coordination and cooperation between network components by avoiding competition, eventually leading to local optima and inefficiency associated with inconsistent isolated decisions at different levels. Such a wide-integrated solution approach would provide new structural alternatives, more effective managemen<sup>t</sup> policies, more economical design options. Moreover, the solution obtained can work in practice requiring fewer resources, emitting less waste, and allowing for better responsiveness to changing market requirements and operational variations, thus reducing cost, waste, energy consumption, environmental impact, and increased benefits.

More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows capturing and utilizing domain knowledge, human knowledge, and expert knowledge towards comprehensive intelligent management. Otherwise, the development of advanced technologies and tools such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, artificial intelligence, big data, cloud computing, and blockchain, have captured the attention of manufacturing enterprises toward intelligent manufacturing systems. This Special Issue also calls for contributions from these advanced areas.

In summary, we look for articles addressing (but not limited to) the following concepts:


The expected models will tackle a multi-objective view of achieving the necessary trade-off between often contradictory benefits in terms of economic, environmental, customer satisfaction, and increased response to dynamic market changes:

the development of detailed production scheduling at the plant level for batch, continuous and discrete manufacturing for online scheduling implemented in practice under real-time variations and uncertainty;

**Citation:** Puigjaner, L.; Espuña, A.; Muñoz, E.; Capón-García, E. Expanding the Horizons of Manufacturing, towards Wide Integration, Smart System, and Tools.*Processes* **2022**, *10*, 772. https:// doi.org/10.3390/pr10040772

Received: 10 March 2022 Accepted: 12 April 2022 Published: 14 April 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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In the following, you will find selected contributions (original research, reviews, opinions, and perspectives) regarding this research topic. They bring novel solution approaches accompanied by rich case studies and examples of practical interest.

#### **1. Original Research**

The articles in this Special Issue examine different facets of enterprise-wide modeling and optimization through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network.

The contribution by David Bogle et al. [1] addresses the present and future situation of the high complexity inherent with batch manufacturing of many products. The intrinsic flexibility associated with batch manufacturing has been the choice of most pharma products manufacturing. As a result, it is usual to see a battery of batch reactors in the pharma industry or single batch reactors with high and complex inlet and outlet pipe connections, some of them unused, because different or new products appear in the industry portfolio. The net result is increasing the cost of the final product. Instead, this original paper offers a novel approach: The use of operational envelopes to study the trade-off between the design and operational flexibility of a tablet manufacturing process. Moreover, using an alternative adaptive sampling technique will alleviate the significant computational burden associated with the operational envelopes. Finally, a critical fluidized bed dryer case study at the heart of the continuous manufacturing of tablets supports the paradigm shift change to continuous manufacturing.

The paper by M. Ziyan Sheriff et al. [2] also examines the transition from batch to continuous processes in the pharmaceutical industry. In order to enable the quality-bycontrol (QbC) paradigm to move forward, this work developed and presented a moving horizon estimation-based nonlinear model predictive control (MHE-NMPC) framework to accomplish the dual requirement of accurate estimation and efficient control. The realtime implementation feasibility of the developed framework was also discussed, and the ability of the proposed framework to solve the optimization problem at each time step in a manner that enabled real-time implementation was highlighted. The practical applicability of the developed framework was corroborated through two realistic case studies that incorporated the effects of glidant to better control CQAs such as the tensile strength. Both examples demonstrated the ability of the framework to achieve reasonable control performance despite the presence of varying sources and degrees of plant model mismatch.

The work by Zixue Guo et al. [3] proposes an evaluation model that addresses the problem of fuzziness and randomness in regional logistics decarbonization, assessing its development. An evaluation index contemplates three dimensions: low-carbon logistics environment support, low-carbon logistics strength, and low-carbon logistics potential. Secondly, the evaluation indexes serve as cloud model variables, and the cloud model theory determines numerical characteristic values and cloud affiliation degrees. Finally, the entropy weight method determines the index weights and calculates the comprehensive determination degree of the research object affiliated with the logistics decarbonization level. Finally, the Beijing–Tianjin–Hebei region is the example used for empirical evidence, analyzing the development of logistics decarbonization and its temporal variability in Beijing, Tianjin, and Hebei provinces and cities. The study results show that the development of logistics decarbonization in Beijing, Tianjin, and Hebei Province has improved to different degrees from 2013–2019, but the development is uneven. Developing to 2019, the three provinces and cities of Beijing, Tianjin, and Hebei still have significant differences in terms of the economic environment, logistics industry scale, logistics industry inputs and outputs, and technical support.

In their article, Vivek Dua et al. [4] aim to introduce a method for designing multi setpoint explicit controllers for nonlinear systems through recent advances in multi-parametric programming. Multi-parametric programming (mp-P) has received considerable attention from the process systems engineering community because of its unique ability to aid in the design of explicit model predictive controllers and thus shift the computational burden associated with offline control. The authors examine a case of multi-parametric nonlinear programs (mp-NLPs) that involve both endogenous uncertainties, in the form of left-hand side parameters (LHS), as well as exogenous uncertainty in the cost coefficient of the objective function (OFC), and, on the right-hand side of the constraints (RHS), uncertain parameters on the right-hand side (RHS). In engineering problems, LHS uncertainty arises from variations in model coefficients, due to parameter estimation errors or model mismatch; OFC uncertainty arises due to fluctuation in market prices or control penalties while RHS uncertainty can be due to varying system exogenous factors. The contribution of the present work is a novel framework for the design of multi set-point explicit controllers for nonlinear process systems.

As a demarcation of the past, present and future of intelligent systems, a Tri-X Intelligence (T.I.) model is proposed in this paper by Baicun Wang et al. [5] to state the mechanism, factors, and connotation of three main entities (conscious humans, physical objects, and cyber entities), including single-X intelligence, two-X integrated intelligence, and three-X complex intelligence. Every single entity shows primitive intelligence. Twoentity integration creates integrated intelligence. Three-entity fusion generates advanced intelligence. The intelligentization mechanism of artificial systems continuously converts human intelligence to machine intelligence via different channels and interfaces. With the increasing use of machine intelligence, humans will gradually play a less significant role in intelligent systems. However, human intelligence will keep influencing artificial systems in the form of software/algorithms to drive intelligent systems. Therefore, we cannot take humans out of the systems given the accelerating development of technology. The key to success is to adapt humans to new work environments, i.e., not to replace but to enhance. According to the Tri-X Intelligence (T.I.) model, humans need to think more about how to collaborate with cyber systems rather than with intelligent systems, a Tri-X Intel than training operators to work like computers. The proposed Tri-X model (e.g., Human-Cyber-Physical-System HCPS) will integrate the intelligence in the complex system with a combination of human-cyber-physical and machine subsystems.

In addition, Sujeon Baek et al. [6] prepared a testbed for conducting a pick-up operation using a vacuum gripper with a single suction cup. Using the proposed method, the air pressure in the Venturi line was automatically monitored in real-time. When a command for starting suction was provided to the gripper, a sharp decrease in the collected air pressure signals appeared at approximately 0.5 s. However, the same decline was not observed in the signal for faulty box surfaces; consequently, the suction action and the corresponding gripper operation were not performed owing to insufficient contact between the suction cup(s) and the contact surface of the object. Using the early detection results derived from the air pressure signal analysis, a prediction-based process adjustment method for the pick-up operation was proposed. Through pick-up experiments using the developed testbed, it was revealed that the z-position of the suction cup significantly affects whether an object is gripped adequately by the vacuum gripper or not. Therefore, it is possible to determine a possible error situation in advance (before the failure of the lifting operation) and provide appropriate feedback control commands so that the target operation is finished successfully without stopping machine operations.

The process, manufacturing, and service industries face many non-trivial challenges in a customized market environment, from product conception, design, development, commercialization, and delivery. Thus, industries can benefit by integrating new technologies into their day-by-day tasks gaining companies profitability. Puigjaner et al. [7] present an integrated model framework for enterprise process development activities called a "Comprehensive intelligent managemen<sup>t</sup> architecture model for integrating new technologies for services, processes, and manufacturing who strive for finding the most efficient way towards enterprise and process intelligence." The model comprises and structures three critical systems: process, knowledge, and transactional. As a result, analytical tools belonging to process activities and transactional data systems are guided by a systematic development framework consolidated with formal knowledge models. Thus, the model improves the interaction among processes lifecycle, analytical models, transactional systems, and knowledge. Finally, a case study systematically presents an acrylic fiber production plant applying the proposed model, demonstrating how the three models described in the methodology work together to systematically achieve the desired technology application of life cycle assessment. The results conclude that the interaction between the semantics of formal knowledge models and the processes-transactional system development framework facilitates and simplifies new technology implementation along with enterprise development activities.

Compared with other fossil fuels, natural gas (N.G.) is considered a sustainable and potential energy source in the future. Being liquefied, natural gas (LNG) is 600 times smaller than the gaseous state of N.G., LNG becomes especially attractive if obtained at a competitive cost. The authors of this article, Liang Zhao et al. [8], show that modeling and optimizing the LNG terminals may also reduce energy consumption and GHG emissions. In this work, the authors propose an operational optimization model of the LNG terminal to minimize the energy consumption of boil-off gas (BOG) compressors and low pressure (L.P.) pumps. Finally, an MINLP model determines whether the pumps are running or on standby, and the number of compressor level chosen as a binary variable. The model can propose operating strategies for varied flow rates of the send-out speed, and the ambient temperature can be offered using the model. An actual case study on the LNG terminal is presented to indicate the effectiveness of the proposed approach. Finally, the optimization model provides the minimum energy consumption and the corresponding decision variables. The optimized compressor load and recirculation flow rate were 8.44 t/h and 122.58 t/h, respectively. Compared with the previous period, 26.1% of energy can be saved after optimization. About 16.21% of energy consumption can be saved annually.

In his challenging article, Heinz A. Preisig [9] presents "Reductionism and splitting application domain into disciplines and identifying the smallest required model-granules, termed "basic entity" combined with systematic construction of the basic entities, yields a systematic approach to process modeling." They do not aim toward a single modeling domain, but enabling specific application domains and object inheritances to be addressed. They start with reductionism and demonstrate how the basic entities depend on the targeted application domain. They use directed graphs to capture process models, and introduce a new concept, which they call "tokens," that enables the extension of the context beyond physical systems. The network representation is hierarchical to capture complex systems. The interacting basic entities are defined in the leave nodes of the hierarchy,

making the overall model the interacting networks in the leave nodes. Multi-disciplinary and multi-scale models result in a web of networks. They identify two distinct network communication ports, namely, ports that exchange tokens and ports that transfer information of tokens in accumulators. An ontology captures the structural elements and the applicable rules and defines the syntax to establish the behavior equations. Linking the behaviors to the fundamental entities defines the alphabet of a graphical language. They use this graphic language to represent processes which have proven to be efficient and valuable. Then, a set of three examples demonstrates the power of graphical language. Finally, the Process Modelling framework (ProMo) implements an ontology-centered approach to process modeling and uses graphic vocabulary to construct process models.

In this article, Ignacio Grossmann et al. [10] address an inventory managemen<sup>t</sup> problem for a make-to-order supply chain with inventory holding and-or manufacturing locations at each node. The lead times between nodes and production capacity limits are heterogeneous across the network. This study focuses on a single product, a multi-period centralized system in which a retailer is subject to uncertain stationary consumer demand at each time period. The authors consider two sales scenarios for unfulfilled demand: backlogging or lost sales. The daily inventory replenishment requests from immediate suppliers throughout the network are modeled and optimized using three different approaches: (1) deterministic linear programming, (2) multi-stage stochastic linear programming, and (3) reinforcement learning. The performance of the three methods is compared and contrasted in terms of profit (reward), service level, and inventory profiles throughout the supply chain. The proposed optimization strategies testing occurs in a stochastic simulation environment built upon the open-source OR-Gym Python package. The results indicate that stochastic modeling yields the most significant increase in profit of the three approaches. In contrast, reinforcement learning creates more balanced inventory policies that would potentially respond well to network disruptions. Furthermore, deterministic models perform well in determining dynamic reorder policies comparable to reinforcement learning in terms of their profitability.

In an inspiring novel article, Zeinab Shahbazi and Yung-Cheol Byun [11] bring the latest developments and experimental results on smart manufacturing. The modern industry, production, and manufacturing core is developed based on smart manufacturing (S.M.) systems and digitalization. Smart manufacturing's practical and meaningful design follows data, information, and operational technology through the blockchain, edge computing, and machine learning to develop and facilitate the smart manufacturing system. This process's proposed intelligent manufacturing system considers the integration of blockchain, edge computing, and machine learning approaches. Edge computing balances the computational workload and similarly provides a timely response for the devices. Blockchain technology utilizes the data transmission and the manufacturing system's transactions, and the machine learning approach provides advanced data analysis for a vast manufacturing dataset. Finally, the model solves the problems using a swarm intelligence-based method regarding intelligent manufacturing systems' computational environments. The experimental results present the edge computing mechanism and similarly improve the processing time of a large number of tasks in the manufacturing system.

Present increasing regulatory demands force the pharmaceutical industry to invest its available resources carefully. That is especially challenging for small and middlesized companies. For example, computer simulation software such as FlexSim allows one to explore variations in production processes without interrupting the running process. Claus-Michael Lehr et al. [12] applied a discrete-event simulation to two approved filmcoated tablet production processes in this article. The simulations were performed with FlexSim (FlexSim Deutschland, Ingenieurbüro für Simulationsdienstleistung Ralf Gruber, Kirchlengern, Germany). Process visualization required the use of Cmap Tools (Florida Institute for Human and Machine Cognition, Pensacola FL, USA), and statistical analysis used MiniTab® (Minitab GmbH, Munich, Germany). The most critical elements identified during model building were the model logic, operating schedule, and processing times.

These factors required graphically and statistically verification. In addition, employee utilization optimization required three different shift systems to be simulated, revealing the advantages of two-shift and one-and-a-half-shift systems compared to a one-shift system. Finally, without interrupting any currently running production processes, we found that changing the shift system could save 50–53% of the campaign duration and 9–14% of the labor costs. In summary, we demonstrated that FlexSim, mainly used in logistics, can also be advantageous for modeling and optimizing pharmaceutical production processes.

Tibor Krenicky et al. [13] present a study on the surface quality dependency on the selected parameters of cuts made in Hardox by abrasive water jet (AWJ). The authors applied the regression process to measured data and prepared the Ra and Rz roughness parameters equation. One set of regression equations describes the relationship of Ra and Rz on cutting parameters—pumping pressure, traverse speed, and abrasive mass flow rate. The second set of regression equations describes relationships between the declination angle in kerf as the independent variable and the Ra or the Rz parameters as dependent variables. Finally, the models can predict cutting variables to predict the surface quality parameters.

The complexity of the automated guided vehicles (AGV) system requires substantial decision-making and is challenging to solve. The authors Adrian Kampa et al. [14], use the flexible manufacturing system solution with the associated AGV transport system and discuss such systems' design and simulation issues. The initial system design optimization stage is crucial, and computer simulation enables relatively easy elaboration and testing of various manufacturing and logistics systems variants. On the other hand, excessive simplifications may appear applied at the modeling stage, making the simulation not reflect the production system properly. On the other hand, it is worth noticing that detailed modeling is very labor-intensive and requires the involvement of experienced specialists. Therefore, choosing which parameters to use in the modeling process and which metric to evaluate the model. Finally, to make the simulation more accurate and assess the system's productivity, the authors propose using overall equipment effectiveness (OEE) metrics. The results obtained from the presented simulations show that the OEE metrics may be helpful in the modeling and productivity evaluation of manufacturing and logistics systems, with the generalization of overall factory effectiveness (OFE) and overall transport effectiveness (OTE). The use of OEE factors also allows for comparison of the results obtained from different manufacturing systems. For example, many of them with OEE scores lower than 45% in the real world and a small number of world-class companies have an OEE value higher than 85%. Accordingly, the simulation results can also be helpful in analyzing the costs involved in implementing a given project and at the stage of the in-depth design of the production system.

The following work addresses the closed-loop stability problem with an application to refinery preheats trains' online cleaning schedule stability problem under fouling. Lozano Santamaría and Sandro Macchietto [15] focus on the sources of instability and ways to mitigate it. The various metrics developed to quantify schedule instability for online scheduling account for distinct aspects, such as changes in task allocation, task sequence, starting time of the task, and the earlier or later occurrence of such changes in the future scheduling horizon. Based on the proposed methods, further stability metric variations could be quickly developed (for example, ways of assigning weights to distinct contributions to a schedule change). These stability considerations can be practically and, in a rather general way, introduced in a closed-loop nonlinear model predictive control (NMPC) formulation of the optimal scheduling and control problem and solved online over a moving horizon, in terms of penalties in an economic objective or via additional constraints. The above methods demonstrated to be helpful for the online cleaning scheduling and flow control of refinery preheat trains, a challenging application with significant economic, safety, and environmental impact. A demanding industrial case study followed an illustrative, small but realistic case study. Results show that, of the three alternatives evaluated, the terminal cost penalty proved to be inefficient in this case. The other two (fixing some predictions horizon decisions and penalizing schedule changes between consecutive evaluations) improved the

closed-loop schedule stability against various economic penalties. The results highlight the importance of including stability considerations in an economically oriented online scheduling problem to obtain feasible solutions for operators over long operating horizons without sacrificing the benefits of a reactive system to reject disturbances or take advantage of them. The application of the metrics developed in this manuscript is not restrictive to the specific closed-loop NMPC scheduling implementation detailed here. They are helpful to assess schedule stability in general regardless of how schedules are calculated, only relying on the existence of two consecutive evaluations or predictions of the schedule within a common period. The two successive instances may have different control horizons, scheduling horizons, or update frequency. Lastly, although this work dealt with a specific application (the optimization of refinery heat exchanger networks subject to fouling), the formulations and solution approach demonstrated here should apply to important systems, such as batch and semi-continuous processes.

Mohammed Alkahtani et al. [16] enlighten the managemen<sup>t</sup> of the man-machine interaction as essential to achieving a competitive advantage among production firms and specifically more highlighted in the case of processing agricultural products. The authors design a non-derivative technique to integrate an algebraic approach in the agri-product based supply chain to optimize the resources and coup with variable demands through a controllable production rate. The analysis provides a platform for manufacturing managers to invest in advanced technology in agricultural supply chain managemen<sup>t</sup> (agri-SCM), leading to a less rejection production environment for clean manufacturing. The solution methodology of the proposed model included manufacturing limitations in the integration of the objective formulations with the developed system. The authors use sensitivity analysis to evaluate sensitivity for an optimal solution to the value of uncertain parameters, providing confidence in the model's resolution. Managerial insights are beneficial to agricultural supply chain managemen<sup>t</sup> (agri-SCM) for the agri-food processing industry, and the people with cleaner production and carbon emission prioritized policies. The authors can extend the research into a three-echelon agri-SCM model by considering the farming industry and agri-retailer. The fuzzy set theorems can deal with costs, prices, inflation, and time value uncertain factors. Finally, the authors envisage a feasible conversion of the deterministic model into probabilistic or stochastic theorems for application in real scenarios. Overall, the agri-product supply chain requires global development to make food more secure and accessible.

In the following article, Dejan Gradišar and Miha Glavan [17] consider a manufacturing problem requirement plan. This plan must satisfy the capacity needs and be available by the work order's due date. In addition to this, the program must also consider a group of work orders to produce from the same batch of raw material. In this way, the manufacturer can systematically compensate for some undesirable variations in raw material quality. In day-to-day practice, the plan managemen<sup>t</sup> makes it challenging to maintain the plan up-to-date, even in smaller dimensions. As a result, the operator's decisions are time-consuming and prone to errors. That results in situations in which the operator must constantly make plan corrections. Finally, this paper proposes using an extended bin-packing problem formulation to solve the material planning systematically. Finally, a fundamental bin-packing problem (BPP) formulation requires an extension to include constraints such as variable bin and item sizes. For example, one can use time limitations and only a group of bins to produce one group of items. The suggested solution offers a tool for supporting the production planner's decisions. With it, they can determine how to efficiently cut the raw material to satisfy the planned work orders. Depending on the situation, the planner can choose between various model formulations. Additionally, they can optimize the leftover, tardiness, or both. Finally, we demonstrated that the proposed solution could quickly solve a problem of realistic dimensions to be of use in an industrial application. However, case-specific requirements would first need to be analyzed to prioritize the importance of leftovers and/or tardiness in real applications.

Andrzej Paszkiewicz et al. [18] propose a novel approach for integrating the distributed additive manufacturing process enabling remote designing, selecting appropriate manufacturing means, and implementing a physical production process and control at all stages. This approach was possible thanks to the development of an unprecedented framework. The authors integrated distributed and functionally different elements (Informative Technology (I.T.) and manufacturing), forming a coherent design and manufacturing system. Importantly, this framework ensures an increase in production efficiency, shortens production time, reduces costs, and increases flexibility and accessibility to the latest methods and design and manufacturing tools. In addition, they presented a mechanism that facilitates the integration of independent manufacturing environments by considering and implementing appropriate levels of maturity in the system. The implementation in a natural production environment, i.e., at Infosoftware Poland, confirms the proposed solution's validity. At present, work is in progress to integrate the rapid prototyping laboratory of the Rzeszów University of Technology. In addition, the automotive and aerospace industries can widely use the presented platform. In addition, it will facilitate cooperation between industrial clusters and academic centers to a higher degree and encourage collaboration between small enterprises and startups. Finally, from the perspective of management, the technical implementation of the presented framework allows one to adapt to the needs of globalization and facilitates the integration of distributed resources. Thus, this framework affects business, logistics, and technological processes. One of the implications of implementing such a framework is the need to develop or adapt existing workflows to the new heterogeneous and distributed work environment.
