Keywords

1 Introduction

The fourth industrial revolution, widely known as Industry 4.0, has transformed research and practice in many fields; supply chain management is no exception to this trend. Industry 4.0 is formed around digitalizing processes, making them interconnected, and interoperable, while ensuring that they occur in a smart environment enabled by real-time data-driven decision-making (Gunnarsson et al., 2006). Industry 4.0 and its enabling technologies have the potential to improve individual performance in every corner of the supply chain by improving productivity and flexibility, resource and energy efficiency, as well as waste reduction. However, the effective integration of processes and activities is essential to fully exploit the advantages of supply chain digitalization at a systemwide level (Ghobakhloo et al., 2021).

Achieving greater degrees of integration requires considering the impact of individual decisions on the performance of other players. Individual decisions can be well-informed and aligned with the bigger goal of visibility across supply chain collaborators. In addition to reduced costs and response time, enhanced integration has implications for improving supply chain quality, flexibility, and resilience (Danese et al., 2020; Tiwari, 2020).

Supply Chain Integration (SCI) refers to the extent to which a company and its supply chain collaborators work together to more effectively manage intra- and interorganizational processes and improve the flow of products, material, information, and funds (Zhang et al., 2015). In this definition, the activities, and processes from the acquisition of raw material and sourcing to those involved in producing and distributing the final goods interact. Some of the interactions are more notable than others, making it necessary to simultaneously plan/optimize their associated operations.

As a major process in the manufacturing supply chain, production interacts with many logistical functions – inventory, facility, transportation, and sourcing. Questions regarding what to deliver, who, when, where, and how to complete the operations should be answered considering both supply chain and production management aspects of operations.

Whether an organization strategically targets cost-effectiveness, responsiveness, or it wants to differentiate its products and services from those of rivals, production management decisions can help adjust the operational capabilities to establish strategic fit. Production management decisions should also be aligned with the supply chain strategy. For example, a cost-effective supply chain is mostly concerned with enabling low-cost operations. In this situation, the way the production operations are handled influences the performance of downstream players and the supply chain cost as a whole. Production operations can also be significantly impacted by the performance of the upstream collaborators, which may have a different competitive strategy. This interaction manifests itself in both macro- and micro-managerial decisions. Therefore, it is important to understand the underpinnings of production management decisions and the way they influence supply chain operations.

This chapter elaborates on the interactions between the major production management topics and supply chain structure, strategy, and performance. For this purpose, the managerial decisions pertinent to the development and design issues, as well as planning and control issues are considered to investigate the possible interdependencies. Section 2 discusses the design decisions and the way they interact with supply chain strategy, structure, and decisions – that is, when and why to enter a market and what to produce (product development), how to produce the products (process design), which technologies and materials to use (technology selection), and where to do the operations (facility layout and location planning). Section 3 elaborates on the question of when to conduct the operations (production scheduling), quality control issues, who should conduct the operations (resource management and supervision), as well as planning for possible disruptions. The chapter is concluded in Sect. 4 with major remarks and managerial insights.

2 Development and Design Issues Relationships to Supply Chain Management

2.1 Product Development

Supply chain performance depends on many factors – one of the factor relates to product characteristics (what). A continuous evaluation of the efficiency and effectiveness of the products portfolio and adjusting them is necessary to maintain the supply chain’s competitiveness. Well-informed product design or redesign decisions are a managerial tool to help improve supply chain performance by reducing inventory times (e.g., the use of standard modules), transportation volume (e.g., the postponement of assembly operations), and, overall, the operational cost (Handfield et al., 2020).

Product development and supply chain management capabilities counterbalance each other (Morita et al., 2018). In addition to product design elements for mass customization, such as modularity and multi-skill employees, high supplier involvement and the supply chain design are required to ensure the best outcomes (Ye et al., 2018). For example, companies can reduce the time-to-market if they include supply chain partners in the product development process. On the other hand, the bargaining power of suppliers and customers (Porter, 2008), as well as the strategic partnerships, should be considered in the product development process.

It is also suggested that new product development (Reitsma et al., 2021), and other product management decisions, such as product revitalization and discontinuation, should be made considering supply chain-related factors (Pourhejazy, Thamchutha et al., 2021). These interdependencies emphasize the impact of product management decisions on supply chain strategy and performance.

Supply chain operations are influenced by product design decisions both at the network and node (entity) levels (Reitsma et al., 2021). From a network-level perspective, the product design decisions interact with the supply strategy: whether to outsource certain parts or services to a third party or keep them in-house. They also define the extent supply chain partners, particularly suppliers, should be involved in the design process. If the parts or products are planned to be outsourced, certification or training of third-party actors will be necessary. Otherwise, if the operations are decided to be completed internally, which manufacturing facilities should produce the product and what operational capabilities are needed for these facilities should be determined. Additionally, companies should select the most appropriate transportation mode considering the product features, compartments, and needs.

Overall, these aspects both directly and indirectly influence supply chain fixed and variable costs. Supply chain reconfiguration for managing risks in time of new product development is another strategic topic at the intersection of product development and SCI (Sabzevari et al., 2019).

From the node-level perspective, packaging operations are impacted the most by the product design. For example, a fragile product requires more rigorous packaging; in this situation, alternative material and design approaches that increase the robustness of the product reduce packaging costs.

Taking IKEA’s approach to product design as an example, postponing the final assembly until the point of consumption is a paradigm shift in the supply chain of furniture and home appliances. In addition to reducing the transportation cost and inventory times enabled by increased modularity, the downstream supply chain nodes do not require assembly-related capabilities, such as space, machine, or workforce further reducing fixed costs.

In addition to the node- and network-level activities, product design decisions have an impact on supply chain planning, in particular demand forecasting, capacity planning, and inventory management (Reitsma et al., 2021). For example, the use of common parts in the design of products reduces the variety of required inventory, which alleviates the warehousing complexities and reduces sensitivity to the forecasting outcomes (Jha et al., 2015).

Internal material handling – movements and storage – have increasingly used automatic guided vehicles, optical guidance systems, and robots to load-unload incoming and outgoing trucks. In this situation, the major facility elements, like the unloading gates, recharging stations, sorting, and buffer areas, as well as waypoints and optical paths for the navigation of automatic guided vehicles, should be optimized. This optimization should consider inbound and outbound flow variables to reduce avoidable delays (Ribino et al., 2018).

More advanced supply chain practices, such as cross-docking have been used in certain industries to streamline the response time to customer orders. Given the dynamics in cross-docking facilities, real-time data collection, synchronization, and analysis should be used for dynamic reconfiguration of the storage area to better integrate the inbound and outbound flows (Vis & Roodbergen, 2011).

From a supply chain information flow perspective, considering historic data on supplier performance and customer demand patterns facilitate well-informed layout optimization in the upstream and downstream supply chain facilities, respectively. For example, the layout design of retail stores, as the interface component between customers and goods in a supply chain, can be dynamically adjusted by investigating less-tangible operational needs extracted from historic demand data (Ozgormus & Smith, 2020). This situation may call for strategic positioning of products in the store.

Another example relates to the seasonal supply chains or those expected to experience occasional but dramatic changes. Massive demand variations for a company with many products necessitate operational strategic adjustment of the production processes for which updating the facility layout may be necessary. Layout redesign considering supply chain parameters and product demand variations for disaster relief operations is a good example of this type (Tayal & Singh, 2019).

Finally, decisions for resizing, repurposing, or moving strategic functioning blocks across the existing supply chain facilities are another relevant production manager responsibility. The location-allocation and network optimization problems are well addressed in the supply chain context (see (Eskandarpour et al., 2015)) but assigning departments to different supply chain facilities and moving them has received limited attention.

The Research & Development and Engineering Design departments are prime examples of more intangible units of a corporation; they are often located in closer proximity to the production sites and the focal company. Intellectual property, access to state-of-the-art technologies, knowledge, and resources, as well as geopolitical considerations may be in favor of relocating or decentralizing sensitive departments. Such decisions result in long-term and sustainable outcomes if made considering wider optimization goals. Overall, layout design and determining the location of the departments within and across facilities require both financial and nonfinancial supply chain considerations in addition to optimizing cost and productivity.

This section discussed the major design and development topics and their interactions with supply chain management. Planning and control issues and their supply chain implications are presented in Sect. 3.

3 Planning and Control Issues Relationship to Supply Chain Management

Long-term supply chain strategy integrates aggregate planning, which is required to direct the business activities over an intermediate time horizon. Aggregate planning sets a tactical framework for demand fulfillment decisions; it uses forecasting to determine the production, inventory, outsourcing, and backlog quantities to manage costs and profitability (Chopra & Meindl, 2015). Production managers use aggregate plans to determine production schedules, organize available resources, and address quality control issues at the factory level. This section elaborates on these production management topics and the way they interact with supply chain structure, strategy, and decisions.

3.1 Production Scheduling

Once an aggregate plan is developed by supply chain managers, production managers are responsible for scheduling the operation at the level of individual production units. Production scheduling consists of determining the order of jobs to be dispatched to production such that the available time and resources are used efficiently. Production scheduling can be categorized into single-machine, parallel-machine, flow shop, job shop, and open shop settings; several other variants combine two or more of these production settings, which would be considered hybrid settings. There are many extensions to each of the main production configurations, which are proposed to address case-specific industry situations, and practical needs, and facilitate the real-world applications of scheduling theory.

Given a set of jobs to be completed on a set of different machines (production stages), all jobs in a flow shop require an identical sequence of operations. In a job-shop environment, jobs go through a prespecified but different sequence of operations with precedence constraints. In an open shop, the sequence of operations for every job is different but arbitrary with no precedence constraints. In the job- and flow-shop constructs, each operation should be completed on a specific machine while in the flexible variant of these production settings, the operations can be assigned to any machine from a given set. Finally, in the parallel machine setting, machines are either identical or uniform and jobs should go through one of the available parallel machines for a certain number of stages, which can be different from one job to another.

In addition to the operational characteristics that determine the type of production setting, supply chain considerations may have an impact. For example, the supply chain may require assembly operations to occur within the same facility where the parts are manufactured, which can be modeled as distributed two-stage assembly scheduling (Pourhejazy, Cheng, et al., 2021). Otherwise, assembling at a separate facility should be modeled using the distributed assembly permutation flow shop scheduling (Ying et al., 2020). As another example, a responsive supply chain may require a set of machines in each production stage – redundant capacity instead of a single machine – to better respond to demand surges. This situation may make flexible flow shop and job-shop more viable settings. The desired flexibility in the production process – single versus multi-purpose machines – is another relevant supply chain factor with implications for scheduling.

Recent studies recognized the need for a supply chain-oriented view toward production scheduling. These models can be categorized into distributed scheduling problems and production routing problems. In the distributed scheduling situation, production operations across distributed manufacturing facilities are scheduled simultaneously. Extending production scheduling from an isolated optimization approach to an integrated one, this category emphasizes coordination between different production units for fulfilling global demands while optimizing system performance. Distributed blocking flow-shop, distributed no-wait flow shop, distributed no-idle flow shop, distributed parallel-machine, distributed job shop, and distributed flexible job shop scheduling problem are some of the recent variants of supply chain-oriented scheduling problems. Such models can incorporate order assignment variables for better integration of customer order and manufacturing cycles. In so doing, the possibility of rejecting an order and backlogging them may be of interest to certain use cases. Alternatively, procurement-related decision variables can be incorporated into distributed production scheduling with release dates to take into consideration the possibility of delays in receiving raw materials and parts.

Production routing problems focus on the concurrent planning of sequential – and heterogeneous – operations along the value chain, that is, across production and distribution activities. In the traditional approach, production scheduling solutions are used as inputs for optimizing distribution operations, which may result in a number of planning issues. First, distribution operations may be planned based on infeasible input data, for instance, the delivery may be scheduled for an order that is experiencing a production delay. Second, a lack of coordination between the two processes may result in suboptimal solutions. For example, a customer order with less urgency may be prioritized in the production stage earlier than more urgent ones, which results in poor responsiveness, operational burden, and unnecessary cost. Third, integrated planning of the production and distribution processes is important for maintaining product quality in the supply chain of time-sensitive and perishable products (Ullrich, 2013), while a stand-alone approach may not effectively account for this requirement.

Scheduling variants are predominantly developed in response to case-specific and technical production requirements. For example, the no-wait setting indicates that a work-in-progress job should proceed to the next operation immediately after finishing the current one. In the no-idle setting, the focus is on the idle time of resources, where machines must start processing new jobs immediately after completing a current task without delays. In addition to these technical features, operational requirements, such as setup time and due dates, are considered in form of mathematical constraints to better reflect the real situation.

The optimization criterion for distributed scheduling models is directly influenced by supply chain objectives. The maximum completion time of all jobs – also known as the makespan – determines the response time for new demand. The number of tardy jobs and maximum lateness are service-oriented measures, and total weighted tardiness prioritizes more urgent demands. These measures support the strategic management of responsive supply chains. Alternatively, total completion time performance metrics emphasize better resource utilization and total flow time concerns minimizing the work-in-process inventory. These various objectives and related functions are suitable for supply chains with a cost-efficiency goal.

There are other opportunities for extending production scheduling to improve SCI. From a market perspective, the product mix and the demand size in various regions are dynamic. An optimal location-allocation solution for a certain period may not remain optimum in a dynamic multi-period environment. In this situation, facility transfer is a possible option for adjusting the supply chain.

Facility transfer adjusts the factory cell formation and production capacity, which have an impact on production schedules in different planning periods. Given the mutual relationship between facility location – using a supply chain network optimization decision – and the production planning considerations, that should be optimized simultaneously (Liu et al., 2018).

From an operational viewpoint, make-to-stock supply chains require real-time coordination between production and inventory management (Dong & Maravelias, 2021). That is, producing additional units of products should be subject to inventory variables and limitations. On the other hand, rescheduling might be necessary to boost production and reload the product inventory. Finally, production scheduling can be extended to consider product defects and account for possible reworks, in particular, considering its interactions with the transportation variables (Gheisariha et al., 2021).

Tactical plans, such as production scheduling, provide production managers a boundary of control for managing their operations and determining whether operations are being performed as tactically planned. Another control topic that interacts with supply chain decisions, quality management issues, is discussed next.

3.2 Quality Management

Quality is the main determinant of supply chain strategy. A cost-effective supply chain may not emphasize high-quality materials, parts, and services. A cost-effective strategy favors minimizing investment in resources and selecting less costly logistics operations – such as slower modes of transportation and less frequent replenishments – which may have negative consequences for quality. Product quality and safety may be compromised if the supply chain overemphasizes responsiveness, for example by relaxing the quality control measures. Downstream and upstream supply chain partners should adopt a coordinated quality control system that serves as a supply chain competitive strategy (Jraisat & Sawalha, 2013).

From a supply chain structure perspective, more distributed facilities may be better for the quality of perishable goods, where a shorter distance to the supply and demand nodes reduces the odds of spoilage and degradation. In other cases, centralized facilities may benefit from economy of scale and more delicate quality control tools and approaches. The absence of integrated quality control/visibility over the supply chain partners may put supply continuity at risk. Quality assurance may favor supply chain vertical integration and in-house production of parts and components – such as using additive manufacturing – where the manufacturer has better control over the quality of raw material and parts.

The quality of products a supply chain offers depends on various aspects including input materials, workforce skills, the state of machinery, tools, and production processes. Continuous evaluation and improvement of these elements facilitate better design, optimization, and management of supply chains (Grenzfurtner & Gronalt, 2021). The interactions between quality management and supply chain should consider the roles of material, man, machine, and methods in production management.

Material. When it comes to the procurement of raw materials and parts, the main interaction happens between the quality control aspect of production management and the pricing element – both of which are regulated by the supply chain strategy. As an intersection between production and supply chain management, material quality has been well investigated in the academic literature (Chen et al., 2014). Integrating quality control variables into inventory optimization models allows for addressing uncertainties from an operational perspective. New inventory management strategies (e.g., consignment stock and vendor-managed) have been introduced as a result of this integration; such strategies extend the supplier’s responsibility for the quality of the product until the consumption point (Alfares & Attia, 2017). The cost (time) of quality control operations is another production management aspect that is investigated from a supply chain optimization perspective (Cogollo-Flórez & Correa-Espinal, 2019).

Expectedly, less attention has been directed toward the intersection of quality control with the transportation and facility elements of the supply chain. This situation is particularly relevant for the logistics of consumer goods and perishables, where time and ambient conditions impact the product quality. Supply chain information technology and quality relationships require access to real-time data on the status of materials, parts, and products to help improve quality control. The role of blockchain in confirming the source of the material (i.e., suppliers of suppliers) is a prime example of disruptive technologies with implications for quality control and counterfeit issues.

Machine. In addition to the quality of incoming materials and parts, using calibrated and well-maintained equipment for processing these inputs has a positive impact on the quality of the final product. There is a bidirectional interaction between the reliability of different supply chain stages and product quality, which should be considered in the maintenance of machinery in multi-stage systems (Zhou & Lu, 2018). Channel coordination in machine maintenance practices by individuals in a supply chain enhances the machine capacity, and product quality, and reduces production costs (Chong et al., 2012). These, together, increase supply chain profitability under certain coordination strategies (Jiang et al., 2020). More rigorous preventive maintenance operations may be required in supply chains with a responsiveness strategy. Alternatively, emphasizing reactive maintenance may decrease the individual short-term costs – when compared to preventive maintenance – but can hurt supply chain performance even if the supply chain pursues a cost-effective strategy. From a supply chain structure perspective, centralized production reduces the cost of maintenance services; this may improve the effectiveness of quality control activities and enhance product quality.

Integrating maintenance decisions in supply chain tactical planning improves optimization outcomes (Fatehi-Kivi et al., 2019). Additionally, supply chain optimization can benefit from integrated quality control and maintenance (Jiang et al., 2020). From an information viewpoint, the recent advances in big data analysis and machine learning help predict possible failures by early detection of anomalies in the real-time data collected using sensors (Cheng et al., 2021); this situation facilitates quality control along the value chain. Maintenance information and quality history of material or parts from across the supply chain can also be used for optimizing product quality and lifecycles (Madenas et al., 2015).

Methods. Production processes along the value chain are another determinant of the quality of materials, parts, and products. Process control and improvement are necessary for maintaining quality to the desired expectation. The production manager in each manufacturing unit is responsible for reacting to anomalies detected through process information analysis (Schiefer, 2002).

The complexities of the process control system depend on the supply chain structure. In a highly distributed manufacturing setting, coordinated process control is necessary to enable the supply chain managers to trace anomalies to prevent the propagation of quality loss and delays in a timely fashion. As a means of improving process and product quality, lean and six-sigma concepts have been widely adopted for process improvement in supply chains (Chugani et al., 2017).

Classical supply chain optimization models and methods have been extended to account for process control-related variables. For example, inventory models are improved by including the variables pertinent to production process adjustments (e.g., stop** production and performing setups) in case of quality issues (El Saadany & Jaber, 2008). From an information flow perspective, radio frequency identification (RFID) technologies can assist lean production to further improve the transportation, storage, and retrieval processes in a supply chain (Chen et al., 2013). In addition to improving product and service quality, process improvements reduce scrap, and reworks, among other types of waste, which help the company to stay competitive by lowering the final prices they offer in the market.

Manpower. There is consensus on the positive impact of supplier development programs on the quality of materials and parts (Karaer et al., 2020). As production management practices, employee training, development, and performance management within company-owned facilities have seen little progress in the supply chain context despite its significant impact on supply chain quality and innovativeness (Haq et al., 2021). With a strategic view towards quality, the integrative, exportive, or adaptive human resource practices help create synergy and improve individual performance should it follow a supply chain-oriented approach (Lengnick-Hall et al., 2013).

Regardless of supply chain strategy, successful SCI requires training, development, and performance management of the employees (Menon, 2012). The objective of these human resource practices may vary depending on the supply chain strategy. Overall, integrating human resource and supply chain management helps boost the competitive strategy and organizational performance (Jena & Ghadge, 2021). The next subsection elaborates on the organization and management of its resources.

3.3 Resource Management

Rapid changes in consumer preferences and shortened product lifecycles have added to demand volatilities and supply process complexities. Within this context, resources should be managed effectively to meet demand at the lowest operational cost. While material resource management is closely related to handling the physical flow in the supply chain, other resources, including the capital, manpower, machines, land, energy, and water, are managed at the factory level. How decisions on managing these resources interact with supply chain strategy, structure, and performance is now discussed.

Supply chain management is mostly concerned with managing supplier and customer relationships while manpower capital plays a pivotal role in integrating operational elements. At the factory level, production managers are responsible for the supervision and organization of workforces in close collaboration with the human resource department. Labor supply practices – such as recruitment, planning, and training – are essential for maintaining desired and necessary operations. Shortage of skilled workers in individual production sites propagates along the supply chain and results in an array of operational issues, from quality degradation to delays. An integrated view toward manpower resources and supply chain management, therefore, improves corporate performance while being largely overlooked (Jena & Ghadge, 2021).

Supply chain strategy directs the recruitment, planning, and training programs. A cost-effective supply chain emphasizes highly repetitive routines while a responsive supply chain strategy requires multiskilling and workforce empowerment. Supply chains that compete on differentiating their products and services often tend to spend more on training programs. Supply chain structure is impacted by manpower resource considerations, like access to skilled workers, cheap labor, work culture, and social sustainability issues.

From an operational perspective, the organizational interdependencies between the workforce across supply chain entities and departments make it necessary to consider manpower resource shortage along with the physical resources to mitigate the disruption effects (Aviso et al., 2018). Given the less tangible nature of decisions on the organization and supervision of workforces, limited integrations of such variables can be found in the supply chain optimization context. Integrating decision variables related to worker types for executing certain production tasks – by considering their competencies – into supply chain network planning and optimization models (Paquet et al., 2008) and reverse logistics operations (e.g., electronics waste collection (Pourhejazy, Zhang, et al., 2021)) are examples.

In managing machinery and equipment, resource redundancy – excess capacity – is a safe way of co** with demand fluctuations. Supply chains with a responsiveness agenda typically use excess resource management strategies while cost-effective supply chains are mostly cognizant of maximizing the utilization of the available resources. Line balancing can be used to balance machine time and adjust the production rate for demand fulfillment – especially when the production system has tight utilization rates and constraints on machinery resources. For line balancing, the number of machines (and operators) assigned to each task is rebalanced to adjust the production rate. This kind of optimization approach provides decision support to production managers, but it can result in better outcomes when coordinated with supply chain variables.

Integrating assembly and disassembly line balancing variables into the supply chain network optimization and closed-loop models is a good example of a broader supply chain integration (Yolmeh & Saif, 2021). New technologies, like additive manufacturing, make it easier to manage resources and adjust to demand changes. Besides, the use of big data analysis and machine learning approaches helps improve resource management by reducing non-value-adding activities and addressing less-tangible aspects of operations.

Land, energy, and water are basic resources required for any supply chain activity. The required amount of these resources is a matter of technical requirements – for example, semiconductor production sites require huge water reservoirs. Access to such resources is considered one of the essential criteria for selecting the location of production facilities as strategic supply chain decisions.

Production managers are responsible for managing the available land, energy, and water resources at the operational level. Managing such resources at the factory level has indirect interaction with supply chain strategy but it directly impacts the supply chain structure. For example, limited land, energy, and water may encourage extending the supply tiers to find alternative sources for the products that cannot be produced in-house due to land, energy, and water limitations. Additionally, distributed and geographically dispersed facilities or relocation decisions may be triggered by resource limitations.

A holistic view of the long-term requirements, as well as the profile of available resources across the supply chain, are prerequisites for informed resource management decisions (Taherzadeh, 2021). At the factory level, the availability of land resources may influence inventory management decisions as well as the production level. Adaptive resource planning may be required to adjust to the changing operational conditions. In particular, dynamic adjustment of the available spaces is a relevant decision that can be considered to best manage such resources in volatile times.

The planning and control issues discussed up to this point of Sect. 3 are meant for routine operations – that is, when everything goes as planned. Disruptions, especially those impacting the factory level operations, change the situation, hence, require decisive actions and alternative solutions to mitigate the adverse supply chain effects as much as possible. Planning for disruption is discussed in the next subsection.

3.4 Planning for Disruption

Unexpected events that impact resources availability – availability of power, machines, material, and manpower – can interrupt the production processes at the factory level and can result in major supply chain disruptions. Planning for disruptions consists of preparing for unexpected situations, finding alternative solutions for maintaining the production facilities’ operations, and having strategies for a quick recovery after a major disruptive event. Digitalization and SCI improve connectivity, transparency, and effective information flow between different departments within and outside of the factory, which enables a timely and well-informed course of managerial actions in times of disruption (Treber & Lanza, 2018). Training programs, which are discussed earlier in the chapter, and drills are some of the initiatives for labor-based disruption preparedness at the factory level.

Operationally, production processes can be impacted by disruptions in: (1) supply – material, parts, and components; (2) manpower, machines, energy, and water; and (3) demand. Supply and demand disruptions relate to supply chain management level activities. Supply chain decisions, such as location and volume of redundant inventory in the network can help alleviate the negative impact of material disruptions. For example, purchasing’s time for finding alternative resources and addressing the shortage problem are additional activities.

At the factory level, a production manager could consider the severity of disruption and its root cause(s), which requires current knowledge on the state of the system at the time of disruption. The manager would need to decide to delay the operations, outsource them, or renegotiate the accepted orders for possible backlog or cancellation. Production rescheduling is a possible production management solution in response to production disruptions (Katrag**i et al., 2013). Production management decisions, like rescheduling, have implications for supply chain performance (Rao & Ranga Janardhana, 2014). In addition, rescheduling the production operations is the most common way of minimizing the losses after disruptions (Paul et al., 2015). In either case, the company may have to employ additional machines and manpower or adjust the working hours of the existing ones – such as using additional shifts or overtime – to fulfill the backlogged demands. Such decisions should also be made in coordination with the supply chain partners to ensure a smooth flow of raw material and finished products.

Production managers may take advantage of alternative solutions – such as the application of new technologies – for producing the delayed parts or components in-house. For example, additive manufacturing can be used as an alternative production method in times of supply disruptions.

Possible changes in the production methods on the shop floor impact the supply chain structure. Taking the alternative manufacturing methods for producing parts or components as an example, the company may have to seek material suppliers or third-party 3D printing service providers, which can shorten or extend the partnerships, supply tiers, and alter the network configuration. This decision also has implications for supply chain performance in terms of cost, quality, speed, and flexibility. Dual-channel supply chain optimization models should be developed to account for the possible shift between the regular and alternate production approaches considering various disruption scenarios.

During market disruptions, the shop floor may not be required to operate at normal capacity due to the physical limitations in the factory and supply chain. Slowing down operations and reallocating resources in response to addressing demand disruptions are some of the possible production management solutions. These factory-level decisions should be made in coordination with downstream entities, in particular warehouses, distribution centers, and retail stores, to pursue optimum outcomes.

As another example, a disruption in outbound distribution operation due to accidents, vehicle breakdowns, and weather conditions may impact production operations. In this situation, integrated planning of production and distribution activities provides better outcomes than an isolated optimization approach (Li & Li, 2020). Production management decisions, such as production scheduling and line balancing on the shop floor, will interact with supply chain activities such as transportation modes, inventory, sourcing, and pricing policy decisions. Integrated optimization models may be helpful for well-informed decisions. Integrated optimization of the production and inventory variables considering the possible disruption risks in the production process (Malik & Sarkar, 2020) is a recent example.

Using redundant – excess – resources is a common strategy for dealing with disruptions caused by labor limitations and machine breakdown. From a planning perspective, employing additional manpower and machines reduces dependency on individual resources. The main tradeoff is between the costs of redundant resources and the ability of the system to remain operational in times of disruption.

Crucial resources and bottleneck operations should receive a higher priority for building redundancy. Identifying the cost break-even point of using redundant resources varies depending on the supply chain strategy. That is, a cost-effective supply chain may not use many redundant resources when compared to a responsive one. Cost-benefit analysis for deciding the use of redundant resources should consider a systemwide perspective. Decisions should not be only focused on the optimality of an individual production facility, or, more generally, a supply chain entity.

From a technical perspective, informed maintenance of machinery and equipment is necessary to prevent unplanned breakdowns. In the case of reactive maintenance, 3D printers can be used to facilitate repair and maintenance activities by producing machinery components and tooling equipment that may require weeks or months to be supplied in a regular situation. Energy source disruptions can have alternative solutions like the use of solar panels, which are becoming more viable and resilient options. The geographical characteristic of the production facility is an enabling factor for the selection of alternative energy sources.

Overall, different combinations of production management and logistics measures result in different outcomes when reacting to disruptions (Peukert et al., 2020). In this situation, a standalone planning approach may result in either infeasible or suboptimal solutions. Alternatively, possible disruptions in the system are usually reflected through parameter changes in the optimization models. In addition to stochastic optimization approaches and dynamic programming that can account for such features, applications of simulation-based optimization models help address the underlining uncertainties effectively, especially when planning for possible disruptions.

4 Concluding Remarks

SCI requires that possible interactions between individual units and their operations are taken into consideration for design and planning purposes; this integration will help achieve a global optimum when improving the operations. The integration between supply chain elements has seen developments in both academic literature and practice.

Production management topics including shop floor decisions and their interactions with the supply chain have received relatively less attention. To investigate the major links between the topics, design and development issues, including product design (what), material and technology selection (which), process design (how), and facility layout (where) are first studied. Planning and control issues, including production scheduling (when), quality management, resource management and supervision (who), and planning for possible disruptions are then considered.

The chapter discussed the interdependencies between the subject matter and the supply chain strategy, structure, and elaborated on the impact of the respective decisions on the supply chain performance. Each subsection concluded by providing insights into some of the latest technological and/or academic developments and suggestions for future works on the subject.

Overall, there are many opportunities for improving the supply chain performance beyond current norms by considering production management-related decisions. This includes joint optimization of production and supply chain variables as well as decision-making considering the factors integrating shop floor considerations. Given the tradeoff between the complexity of an optimization or management decision model and its practicability, the sort of integration should be determined considering the targeted competency and the core interactions. For example, integrating production scheduling and inventory management variables should be targeted when achieving a short product shelf-life is an optimization priority.

Broader supply chain perspectives such as end-of-life disassembly and reverse logistics variables should be optimized simultaneously when the supply chain emphasizes the use of recycled material and closed-loop operations. Operational mandates may impact the sort of integration, for instance, just-in-time production requires an advanced level of connectivity between the production and distribution operations. From a methodological perspective, simulation-based optimization frameworks should receive more attention in SCI. This is particularly relevant for integrating the micro and macro processes at the intersection of production management and SCI. Reducing modeling assumptions, generating more accurate model parameters, more realistic performance evaluation, and the effective inclusion of various uncertainty sources are some of the major advantages of simulation-based optimization methods.

Supply chain integration with production management will always be a prerequisite for broader strategic competitiveness.