Organizations have always had the development of efficient supply chain systems as their focus. According to Attaran and Attaran [14], collaboration in the supply chain practice is becoming the crux of successful and long-lasting management of business operations. They posited that the inclination to produce quality goods and services is driving up the supply chain cost, affecting the supplier’s financial performance. Therefore, the significance of the supply chain to the production and eventual financial strength of the business makes it a top issue for the organization’s management [15]. For an organization to run a sustainable and successful supply chain, though, management has to ensure collaborative planning due to its effect on the movement of goods and services. As indicated by Cassivi [16], collaborative planning, forecasting, and replenishment (CPFR) is the bone of the business process that strengthens the management of the supply chain in an organization.
A successful supply chain management goes a long way in benefiting a company in a competition. Some of these benefits are evident in improving the cost of production, distribution, inventory, and the flexibility associated with the ‘production of goods and services, and improvement in market share [17] and customer relations [18]. Simatupang and Sridharan [19] posited that flexibility is vital in measuring the success of the supply chain in the organization, and it’s one of the benefits of having effective supply chain management. According to Hsu [20], the benefits of supply chain management can be either tangible or intangible. Tangible benefits are exemplified in the cost and reduction of inventory and its effective management. The time saved when the inventory is delivered quickly, while the intangible information accuracy, consistency, flow, service quality, and response time [21]. These, however, can only be realized when there is an integration of various functions and stakeholders in the organization.
According to Stevenson and Spring [22], correct and instantaneous information flow in the supply chain is equally as vital to the business as material flow. “An information-enriched supply chain would have a single customer entity connected to every scheduling process, showing order information flowing to all links, while for a non-enriched supply chain, the customer entity might connect only to the final scheduling link, leaving the remainder of the supply chain hidden from the customer” [23].
Sharing of information is essential due to its reflection of teamwork within the supply chain of an organization [24]. Simatupang and Sridharan [25] refer to information sharing as “the ability to see private data in a partner’s systems and monitor the progress of products as they pass through each process in the supply chain; the activity includes monitoring (data capturing), processing, and dissemination of customer data, end-to-end inventory status and locations, order status, costs-related data, and performance status”. Simatupang and Sridharan [19] believe that sharing information among supply chain partners allows short time order fulfillment within the order cycle times due to the shared information. Supply chain partners sharing of information generates supply chain information flow management that aids effective decision making among partners. Li et al. [24] stated that information flow is categorized based on the area of operation it is generated and needed. Therefore, the categories of information flow include production plan, inventory, order state, demand forecasting, and sales [26]. In their paper, Koh, Saad, and Arunachalam [27] stated that information flow is needed to support the management of activities like procurement of raw materials, schedule for production, and physical distribution system [28].
For information flow to be complete, two-way communication needs to be conducted, which involves contents, medium/channel, and systems [29]. The content is the actual information to be passed; the medium/channel is the pathway for the information, while the system allows the management of both information and the channel. According to Kembro and Selviaridis [30], information in supply chain information is sharable into three levels within the organization: strategic, tactical, and operational. At each of these levels, different types of information are communicated while various associated advantages and hindrances are encountered in sharing the information in the supply chain. Hsu et al. [31] also separated information shared in the organization into diverse levels, which are: strategic information (e.g., long-term objective, marketing, and customer information) and tactical (e.g., purchasing, operations scheduling, and logistics).
Information exchanged can also be classified into managerial and transactional information. Transactional refers to information required for an organization to conduct procurement or supplies. This class of information is related to payment order, receipt, inventory, transportation, and delivery. This information class relates to the technology needed for operations, quality, costs, and profitability.
From the perspective of supply chain management, information flow and its management are critical activities of the leaders in an organization. The flow of information in the supply chain is bi-directional. This is because other forms of activities, including materials and money flows, are activated by the movement of information to achieve the set objective. This means that material and money flows effective management is positively related to effective management of information flow. Therefore, the huge interest in these flows in literature and supply chain practice is understandable. Several supply chain practitioners have identified the significance of materials flow management as a critical strategic achievement issue [32].
Kuck et al. [33] suggested a data-driven simulation-based optimization approach for dynamic manufacturing system control. The study devised a method for rescheduling production in response to changing circumstances, taking into account aspects that may confuse, such as the simultaneous delivery of many orders. Ersoz et al. [34] attempted to bridge the scheduling theory and practice gap. They tailored their planning operations to the real-time information supplied by the process control and control systems. The dynamic structure of the production environment is quickly sensed in the offered procedure, and the schedule is modified in response to the changing conditions. In manufacturing, the traceability of the parts improved. Furthermore, needless waiting or downtimes were reduced.
Xiong et al. [35] suggested a simulation-based methodology for deciding dispatching rules in a dynamic scheduling issue with task release times and extended technical priority limitations. The proposed approach decreased total delay and the number of late tasks. Zhang et al. [36] conducted a literature study on job-shop scheduling issues and explored fresh viewpoints in the context of Industry 4.0. They added that under Industry 4.0, scheduling issues are addressed using new methodologies and approaches. The findings suggest that scheduling research should focus on smart distributed scheduling modeling and optimization. According to their assessment, this may be accomplished through two methods: combining old techniques and presenting a new way, as well as proposing new algorithms for smart distributed scheduling (Fig. 1).
In their study, Rossit et al. [37] introduced the notion of intelligent manufacturing that arose with Industry 4.0. They have addressed the subject of smart scheduling, which they feel has a significant role in today’s product knowledge. They created the notion of tolerance scheduling in a dynamic environment to avoid production rescheduling. Likewise, Tao et al. [38] investigated contemporary advancements in production systems and smart manufacturing technologies, as well as Industry 4.0 models and stated that dynamic scheduling is one of the significant research undertaken in the literature in the context of Industry 4.0.
Jiang et al. [39] investigated the topic of energy-efficient job-shop scheduling to reduce the total cost of energy use and finishing time. However, the problem at hand was deemed NP-Hard. As a result, they created an enhanced whale optimization technique to address this issue. They improved the whale optimization method by using dispatching rules, nonlinear convergence factors, and mutation operations. They ran simulations to demonstrate the algorithm’s usefulness. According to the findings of simulations, the algorithm delivered benefits in terms of efficiency. Ortiz et al. [40] investigated a flexible job-shop problem and offered a novel methodology for solving it. They created a novel algorithm that reduces average tardiness and discovered better solutions than the existing dispatching rules. They developed a real-world production-scheduling challenge and an effective solution for solving it.
Ding and Jiang [41] examined the impact of IoT technologies in an industrial setting. They claim that while IoT has boosted production data, these data are sometimes discontinuous, uncorrelated, and challenging to use. As a result, they devised a strategy for using priceless data. They developed an RFID-based production data analysis system for production control in IoT-enabled smart businesses. Leusin et al. [42] developed a multi-agent system in a cyber-physical approach to handling the dynamic job-shop scheduling problem. The suggested system included self-configuring characteristics in the manufacturing process. This was accomplished through the usage of agents and IoT. In the shop, real-time data was used to make more informed decisions. A real-world case study was used to test the concept. The benefits of utilizing dynamic data and IoT in industrial applications are explored (Fig. 2).
Following a review of the relevant literature, it was discovered that numerous models were created to establish a decision support system for continuous process improvement based on IoT-enabled data analytics. There is a need to improve the decision support system for dynamic settings that can function with various dispatching criteria. The main goal is to improve the efficiency of production management and the job-shop.
