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This paper sets out to present a reappraisal on advanced planning and scheduling (APS) systems in industrial settings and propose an effective approach for APS implementation.

A case study approach is adopted, and a research framework comprising human‐, technological‐, and organizational‐dimensions is developed to analyze the evidence database which includes business flows, system design documents, archival records, post‐system assessment, participant‐observation and semi‐structured interviews.

The findings indicate that real‐world production planning problems are ill‐defined, complex and dynamic. A post‐implementation evaluation reveals major pitfalls in the technology‐dominant approach, whose negative ramifications are usually overlooked. Besides, these APS implementation pitfalls are found to be attributable to the real‐world context, human factors and organizational aspects.

Despite advances in information technology (IT) and computer modeling techniques, humans still play critical roles in the production‐planning processes – especially in a complex and dynamic manufacturing environment where incomplete, ambiguous, inconsistent and untimely data make automatic planning unrealistic. A rational human‐computer collaboration scheme under an effective organizational structure would be in a better position to take advantage of the IT.

This paper presents a humans‐technology‐organization‐framework of real planning systems, which is employed to analyze a case of APS implementation. Practical insights are extracted as a result of this field research, and a realist approach is proposed to cope with the problems and pitfalls of APS implementation in industrial settings.

The purpose of this research is to improve efficiency of the traditional scheduling methods and explore a more effective approach to solving the scheduling problem in supply networks with genetic algorithms (GAs).

This paper develops two methods with GAs for detailed production scheduling in supply networks. The first method adopts a GA to job shop scheduling in any node of the supply network. The second method is developed for collective scheduling in an industrial cluster using a modified GA (MGA). The objective is to minimize the total makespan. The proposed method was verified on some experiments.

The suggested GAs can improve detailed production scheduling in supply networks. The results of the experiments show that the proposed MGA is a very efficient and effective algorithm. The MGA creates the manufacturing schedule for each factory and transport operation schedule very quickly.

For future research, an expert system will be adopted as an intelligent interface between the MRPII or ERP and the MGA.

From the mathematical point of view, a supply network is a digraph, which has loops and therefore the proposed GAs take into account loops in supply networks. The MGA enables dividing jobs between factories. This algorithm is based on operation codes, where each chromosome is a set of four‐positions genes. This encoding method includes both manufacture operations and long transport operations.

The purpose of this paper is to explore what potential benefits may be achieved by using advanced planning and scheduling (APS) systems in the sales and operations planning (S&OP) process.

The paper investigates benefits at the S&OP process level by interviewing APS experts and APS users. Several methods have been used; literature review, Delphi study, and a case study at a company in the chemical industry which uses APS system support in the S&OP process.

Three types of potential benefits were found to be achieved when using APS systems in the S&OP process; benefits concerning decision support, planning efficiency and learning effects. The most common type was decision support benefits according to APS users and APS experts. The results from the case company showed that the benefits perceived in the different S&OP activities differed. In the activities concerning the preparation and generation of delivery plans, the perceived benefits mainly concerned learning effects. In the activities concerning the generation of a production plan, the benefits were foremost found in planning efficiency. In the S&OP meeting decision support benefits were highest valued. The reason for the different results can be explained by the aim of the activity, how APS was used in the activity, the user characteristics and the design of the model and access and quality of planning data.

The focus of this paper is on potential benefits of APS systems in the S&OP process only, not the costs. It has established a typology of potential benefits. No validation in form of statistical analysis has been done. The empirical analysis is mainly based on findings from a single case study.

The findings about the types of APS potential will assist companies in understanding the benefits they can expect from its use in the S&OP process. The case study analysis gives further insight into how APS can be employed and what benefits different APS user categories can expect when it is used in an appropriate way.

The knowledge about which benefits that can be achieved when using APS in the S&OP process is quite unexplored. This paper fills some of these gaps.

The purpose of this paper is to present a new framework for designing and implementing simple but effective advanced planning and scheduling (APS) systems in glass container industry by means of spreadsheets.

The conceptual framework for APS system design is developed by integrating principles of end-user computing and mixed-integer programming (MIP). This framework comprises the APS implementation and its integration with enterprise resource planning systems. Such a proposal is applied in a real-world glass bottle manufacturer and its performance assessment is carried out by using metrics of the well-known supply chain operations reference (SCOR) model.

The case study shows that the framework application can improve the responsiveness of glass container supply chain (SC) as well as reduce significantly SC planning costs. A further assessment of the end-user computing satisfaction highlights the general acceptance of the framework.

The proposed framework can assist glass industry managers and practitioners to solve their complex production planning problem since custom solutions are not available for this sector. Another important implication is the possible generalization of the framework given that MIP models for production planning are currently available in the literature for different industries.

On the one hand, the new framework successfully integrates concepts of end-user computing and MIP bridging the gap between theory of aforementioned areas and their practical applications. On the other hand, the paper contributes to the scarce literature on performance evaluation of APS systems and their impact on production system performance. The novel APS gain analysis based on SCOR metrics ensures that the system potential benefits are aligned with the modern concepts of supply chain management.

The purpose of this paper is to explore how the context affects successful use of advanced planning and scheduling (APS) systems in sales and operations planning (S&OP) processes, and how individual, technological, and organizational (ITO) dimensions affect this procedure.

This is a qualitative case study of two APS system-supported S&OP processes. The work aims to generate propositions concerning the relationships among the use of APS system, the context, ITO dimensions, and fulfillment of S&OP aims.

Use of APS systems was especially appropriate in support of S&OP processes in complex planning environments and when S&OP aims were ambitious. ITO dimensions were important influences on successful APS system use in most contexts. APS systems were not considered appropriate when having S&OP processes with ambitious aims and low individual and organizational maturities. Use of APS systems was also inappropriate when the extent of technological maturity was minimal. S&OP processes with ambitious aims, operating within a complex planning environment, are difficult if not impossible to implement without the support of APS systems.

The suggestions on when APS systems should be used in different S&OP environments will be useful to companies implementing or about to implement APS systems.

APS systems offer great potential if they are effectively used to support S&OP, still the use of APS system in S&OP is unexplored. The paper shows how the context and the ITO dimensions affect the successful use of APS systems in S&OP processes.

The general purpose of the paper is to improve supply chain (SC) responsiveness and agility by developing advanced planning and scheduling (APS) with collaboration process into agile supply and demand networks (ASDN).

Some industrial examples are presented to extract the APS requirements, then business models that are supported by analytical models are developed into APS modules to respond to the requirements. At the end, the modules are attached into an ASDN simulator to measure the benefit of the APS with collaboration process.

The results show that the APS with collaboration process is superior to existing APS software in terms of promising lead times to customers at minimum inventory level.

Since the APS with collaboration process cannot optimize transportation planning, SCs cannot therefore optimize networks by finding the optimum network configuration. Currently, the simulator needs to be tested in several possible network scenarios to find the optimal network configuration.

The APS with collaboration process makes it possible to give guaranteed lead times at minimum inventory level. Furthermore, it is possible to combine the APS with collaboration process with enterprise resources planning or MRP II by considering the criticality of the planning.

The attachment of APS with collaboration process business into ASDN represents the original aspect of this paper.

The purpose of this paper is to develop a new approach called advanced overlapping production planning (AOPP) model which considers multi‐site process selection, sequential constraints, and capacity constraints in a manufacturing supply chain environment (MSCE). AOPP model may determine the capacity plan and order margin allocation for each site and machines in an MSCE and provide the capacity information for a production planner to effectively adjust the production strategies (e.g. outsourcing, overtime, or adding a work shift) of overloading resources.

First, an AOPP model is presented to model the production scheduling problem in a supply chain with the objective of minimizing the fulfilling cycle time of each order and the overloads of each machine group. Second, a genetic algorithm (GA)‐based approach for solving the AOPP model is developed. Finally, a heuristic adjustment approach is proposed for planners to adjust the production plan whenever there is an exception of production occurring.

The production schedule obtained from the GA‐based AOPP approach retains order margins in each operation against other overlapping operations, and it satisfies the capacity constraints of each machine group in an MSCE and results in a better performance in process planning and production planning with finite capacity. In practice, the overloading problem can be solved by adding a work shift or working overtime. The GA‐based AOPP model provides useful information for production planners to make such decisions.

Production planners need a more flexible production plan with order margins to compensate for the uncertainties which frequently occur in the supply and demand sides. This research develops a model to help planners manage the order margin of production planning in an MSCE and showing that order margins become a crucial factor for achieving effective production objective in terms of short OTD (or order cycle) time.

The overlapping production planning approach is a useful finite capacity planning approach for handling the capacity and order margin management in certain manufacturing environment (e.g. flow shop), but less on overcoming multi‐site process selection, sequential constraints, and capacity constraints in an MSCE. In addition, the capacity plan and order margin allocation information for each site and facilities are very important for a planner to effectively adjust the production strategies (e.g. outsourcing, overtime, or adding a work shift) of overloading resources. This research addresses both issues.

The purpose of this paper is to examine the inventory performance effect of advanced material planning modes and analyse how internal and external contextual difficulties moderate this relationship. This study also identifies avenues for future research.

The empirical analysis uses a survey of material planning for purchased items in 292 Swedish manufacturing and wholesaling companies. Three dimensions of inventory performance are dependent variables: material planning performance, inventory turnover rate, and service level.

Advanced material planning modes are directly associated with material planning performance, but this study could not verify direct associations with inventory turnover rate and service-level performances. External and internal contextual difficulties have direct effects on all inventory performance dimensions and moderate the inventory performance effect of advanced material planning modes. The moderating effect is stronger in non-difficult contexts, for which advanced material planning has significant inventory performance effects. Demand- and human-related contextual dimensions are especially critical.

The study identifies the following guidelines for companies to consider in order to unlock the potential of advanced material planning: consider full implementation of advanced material planning in non-difficult contexts; minimise the plan variability effects of high parameter revision and planning frequencies; minimise the need for, and use of, manual modification of planned orders before release; reduce demand uncertainty and variability; and secure appropriate human skills and working time.

This study somewhat contradicts the literature on material planning by not finding a direct positive effect on any inventory performance dimension from analytical design of order quantities and safety stocks. The research adds to the literature by identifying direct and moderating effects of external and internal contextual difficulties on all three-inventory performance dimensions. The relative importance of managing automatic order release identified in the study motivates future research as the effect has not been previously highlighted in the literature. Accordingly, avenues for future research and an agenda for practice-oriented research are suggested.

The purpose of this paper is to study the information support technologies of integrated production planning control for OEM (original equipment manufacturer) driven networked manufacturing systems, and offer implications to firms for implementing networked manufacturing.

OEM driven networked manufacturing and its operations modes and support technologies are first discussed. Then, integration framework of production planning and control is proposed and relative technologies are discussed. Finally, a case of the application of information support technologies in networked manufacturing is illustrated.

Both theory analysis and case experience show that information integration and sharing are critical for effective operations of OEM driven networked manufacturing and an integrated production planning and control system can benefit firms for successfully operating a networked manufacturing system.

It is valuable to develop and apply integrated production planning and control systems in OEM driven networked manufacturing, Firms should pay more attention to information sharing and communication with partners and utilize advanced information technologies to synchronize the operations of partners.

Integration framework of production planning and control proposed in this paper has originality and the technology strategies are also practical. Managerial ideas, technology framework and application strategies of integrated production planning and control are helpful for firms to implement OEM driven networked manufacturing.

The purpose of this paper is to develop an intelligent manufacturing system for transforming production management and operations to an Industry 4.0 manufacturing paradigm.

A manufacturing mode-Graduation Manufacturing System is designed for organizing and controlling production operations. An Industrial Internet of Things (IIoT) and digital twin-enabled Graduation Intelligent Manufacturing System (GiMS) with real-time task allocation and execution mechanisms is proposed to achieve real-time information sharing and production planning, scheduling, execution and control with reduced complexity and uncertainty.

The implementation of GiMS in an industrial company illustrates the potential advantages for real-time production planning, scheduling, execution and control with reduced complexity and uncertainty. For production managers and onsite operators, effective tools, such as cloud services integrates effective production and operations management strategies are needed to facilitate their decision-making and daily operations at the operational level.

This paper presents an Industry 4.0 paradigm-GiMS, which aims to explore Industry 4.0 technologies opportunities on operations and production management, especially on production planning, scheduling, execution and control.