BUS606: Operations and Supply Chain Management

MC provides a set of enabling concepts and methods for providing the customer with products they desire and for organising production resources and networks to realise these products. However, on a practical strategic, tactical, and operational level, the tools for the realisation of MC are under development and refinement and a number of issues related to the design of manufacturing networks and their management are still not tackled in a holistic integrated manner. Several particular challenges need to be addressed as described below. Possible solutions are also proposed in the context of supporting a more efficient implementation of MC and personalisation.

Challenges for the manufacturing network life cycle

Regarding supplier selection, existing frameworks that handle both selection of suppliers, order allocation, and capacity planning are rare in the literature. Therefore, inconsistencies between the design phase and the actual implementation of the supply chain are a common issue. The problem most commonly treated jointly with supplier selection is the order allocation problem, among other. Moreover, several studies point out the difficulties of coordination between large networks of stakeholders. Potential solutions in novel approaches to tackling the issues generated in supply chain coordination for the procurement of customised products are proposed, where organisation flatness is proposed as a mediator for enhancing MC capability. Flatness in cross-plant and cross-functional organisation alleviate the need to decisions to pass through multiple layers of executives, simplifying coordination and information sharing. Among the several challenges for configuring robust manufacturing networks to satisfy MC are the need for frameworks that handle the entire order fulfilment life cycle (from product design to delivery), methods to allow easy modelling and experimentation of what-if scenarios and deeper examination of the impact of product variety on the performance of manufacturing networks. On the field of SCM, identifying the benefits of collaboration is still a big challenge for many. The definition of variables, such as the optimum number of partners, investment in collaboration, and duration of partnership, are some of the barriers of healthy collaborative arrangements that should be surpassed. Available solutions for lot sizing are following traditional approaches and are not able to address the increasing complexity of problems arising in the modern manufacturing network landscape. The economic order quantity (EOQ), established for more than 100 years, still forms the basis of recent lot sizing practices. In setups of complex and changeable products, the problem of lot sizing becomes extremely complex. Nevertheless, the optimality of inventory and capacity planning is often neglected due to increased complexity of the supply chain problems which comes with higher priority. For instance, in multi-agent manufacturing systems, each agent resolves inventory issues in its domain partition level, without clear global optimisation overview. Furthermore, the broader role of logistics capabilities in achieving supply chain agility has not been addressed from a holistic conceptual perspective. Therefore, an open research question is the relationship between logistics capabilities and supply chain agility. Regarding ERP suites, apart from their apparent benefits, the reported successful implementations of ERP systems are limited when considering implementation costs and disruptions caused in production. One reason for the low success rates in ERP implementations is attributed to the organisation changes needed for the industry that disrupt normal flow of business. Another reason is that production planning, a core function handled by currently deployed closed-loop MRPII (manufacturing resource planning) and ERP suites, is performed through the fundamental MRP (material requirements planning). This leads to the generation of low-detail shop-floor schedules, assuming infinite production capacity and constant time components, thus leading to inflated lead times. Challenges on the technological level of ERP systems include delivery of software as a service, mobile technology, tightly integrated business intelligence, and big data analytics. Challenges in the field of product data management (PDM) are related to the efficiency of these systems with regard to studying factors that affect the accessibility of product data, for instance, the nature of data in different timeframes of a development, the relationship between the maturity of the data, and the probability of them being modified. The deployment and tight integration of product life cycle management (PLM) tools must also be considered since they bring an abundance of benefits against current manufacturing challenges. Yet these benefits are still not appreciated by many industrial sectors, mainly due to the following reasons: (1) they are complex as a concept and understanding their practical application is difficult, (2) they lack a holistic approach regarding the product life cycle and its underlying production life cycle and processes, and (3) the gap between research and industrial implementation is discouraging. Concerning CRM, although data rich markets can exploit the feedback of consumers through social networks to identify user polarity towards a product–service, improve its design, and refine a product service system (PSS) offering, only few initiatives have tapped that potential.

Further challenges that are related indirectly to the previous aspects are discussed hereafter. Concerning individual disparate software modules, it is often observed that they contradict each other because they refer to not directly related manufacturing information and context. The harmonisation, both on an input/output level and to the actual contents of information, is often a mistreated issue that hinders the applicability of tools to real-life manufacturing systems. Limitations of current computer-aided design (CAD) tools include: the complexity of menu items or commands, restricted active and interactive assistance during design, and inadequate human–computer interface design (focused on functionality). To fulfil the needs of modern manufacturing processes, computer-aided process planning should be responsive and adaptive to the alterations in the production capacity and functionality. Nowadays, conventional computer-aided process planning (CAPP) systems are incapable of adjusting to dynamic operations, and a process plan, created in advance, is found improper or unusable to specific resources. Highlighted challenges for life cycle assessment (LCA) are modularisation and standardisation of environmental profiles for machine tools, as well as modelling of "hidden flows" and their incorporation in value stream mapping tools. Regarding knowledge management and modelling, reusable agent-oriented knowledge management frameworks, including the description of agent roles, interaction forms, and knowledge description, are missing. Moreover, ontologies used for knowledge representation have practical limitations. In case an ontology is abstract, its applicability and problem-solving potential may be diminished. On the other hand, in the case of very specific ontologies, reasoning and knowledge inference capacities are constrained. Furthermore, in the turbulent manufacturing environment, a key issue of modern manufacturing execution systems is that they cannot plan ahead of time. This phenomenon is named decision myopia and causes undoubtedly significant malfunctions in manufacturing. In the field of layout design and material simulation, some commercial software can represent decoupling data from 3D model and export them in XML or HTML format. While this is an export of properties, it cannot fully solve the interoperability and extensibility issues since the interoperability depends on how the different software and users define contents of data models. Concerning material flow simulation, it can be very time-consuming to build and verify large models with standard commercial-off-the-shelf software. Efficient simulation model generation will allow the user to simplify and accelerate the process of producing correct and credible simulation models. Finally, while the steady decline in computational cost renders the use of simulation very cost-efficient in terms of hardware requirements, commercial simulation software has not kept up with hardware improvements.
Solutions for addressing the challenges in the future manufacturing landscape

A view of the manufacturing system of the near future that incorporates the latest trends in research and ICT developments and can better support MC is shown in Fig. 12. It is envisioned that, fuelled by disruptive technologies such as the IoT and cloud technology, entities within supply chains will exchange information seamlessly, collaborate more efficiently, and share crucial data in real time. Data acquisition, processing, and interpretation will be supported by wireless sensor networks. The information will be available on demand and on different degrees of granularity empowered by big data analytics. Drilling down to specific machine performance and zooming out to supply chain overview will be practically feasible and meaningful. The distinction between the physical and the digital domains will become less clear. Besides, physical resources are already considered as services under the cloud manufacturing paradigm. A tighter coupling and synchronisation between the life cycles of product, production, resources, and supply chains will be necessary, while the distinction between cyber and physical domains will become hazier. A discussion on potential directions for adhering to this view of manufacturing is provided hereafter.

Fig. 12 View of manufacturing in the near future

New technologies and emerging needs render traditional SCM and manufacturing network design models obsolete. To support manufacturing network design, planning, and control, a framework that integrates, harmonises, processes, and synchronises the different steps and product-related information is needed. The framework will be capable of supporting the decision-making procedure on all organisation levels in an integrated way, ranging from the overall management of the manufacturing network, down to the shop-floor scheduling fuelled by big data analytics, intuitive visualisation means, smart user interfaces, and IoT. An alignment and coordination between supply chain logistics and master production schedules with low-level shop-floor schedules is necessary for short-term horizons. The framework needs not be restricted on a particular manufacturing domain; since it is conceived by addressing universal industrial needs, its applicability to contemporary systems is domain-independent. The constituents of the framework are described hereafter.

The system will be supported by automated model-based decision-making methods that will identify optimum (or near-optimum) solutions to the sub-problems identified above, such as for the problem of the configuration of manufacturing networks capable of serving personalised product–services. The method must consider the capabilities of the manufacturing network elements (suppliers of different tiers, machining plants, assembly plants, etc.) and will indicate solutions to the warehouse sizing problem, to the manufacturing plant allocation, and to the transportation logistics. The decision support framework requires interfacing with discrete event simulation models of manufacturing networks and assessment of multiple conflicting and user-defined performance indicators.

The joint handling of order allocation, supplier selection, and capacity planning is necessary to alleviate inconsistencies between the supply chain design and implementation phases under a flatness concept. The incorporation of the entire order fulfilment life cycle is additionally envisioned, enhanced with methods that allow easy modelling and experimentation on what-if scenarios. The relationship between logistics capabilities and supply chain agility can also be revealed through this holistic view of the constituents of the supply chain.

Regarding SCM, collaboration concepts based on cloud computing and cloud manufacturing are a game changer. Through the sharing of both ICT as well as manufacturing resources, SMEs can unleash their innovation potential and thus compete more easily in the global market.

Further to that, the measurement and management of the manufacturing network complexity should be considered as a core strategic decision together with classical objectives of cost, time, and quality. Handling a variety of market excitations and demand fluctuations is the standard practice even today in many sectors, while this trend is only bound to intensify. In parallel, a risk assessment engine should correlate complexity results and leverage them into tangible risk indicators. Complexity can then be efficiently channelled through the designed network in the less risky and unpredictable manner.

To address the increasing complexity of problems arising in the modern manufacturing network landscape, the lot sizing and material planning need to be tightly incorporated to the production planning system. The consideration of capacitated production constraints is needed in order to reflect realistic system attributes. A shared and distributed cloud-based inventory record will contain information related to MRP and ERP variables (e.g. projected on-hand quantities, scheduled order releases and receipts, changes due to stock receipts, stock withdrawals, wastes and scrap, corrections imposed by cycle counting, as well as static data that describe each item uniquely). This record should be pervasive and contain dataset groups relevant to intra-departmental variables, as well as datasets visible only to suppliers and relevant stakeholders, in order to increase the transparency of operations.

The mistreated issues of deployment and tight integration of PLM, ERP, and CRM tools must also be tackled through interfacing of legacy software systems and databases for seamless data exchange and collaboration. Software as a service PLM, ERP, and CRM solutions available to be purchased per module will be the ideal ownership model since it allows greater degree of customisation of solutions, more focused ICT deployment efforts, and reduced acquisition costs. CAD/CAM, PDM, and MPM (manufacturing process management) systems and databases will be interfaced and interact with digital mock-ups of the factory and product–services solutions as well for synchronising the physical with the digital worlds. In addition, the knowledge capturing and exploitation is pivotal in the proposed framework. Product, process, and production information is acquired from production steps and is modelled and formalised in order to be exploited by a knowledge reuse mechanism that utilises semantic reasoning. This mechanism is comprised of an ontological model that is queried by the knowledge inference engine and allows the retrieval of knowledge and its utilisation in design and planning phases. The developments should also mediate the deeper examination of the impact of product variety on the performance of manufacturing networks.

In parallel, there is an urgent need of standardisation and harmonisation of data representation for manufacturing information, for example: the product information (BoM, engineering-BoM and manufacturing-BoM), the manufacturing processes (bill of processes - BoP) including the manufacturing facilities layout, the associated relations (bill of relations - BoR), and related services (Bill of Services - BoS) should be pursued through a shared data model. Moreover, the product complexity needs to be assessed based on functional product specifications using, for instance, design structure matrices (DSM), which incorporate components (BoM), the required manufacturing and assembly processes (BoP) including sequences/plans, relationships (BoR), and the accompanying services (BoS). The complexity of the product in relation to the manufacturing network and service activities (impact on delivery time and cost, and effect on the overall reliability) will be quantified and will be incorporated in the decision-making process.

Last but not least, it should be noted that the components of the proposed framework must be offered following a software as a service delivery method and not as a rigid all-around platform. The framework should act as a cloud-based hub of different solutions, offering web-based accessibility through a central "cockpit" and visualisation of results through common browser technology and handheld devices (tablets, smartphones, etc.).