A DSS-Based Framework for Enhancing Collaborative Web-Based Operations Management in Manufacturing SME Supply Chains

According to the level of collaboration and the number of participants involved, supply chain collaboration can be classified as: collaboration among members, a value chain and as a supply chain network (Poirier 2002). Therefore, it is important to consider that collaboration will imply the creation of semantic descriptions that provide a mutual understanding between the firms or nodes that, directly or indirectly, are wishing to collaborate (Chen and Doumeingts 2003). Thus, realising that there may be different types of collaboration among the nodes in the supply chain which, in turn, could belong to different supply chains, and also that there could also be different collaborative behaviours, models should address each case since the generation of a single model to homogenise collaboration is difficult to manage (Poler et al. 2008). There are many definitions that can be found for the collaboration concept and in relation to the framework that is addressed in this scientific research contribution, the definitions presented by Hernández et al. (2011), Hernández et al. (2014d) stand out as a key support for this purpose. These authors consider that collaboration could be defined as the way in which firms, belonging to the supply chain, will work together to accomplish shared and private objectives by means of the information exchange and the consideration of negotiated decisions. In addition to this, Cao and Zhang (2011) established that the most important way for supporting the information exchange across supply chains in collaborative environments is by considering that every supply chain node will tend to favour a collaborative relationship. This concept was originally defined by Lewieki and Litterer (1985) and establishes that all the participants in a network, or at least the vast majority of them, will collaborate to become more competitive with regard to other supply chains. Sapena et al. (2008) suggested the collaboration process can be divided into three sequential states. The first relates to interchanging messages (or information). The second contemplates defining the restrictions associated with the processes and, therefore, defines the variables with which nodes will assess the results. The third and final one considers the ways to model restrictions to obtain model generation-based solutions, architectures and tools to support supply chain collaborative policies and mechanisms. Moreover, with this collaboration terminology, the chain could become more competitive in terms of quality, costs and times as it would be more efficient (Soosay et al. 2008). Conversely, Chen and Chen (2005) consider that collaborative relationships in the supply chain context will have a positive impact on the integration of the chain’s replenishment processes and its coordination mechanisms. This means that the advantages which collaborative relationships offer are related to the profits gained by not only each member of the chain, but also by the whole chain (Cao and Zhang 2011). Thus, there will be a tendency to distribute these profits throughout the chain rather than them being used at a local level by chain members. One example of this can be found in the logistics area of distribution where collaborative management in a supply chain will better support the constant modifications made in aspects such as defining objectives, distributing raw materials, transport planning and requirements to support information interchange. The supply chain will tend to be more flexible in terms of physical distributions and will, therefore, seek to diminish any inefficiency encountered in chain management and planning (Kwon et al. 2007). According to Reiner and Trcka (2004), and since supplier nodes will be able to identify other suppliers and/or customers’ requirements, a supplier must be allowed to communicate (downstream) and access the supply chain’s requirements and needs to the customer in order to perform a collaborative behaviour across the supply chain network.

Manthou et al. (2004) pointed out that the appropriate use of technologies can help strengthen relationships among supply chain members and will, therefore, favour integration and collaboration between the various transactions and events present in the different supply chain processes. In this way, each node could analyse and improve its plans at a local level by considering this external information (Dudek and Stadtler 2005). Therefore, collaboration in the supply chain will address the joint creation of information interchange processes, followed by the implementation of information systems that support these processes. Essentially, the support of these systems considers the chain’s physical aspects such as the nodes’ geographical distribution, as well as data or information aspects such as demand patterns or product characteristics (Holweg et al. 2005). So from the supply chain collaboration viewpoint, it is important to bear in mind that technologies should be able to rapidly and comprehensively respond to customer demand, which is generally variable over time (Tolone 2000). Hence, the use of technologies with algorithmic functionality must be employed to support the changes made in demand patterns. One solution to this is presented by Deng et al. (2011) who consider using bi-level linear programming to support supply chain management. Integrating between technologies and tools to support data analysis and information can help improve decision-making processes in supply chains (Jiang and Chen 2010). In line with this, decision-making processes should contemplate the different behaviours of the decision makers in the supply chain (manufacturers, retailers, customers, etc.) to maximise the chain’s profits (Cruz 2009).

In light of this, a prominent characteristic is that collaboration in the supply chain requires efficient information exchange. This information will support planning processes in chain nodes. In other words, nodes should regard information exchange as a fundamental pillar to support collaboration within the chain. However, it is important to contemplate those technologies that are linked to collaborative processes, as they should help to include information in the supply chain and to define those architectures that help the collaborative processes in the chain. Therefore, the information interchange process is a complex task which relates the decisions made about what and how much information is to be shared, and also establishes the suitable times for these interchanges to take place to favour the collaboration and synchronisation in supply chain processes. Solutions must be able to be replicated in each chain node, and will also become an architecture which will provide the models and modelling languages required to support the different perspectives of collaborative processes. Moreover, from a real supply chain management viewpoint, supporting SMEs’ planning processes in an increasingly collaborative context must consider a study of the collaborative relations and how they are framed within the chain’s different configurations. However, conceptualisation of flows and business languages will be of much importance to permit the inter-node communication.

Local as well as international SMEs increasingly recognise their importance in the global economy as well as their relevance to supply chains. However, these supply chain systems are often not well aligned within the requirements of a global, dynamic and very volatile market environment. Koh et al. (2007) realised that the relationship between supply chain management practices and operational performance indicates that supply chain management practices might directly influence the operational performance of SMEs. Furthermore, the increasing trend for product customization and service provision associated with products requires choosing the right partners and effectively organizing the collaborative processes. In light of this, the SMEs’ environment is a key domain to be studied, which constantly requires to be analysed in order to implement new required technology-based solution. This is in order to help the decision-making process for their planning and management activities across supply chain networks. Ayyagari et al. (2007) noted that SMEs play a central role in local economies because the high employment and key contributions to local and regional activities Goldberg and Palladini (2008) and if SMEs improve their management practices they can become more competitive in international as well as local markets (Cortes-Lobos 2013), hence they are potential contributors to national economy growth.

Therefore, as pointed by Hernández et al. (2011, 2014a, b, c, d), it is established that an effective collaboration leads to collaborative advantages and also to better partnership performance (Hernández et al. 2011), hence, the use of compatible technologies amongst them are key and relevant. Moreover, during the last ten years, web technologies have provided users with a large set of new tools for remote collaboration and information sharing (Kuo 2010). In this context, managers at SMEs are moving toward the use of improved information sharing and forecasting mechanisms throughout the network in order to remain competitive (Byrne and Heavey 2006). This also will support SMEs to become more competitive and enhance their innovation processes from a collaborative point of view. Co-innovation networks integrate external inputs and resources and adopt web capabilities and facilitators to achieve the targeted values. From Wolf and Harmon (2012) and Afsarmanesh and Camarinha-Matos (2008), it is apparent that at the service delivery frontier, business processes and business services have the responsibility to support and establish co-working and sharing between stakeholders, partners, internal strategic business units, and customers. For supporting this communication, IDEFx, BPMN, UML, EPC and even execution languages (namely, WS-CDL, BPEL) are some of the popular methods used for this purpose. A further review of these languages can be seen in Hernández et al. (2014a). Hence, to consider a maximum level of preparedness for collaboration, and to support integration possibilities within collaboration networks, suitable business process modelling languages can support the co-innovative environment and provide an extendable standard structure, formalism-based robustness, and easy-to-use mediator.

Considering the global trends of the economy, supplementary technologies and standards might be adopted such as WSDL and OWL-s to ensure the extensibility of the customised SME models and solutions. Moreover, co-innovation business service taxonomies might also be considered in order to relate the structure of business services to the resources. The purpose of this is to achieve the core competences of the supply chain structure. Hence, the proposed framework in Sect. 3, is orientated to support the creation and execution of integrated service structures, providing an adequate service-oriented co-innovation/collaboration framework, and service structure to enhance the decision-making process, especially under dynamic service perspectives, when non-existing services are designed when users request it (Rao and Su 2005).

The innovation capacity issues in SMEs have been analysed for many years. Nevertheless, innovation in SME product and services remains as a key issue (Sepulveda et al. 2014). Oke et al. (2007) noted a clear link between innovation and SME sales turnover. Policies and government initiatives directed at SMEs tend to encourage the development of radical innovations, supported in most of the cases by R&D grants and programmes, and enhancing the possibilities of entering into new markets. Therefore, and considering the modelling methodology from Hernández et al. (2014d), a holistic framework approach for supporting the decision-making process in SMEs is proposed to provide feasible solutions for designing, implementing and enhancing sustainable and collaborative operations management decision-making and co-innovation in manufacturing SMEs (see Fig. 1). The proposed framework provides a clear identification of the core supply chain-based manufacturing SME decision-making elements. These elements include: product design, management and manufacturing. In addition, a service perspective is also considered to enhance the SME’s information, communication and technologies (ICT) platforms.

As depicted in Fig. 1, the right selection and provision of models, tools, and collaborative technologies, will enable a faster and more effective business strategy for handling the products and customer service perspective of key SME processes. Hence, and considering the main SME environment characteristics, the proposed framework also considers the high disruptions on demand and the volatile market behaviours as part of its modelling methodology and structure. Therefore, the holistic perspective is adopted in order to assess different kinds of dimensions in terms of the SME performance, such as: the services that contribute to enhance the products, the related manufacturing processes and the SME’s network related to the main operations management decisions. In terms of co-innovation, this framework also establishes the holistic view for capturing the values, risks and sustainable conditions for transforming the traditional product life cycle into a product-service life cycle. Moreover, and driven by real-life SME industrial needs, the solutions established from the proposed framework take into account the structure for coping with SME operations management requirements dynamically, in real-time and through the consideration of a synchronised supply chain with agile response capabilities supported by collaborative mechanisms and structures with customers and suppliers. This is to be applied in the following section (Sect. 4), from which a web-based model is provided.

In this context, the mass customisation of products is also pursued by considering high-customer involvement under a co-design approach to meet their individual needs/choices with regard to the variety of different product-service features. Furthermore, in order to support national and regional SMEs with their dynamic operation at multiple locations in the co-innovation processes, the involvement of the customer with local suppliers and other stakeholders, particularly those geographically close to the customer, are also pursued. However, it is important to highlight that this framework was conceived based on the recognition that co-innovation involves many manufacturing processes and requires a network of stakeholders to be built in order to generate an agile and robust system for producing these service-enhanced products. Therefore it is regarded as a realistic approach to be useful for end SME users. Then, through warning signals of any unexpected behaviour from predicted life-cycle processes, stakeholders will be advised when it is necessary to reconfigure the service-enhanced products. In this way, a re-adaptable and real-time based system oriented to meet, cope and cover existing and new stakeholders’ expectations is also considered. Thus, the requirements for enhancing the operations management and co-innovation decision-making processes in SME environments are dealt with in this holistic framework in the following ways:

The modelling domain encompasses the production planning process under a collaborative regime (see Fig. 2). The inputs and outputs of the process have been identified based on the literature and the SME interviews.

From many modelling languages, the IDEF0 approach was selected, since the main governances for these models are the information flow and the decision flow (see Fig. 3). In this context, the production planning process is to be related to the information transformation of the materials requirements from the end customers to the lower-tier suppliers and the decision-making reflection from the suppliers to the customers.

Therefore, the collaborative production planning process, in the precision-engineering SME (PE–SMEs), is built on the real-time demands of the products from the customers of the manufacturer and the real-time material demands sent from the manufacturer to its suppliers.

As depicted in Fig. 3, one important activity in the model is the traditional MRP (Materials Requirement Planning) process calculation, which is determined by a traditional ERP system. The output of the MRP is considered as a part of the input information of the production planning for suppliers. The MRP outputs generally include the components, real-time finished goods inventory and the real-time requirements planning (see Fig. 4). Therefore, the outputs of the MRP will be automatically be updated to the collaborative platform online.

In this case, the suppliers and the PE–SME will make their production plan based on the MRP outputs of the customers. Meanwhile, the decision flows in the suppliers, manufacturer and customer are also defined in the model. The conceptual web-based solution for the PE–SME is presented in Fig. 5.

Hence, and assuming that there are three main roles in the process, Fig. 5 shows that suppliers and customers can be seen as a group of individuals with similar manufacturing management characteristic behaviours in terms of information provision. The customers normally represent the large companies, which produce the finished precision-engineered products; the manufacturer and the suppliers are normally linked to the companies that produce the components for the customers. Then, the operators will compare the forecasted quantities against the real sales in order to make the product replenishment decision. Therefore, through the web-based solution, operators can log-on to the collaborative platform and receive the MRP information from the shared ERP database system. Collaborative reports will be generated and shared amongst the supply chain parties through the network. The manufacturer can log-on to the OpenBook\(^\circledR \) and view the customers’ MRP and the material requirements. Manufacturers are able to check their finished goods inventory and support their decision-making processes collaboratively. Consequently, suppliers and manufacturers will be able to report their quotation, capacity, schedule, inventory and productive time to the customers. In this way, the individual decision-making process is now enhanced by up-to-date, shared, accessible and ready-to-use information available in the collaborative web-based system.