Keywords

1 Introduction

Data collection is critical in a manufacturing system. The ability to collect accurate data in an affordable manner is important to an enabling appropriate decision making. Small medium sized enterprises (SMEs) lack the funds required to collect accurate data in real-time; hence poor planning and productivity [1]. The ability to collect, analyse and communicate data through an affordable technique is crucial to any organisation. Globally, smart manufacturing is getting recognised as a value adding technique with the introduction of the fourth industrial revolution [2]. The SMEs that are readily flexible and agile stand to benefit immensely from adoption and implementation of data collection architecture. For this reason, effective data collection structure is necessary. Smart Manufacturing Systems (SMS) defines the view of predictive tool, decision devising, and susceptibility to ubiquitous information from big data environments to help manufacturing enterprises better predict, balance production and improve efficiency and productivity [3].

SMS pronounces the merging of the digital and physical worlds within the manufacturing setting [4]. The National Institute of Standards and Technology (NIST) defines smart manufacturing as “fully-integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs” [5]. Smart manufacturing is characterised by digitalisation and service-orientation, connected and autonomous cyber physical system (CPS) objects, collaborative supply networks, integrated and decentralised decision-making, interoperability and advanced analytics [6]. It is considered as a new paradigm that carries the convergence of the cutting-edge information and communications technology (ICT) and manufacturing technologies. The added advantage of meeting the personalised customers’ needs rather than only completing the manufactured products will intensify customers’ interest [9]. Thus, SMS is estimated to be far more capable than usual manufacturing processes [10]. Industry 4.0 gives a boost to computer-integrated manufacturing (CIM), allowing a more decentralised architecture based on Customer Premises Network (CPN) [11]. Internet of Things (IoT) allows integrating devices and equipment into the company’s information system infrastructure [12]. While Industry 4.0 tools enhances Machine to Machine (M2M) communication [13], CIM was initially developed with a focus on human employees [14]. This includes self-organised diagnostics and repair request communicated to machine and equipment suppliers and allowing smart and intelligent predictive maintenance (SIPM) [15]. Components within the Industry 4.0-framework act as autonomous agents [16]. The transition from usual manufacturing towards smart manufacturing usually passes through the stages of connected (computerisation and connectivity), transparent (visibility and transparency), and intelligent (predictive capacity and adaptability [17].

Big data plays a key role in SMS, as big data is used for manufacturing operations’ optimisation, efficient planning and control, predictive supply, fault diagnosis, asset utilisation, and risk assessment [18]. Implementation of data science for SMS can be achieved through four enabling technical approaches; these include Data Technology (DT), Analytic Technology (AT), Platform Technology (PT) and Operations Technology (OT) [19]. Data-driven can be utilised in manufacturing, however, the conceptual model to implement such is required. Therefore, this paper pushes for a simple effective framework to structure data for the benefit of SMEs to ensure competitiveness.

2 Literature Review

Enterprise architecture is defined as a model organization use to design and implement information, technology, infrastructure and systems [21]. Enterprise data architecture in this study refers to the design and implementation of a data collection system for SME. Data collection is a costly exercise and manufacturing should provide cost-effective, sustainable, and safe manufacturing. [22]. The ability to collect data effectively requires enterprises to invest in data collection tools and systems. Many SMEs do not have the funds to invest in such resources; hence, their organisation suffer from low productivity and efficiency [20]. Industry 4.0 has opened the opportunity for organisations to take advantage of the internet of things, big data technology, and cloud computing without investing a significant amount of resources and skills required to design such systems. Data collection architecture proposed by multi-level integration is intended for large industries [20]. Utilising data collection tools linked to cloud hosting can have SMEs manufacturers to make decisions based on data, hence improving the business's operations. There is an extensive definition of smart manufacturing, according to Wallace and Riddick [23]. Smart manufacturing is described as a concentrated data application of information technology at the shop floor level and above to enable intelligent, efficient, and responsive operations [23]. The highlight concerning SM is the use of advanced data analytics to improve manufacturing operations at all levels of network, from shop floor, factory, supply chain, with the aid of information and communication technology (ICT) [24]. Some authors extended the knowledge of the smart manufacturing framework beyond the perception of manufacturing, but to all-round life cycle data management [25]. Based on the past research, review relating to data architecture designed is conducted in this section to specifically conceptualise for a low-level SMEs.

2.1 Smart Manufacturing Systems of Industry 4.0

Smart manufacturing is a data-driven connected network of a number of integrated technologies such as cloud computing, cyber-physical production systems (CPPS), internet of things (IoT), robotics/automation; all these are suitable for SMEs adoption [12]. The technology roadmap and changes in manufacturing paradigms are categorised into three basic levels [12]:

  1. 1.

    Information and communication technology (ICT): Computer network – Internet; Information space – World Wide Web (WWW or the Web); Agent technology; Mobile technology (communication protocol); Cloud technology; Internet of Things; Big data; etc.

  2. 2.

    Manufacturing technologies: Machining techniques – Computer Numerical Control (CNC); additive manufacturing – 3D printing; Reconfigurable machine tools; etc.

  3. 3.

    Artificial intelligence (AI): Computer & computing techniques; Cognitive technology – software-based; Computer-aided Diagram/Manufacturing (CAD/CAM), Enterprise Resource Planning (ERP), etc.; Robotics techniques – industrial robots, etc.; Virtual techniques; etc.

2.2 Benefit of Smart Manufacturing and Data Analytics

Post pandemic effect changes the way business is done globally, according to Almeida et at [24], 24% of businesses felt the urge to invest more in digital workflow and optimized automation technologies while 18% of companies expect to spend more on data analytics tools. The increase in fluctuating demands from customers throughout the entire manufacturing operations necessitate real-time response by the manufacturers, SMEs inclusive. Data in manufacturing is no longer a choice; it is now the required tool to help SME stay competitive in the vast manufacturing market. An architecture methodology proposed in [24] described work outlines for the integration of data collection tools, in-house manufacturing systems, and cloud computing to enable smart manufacturing systems with artificial intelligence (AI). However, the small manufacturing ventures are yet to utilize the full advantage of data analytics for efficient use.

2.3 Big Data Informatics for Small Medium Enterprise

The consideration of business informatics according to industry 4.0 principles, SME companies need to tackle two factors to remain active [18]: (i) analytical tools that can accurately capture and predict consumer patterns, and (ii) an automated closed-loop feedback system that can intelligently inform business processes to respond to changes in real-time based on the inputs received (data trends, user experience, etc.). Simplified big data value chain includes at least 4 stages [18]:

  1. 1.

    Data sources, during the data-generation stage, a stream of data is created from a variety of sources: sensors, human input, etc.

  2. 2.

    The raw data is combined with data from other sources, classified, and stored in some data repository.

  3. 3.

    Algorithms and analytics are applied by an intelligence engine to interpret and provide utility to the aggregated data.

  4. 4.

    The outputs of the intelligence engine are converted to tangible values, insights, or recommendations.

The opportunities for sustainable smart manufacturing environments from a macro and micro perspective, these efficient data managements are pinned on: business models, value-creation network, products and processes [21, 24]. Table 1 present a summary of smart manufacturing approaches useful for SME adoption.

Table 1. Summary of smart manufacturing and scope of SME adoption.
Full size table

3 Methodology

Manufacturing system architecture demonstration is subjective to enterprise requirements. As such, researcher seeks to design a conceptual data collection architecture for SME to manage; thereby becoming efficiently smart in manufacturing. The usual knowledge about data analysis process follows the cross industry standard process for data mining (CRISP-DM). The concept known as CRISP-DM approach is the basis set for data mining and useful for entry level of manufacturing ventures, adopted [33] (see Fig. 1).

Fig. 1.
figure 1

Cross industry standard process for data mining (CRISP-DM).

Full size image

CRISP-DM approach introduces standard phases for data science in business, namely;

  • Business understanding

  • Data understanding

  • Data preparation

  • Modelling

  • Evaluation

  • Utilization

To demonstrate an effective Data Enterprise Architecture (DEA) solution, the research partner with an SME to solve basic real life manufacturing problem; a conceptual solution was developed after point and dot assessment. The DEA application focused on productivity record and reduction of downtime as a problem to be solved.

In order to project a cognitive DEA solution, the use of database roles was conceptualized to form a broader flowchart inter-network based on functions and attributes. The attributes are in line with the company’s character (a small-scale textile venture), starting with the process production configuration sub-divided into functions. An encompassing data collection architecture was conceptualised for the SME smart manufacturing users. This was done to ensure that there is a control of what to use in an appropriate environment and get the targeted results. The data architecture structure conceptualized in this study to attain a smart system phase is presented in Fig. 2.

Fig. 2.
figure 2

Data Architecture for implementing data analytics

Full size image

With smart manufacturing system, real time information is essential. Thus, the function and attributes of decomposable data capturing architecture on the bases of SME venture understanding, model, network and configuration is denoted. This concept premised on basic intelligent system using cell sensor, external server, and internal database for data analysis and prediction. The structure is to allow enterprise to collect data, analyse, and decide on the functioning of the manufacturing decision, continuity, quality of products and digital order.

4 Conclusion

Unambiguously smart enabled manufacturing has been critically viewed as a tool for the SMEs to thrive in the competitive business market. A small and medium-sized venture will benefit more through the adoption of smart technologies to improve product quality, reduce lead times, reduce overtime, better cost estimation, increasing throughput, improving productivity, minimising work-in-process and better production planning among others. Among these operational benefits, decision making with appropriate and precise information is crucial in achieving the aforementioned benefits. As such, this study had proposed a data architecture for SME smart manufacturing approach; this is different in a sense that it is simple, and it is a design-fit-all for any person without extensive skills to implement. SME in nature are hesitant to adopt technology, this architecture is designed to ensure that it is easy for the middle-sized manufacturing sector to adopt thereby leading to technology diffusion. Recommendation for this study on the long term includes management activities with respect to manufacturing enterprise execution and implementation with regards to specific small scale ventures. This contribute to closing gaps encountered and the acquisition and implementation of information systems and technology.