With its potential to change significantly the knowledge and skillset requirements for industrial engineers (IEs), Industry 4.0 creates a need to reassess the place of IEs to avoid a greater shock than that caused by the information technology identity crisis of the 1990s. This article examines the likely impacts of Industry 4.0 on industrial engineering (IE) and proposes enhancements to IE curricula in South Africa. Research methods include a literature review, a study of IE curricula, and a questionnaire survey of IE programmes. Results indicate that several IE functions might become somewhat transformed, less visible, or downright diminished in Industry 4.0. Emphasis has shifted from traditional IE methods to data-driven functions and cyber-physical systems. The developing mismatch needs correcting by emphasising skills such as 'big data' analytics and novel human-machine interfaces in IE curricula. Only one university in South Africa has made progress towards the adoption of an Industry 4.0 infrastructure. The authors propose a set of curriculum enrichment items as the basis for reform. OPSOMMINGIndustrie 4.0 het die potensiaal om op 'n beduidende wyse die kennis en vaardigheid vereistes vir bedryfsingenieurs (BI) te verander. Dit skep ook 'n behoefte om die posisie van bedryfsingenieurs te herevalueer, om sodoende die informasie tegnologie identiteitskrisis van die 1990's te vermy. Hierdie artikel ondersoek die waarskynlike impak van Industrie 4.0 op BI met 'n voorgestelde verbeterde kurrikulum in Suid-Afrika. Navorsingsmetodes sluit in literatuuroorsig, BI kurrikulum studie, en 'n vraelys opname van BI programme. Die resultate dui daarop dat Industrie 4.0 sommige BI funksies ietwat sal transformeer, minder sigbaar maak, of selfs totaal verminder. Klem verskuif van die tradisionele BI metodes na meer data-gedrewe funksies en kuber-fisiese stelsels. Die ontwikkelingswanaanpassing vererger regstelling deur die beklemtoning van kurrikulum vaardighede soos byvoorbeeld groot datastel analise en nuwe mens-masjien koppelvlakke. Slegs een universiteit toon vordering in die aanvaarding van en implementering van Industrie 4.0 infrastruktuur. Die skrywers stel 'n stel kurrikulum verryking items as basis vir die hervorming voor.

To manage the impact of Industry 4.0 on industrial engineering (IE) education curriculum requirements, realistic teaching and learning infrastructure such as a learning factory are required. This paper scans the literature to determine Industry 4.0's principles and interactions with IE and a learning factory, surveys relevant universities by questionnaire to determine its current status and practices, and formulates didactic design parameters for an Industry 4.0 learning factory to support IE education in South Africa, making use of existing models of cyber-physical systems and learning factory morphology. In other results, the technical universities are discovered to be more positively disposed, in general terms, to developing an Industry 4.0 learning factory than are the traditional programmes which, with one exception, prefer computational facilities. Of ten universities that offer IE, only one -a traditional programme -has made significant progress towards creating an Industry 4.0 learning factory. OPSOMMINGOm die impak van Industrie 4.0 op die bedryfsingenieurswese (BI) kurrikulum te bestuur vereis realistiese onderrig en leer infrastruktuur, soos 'n "leer-fabriek". In hierdie artikel is 'n literatuur studie uitgevoer om die beginsels van Industrie 4.0 te bepaal en die interaksies daarvan met BI en ʼn "leer-fabriek", ʼn vraelys is aan relevante universiteite gerig om die universiteit se huidige status en praktyke in hierdie verband te bepaal, en didaktiese ontwerp parameters vir ʼn Industrie 4.0 "leer-fabriek" word geformuleer binne die Suid-Afrikaanse konteks. Hierdie formulering maak gebruik van bestaande modelle van kuber-fisiese stelsels en "leer-fabriek" morfologie. Verdere resultate toon dat universiteite van tegnologie meer positief gesind is as tradisionele universiteite, wat (met die uitsondering van een geval) berekeningsfasiliteite verkies. Van die tien universiteite wat BI aanbied, het slegs een (ʼn tradisionele universiteit) noemenswaardige vordering gemaak tot die oprig van ʼn Industrie 4.0 "leer-fabriek".

No abstract

<p>This paper describes the contribution of the Tuning Methodology toward harmonisation of undergraduate mechanical engineering programmes in Africa. This methodology is an interactive process in which academics develop high quality curricula and learning standards for students through the identification of generic and subject specific competences in consultation with employers, students, graduates, peers and other stakeholders involved in Mechanical Engineering higher education. The current Tuning process involves academics in 11 universities drawn from across Africa. The aim is to collaboratively contribute to revitalizing and reforming Mechanical Engineering higher education in Africa to make it more responsive to Africa’s developmental needs. The results so far show that such a project is not only highly feasible but also holds promise for establishing compatible academic structures and reference standards across Africa, which would facilitate student and staff mobility as well as enhance cooperation not only among African academic institutions, but also between African institutions and those in the rest of the world. Eighteen generic competences and nineteen mechanical engineering-specific competences are developed, analysed and synergised to form a meta-profile that will inform the next phase of the project, which is the actual curriculum development. This activity is part of “Tuning Africa” project, which is funded through European Union-African Union collaboration.<em> </em></p>

Purpose: A paucity of proven failure criteria for brittle engineering materials exists, and this paper intends to present and validate a novel concept of equivalent stress criterion for predicting the failure of brittle isotropic homogeneous materials based on the concept of effective causative failure stress. Design/Methodology/Approach: Mathematical modelling is first performed based on strain-state equivalence, followed by conversion to the equivalent causative stress. The model is then validated with experimental and other data and with comparisons to traditional models. The material studied is BS 1452 Grade 250 continuous-cast grey cast iron with a Young’s Modulus of 39 000 MPa and ultimate tensile strength of 290 MPa. The test samples were prepared square in shape 12 mm x 12 mm to enable stresses in two perpendicular directions. Data is generated from uniaxial and bi-axial tests, performed using a standard universal testing machine, INSTRON 880, improvised to enable bi-axial recordings. Findings: Results point consistently to higher fidelity and transparency of the new model in representing the state of stress, especially in the second and fourth quadrants of the principal stress diagram, where Rankine’s criterion completely ignores stress differences and Mohr handles shear stresses in a suboptimal fashion. Both the maximum principal stresses and maximum shear stresses predicted by the proposed model are found to be somewhat greater than those from the traditional models, indicating higher accuracy and greater aggressiveness in prediction. The findings have further revealed that shearing effects play a greater role in the failure of engineering brittle materials than traditional failure theories have considered. Research Limitation: The study involved improvisation to enable biaxial stress recordings. This process was not perfect, resulting in smaller-than-ideal values of the lateral stresses. Practical implication:  The study recommended process and equipment development toward perfecting multiaxial tests. Social implication: The survey will enrich the literature with pertinent design methodology to help in product design, including social-interest products.  Originality / Value: Since truly homogeneous materials are known to withstand very high hydrostatic pressures, direct stresses alone do not constitute valid failure criteria for all loading conditions.

No abstract

Purpose: This purpose of this paper is to develop a mathematical demand forecast model as an alternative to expert-intensive methods for decision support in automobile companies using Toyota Ghana as a case. The paper explores the challenges associated with reliance on experts’ judgment in demand forecasting.  Design/Methodology/Approach: The methodology involved analysing stock reports, lost sales reports, and financial reports from Toyota Ghana to understand the effect of poor forecasting. Using data from two key managers and six sales staff, the project examines the perspectives of staff regarding the use of expert judgment for demand forecasting. Further data was collected via a questionnaire from five authorized automobile distributors and dealerships. Findings: The results revealed the adverse effects of expert-opinion forecasting, which include irregular stock quantities leading to lost sales, vehicle quality challenges leading to deterioration, and long-term negative impact on profitability. Yet demand forecasting by reliance on experts was very prevalent in the automobile industry. The developed forecast model relies on Mean Absolute Percentage Error with a smoothing constant of 0.4. was validated using recent historical data revealing a 2% variance with actual demand values, while for expert judgment the variation margin was 14%. This strongly indicated that the model yielded more accurate predictions of demand than expert predictions. Research Limitation: The case-study nature of the study means a more generalized study was still needed before the findings could be more widely applied across the automobile industry. Practical implication: The study recommended further development of scientific forecasting models for predicting demand across the automobile industry since they carried positive implications for the smooth running of the industry. This could help mitigate the challenges associated with using expert opinions in demand forecasting. Beyond this, the model could serve to provide valuable information to vehicle manufacturers, thereby yielding efficiencies in their value chains. Social implication: Accurate demand forecasting and management have positive implications for operational efficiency that minimizes customer disappointment.   Originality / Value: The model offers a better alternative for predicting demand more accurately, promoting correct stock holding quantities, avoiding stock deterioration, and reducing expenditure on quality checks, thus ultimately increasing profitability.

Purpose: The central purpose of the study is to model the process capability of drift-inherent manufacturing processes by testing the efficacy of a novel approach that filters trend from raw process data before applying statistical process control tools. A secondary aim was to ascertain the intrinsic capability of the process following the filtering. Design/Methodology/Approach: Specifically, the study focused on processes in a nail-wire drawing and tested a method for analysing data from naturally-drifting processes that involves filtering trends from data before applying appropriate tools to verify the state of statistical control and capability of the process. The physical foundation for this work is based on data collected from a nail-wire drawing process A total of 250 data points were gathered over 50 days in two successive instances of 125 points, each spanning 25 days. Data were checked for normality followed by mathematical conditioning to filter out the wear trend before analysis by normal statistical process capability and control chart procedures. Findings: Results show that the proposed method is effective for tracking hidden effects in steadily drifting processes such as those associated with wear. After filtering, the data is found to fall within product specifications, though robust statistical control was still required through appropriate measures. Research Limitation: To investigate the intrinsic nature of the process outside of the process, material wear is assumed to be the sole source of the inherent drift. In processes where several sources of inherent drift are present, this may pose a problem. Additionally, the study focused on just one plant; however, data from other similar plants will be needed to buttress the findings and widen the scope of applicability of the findings. Practical implication:  The competitive pressures of today’s marketplace are increasingly forcing companies to place premium emphasis on product quality while aiming at the lowest costs possible. The study recommends continuous and sustained efforts to reduce variation in manufacturing processes to brighten firms’ competitive survival. Social implication: The study will bring new knowledge to metal product manufacturers that can help them deliver high-quality products and value for money to consumers.   Originality / Value: New insights afforded by the study’s approach include revelations of otherwise hidden measurement errors as well as undersized finishing-die. Any other out-of-control occurrences can then be more easily tracked and identified and root-cause analysis applied to eliminate them.  This is a practical study that seeks to develop an innovative way to monitor the quality of processes whose tracking is made difficult by inherent drift. The easy-to-adopt methodology can be implemented by metal product manufacturers grappling with drift-inherent processes.