Learning Design Thinking Through a Hands-On Learning Model

Introduction

Approaches to design of products and systems have evolved over the years. Traditional design approaches have been regarded as “over the fence” methods, referring to their fragmented nature where silos of teams of disjointed departments work on part of the solution and pass on the output to subsequent departments until the final stage of launch. Sequential design methods are generally slow and ineffective. The need to involve the whole design team from the onset bore Concurrent Engineering (CE). CE is a simultaneous process, has team approach, and focuses on the customer. Other techniques such as Quality Function Deployment (QFD), complement concurrent engineering by bringing in customer focus. The history of CE dates back to the 1970s, when Japanese companies started using the method informally. First formal application of the method was by the Defense Advanced Projects Agency (DARPA) in America in 1982 (Menon & Graham, 1996). CE gained the hype in American, Japanese and European companies for formalizing and simultaneous execution of product-process design activities for new products. Although the customer centricity and multi-disciplinary team approach in CE brings innovation, the method is largely a formalized approach to traditional engineering of new products and enhancement of research and development (R&D) in companies. Among great achievements of CE, are shortening of time-to-market, convergence of diverse ideas through multi-disciplinary teams, customer focus, iterative simulation and rapid prototyping, and fewer Engineering Change Orders (ECO) issued after product launch.

The modern economy has transited to an “as a service economy”. The 4^th industrial revolution (Industry 4.0) is taking place in the digital and knowledge based economy. Simply put, Industry 4.0 is the transition from traditional means of doing work to smart automation, using modern smart technologies. The major enablers of Industry 4.0 are integrated technologies that include Internet of Things (IoT), Cyber Physical Systems (CPS), and Cloud Computing (CC) (Baena, Guarin, Mora, Sauza, & Retat, 2017; Posada et al., 2015; Kagermann, Helbig, Hellinger, & Wahlster, 2013). Cyber-physical systems (CPS) are physical and engineered systems, whose operations are monitored, coordinated, controlled and integrated by a computing and communication core (Rajkumar, Lee, Sha, & Stankovic, 2010). CPS-enabled smart factories have a network of intelligent objects linking products and assets with information from the internet, as well as capturing context information. Many factors contribute to the progression of manufacturing trends through to the current phase (Industry 4.0) and beyond. Some of them include shorter product life cycles, increasing product variation (mass customization), volatile markets, cost reduction pressures, scarce resources, cleaner production, lack of skilled workforce and aging community (Gwangwava, Mpofu, & Mhlanga, 2016). Modern technologies involve the use of connected sensors that enable data to be shared and accessed by people and machines across bigger networks in the services and manufacturing industries. These technologies extend the internet into the real world, embracing everyday objects (Mell & Grance, 2011). Individuals and corporate end users access the cloud through internet and access spans over different enterprises and platforms. Industry 4.0 is sustained by a new kind of worker—a knowledge worker. Industry leaders, managers and workers must possess skill sets to adapt and manage their operations in Industry 4.0. Critical thinking, problem solving, innovation, and communication are some crucial skills required in Industry 4.0. Business, education, and government must be innovative in training and retraining the Industry 4.0 compliant workforce.