Intended learning outcomes: Produce an overview on terms such as intelligent supply chain, cyber-physical system, Industry 4.0, smart sensor, internet of things (IoT), digital twin, big data. Disclose the potentials of additive manufacturing (3D printing) and personalized medication.
Increasingly, the buyer's market demands personalized production even of consumer goods. The product is ever more frequently defined in direct interaction with the customer. At the same time, clearly identifiable market segments disappear. Brand names serve increasingly to reflect the personality of the customer instead of providing a function as they used to do.
The term intelligent supply chain stands for, according to [ASCM22], the use of emerging and intelligent technologies such as artificial intelligence, big data, and advanced analytics software within the supply chain to improve its value and efficiency.
The ideas outlined below show how, in the future, information technology (IT) will play a substantially more significant role in the production of physical goods than they do today.
In a cyber-physical system, IT devices that control physical objects (e.g., mechanical and electronic objects) work together over a communication network.
In industry, the trend is increasingly towards a complete network that covers all of the relevant machines, both within a company and across companies, and regardless of the machine's manufacturer. The digital components will allow automated production to adapt increasingly quickly to changing requirements. In the USA, a first step in this direction was taken in 2014 when the Industrial Internet Consortium (IIC) was founded. Many large companies work together in this organization to draw up common standards.
The term Industry 4.0 was coined by Acatech (German Academy of Science and Engineering) in 2011. It It postulates a fourth industrial revolution [note 119], following on from the previous revolutions of mechanization, electrification, and computerization.
Accordingly, digitization is expected to replace many production technologies in a disruptive way, as happened, for example, with digital photography in a short time. At the same time, the individualization of products is to increase without costs rising significantly. Building blocks for this include smart sensors, the Internet of Things, digital twin, Big Data and additive manufacturing, which are briefly presented below. In practice, however, development is rather continuous. But companies that rely on a particular analog technology (e.g., analog photography or analog telephony) run a considerable risk. One current area is offset printing: this is increasingly giving way to digital printing, which eliminates all the steps involved in creating the fixed artwork, i.e. the printing plate. Thus, the production process is much shorter, and each sheet can be printed differently (keyword "personalized printing").
Apart from simply measuring things (as a conventional sensor would), a Smart Sensor can also process the measured data and make the results available in the required form.
The “intelligence” is provided by a microprocessor, a result of microsystem and nanosystem technologies. Here again, the driving force is the need for personalized production. Examples include accelerometers, motion sensors, and magnetic field sensors for functional movement therapy. The use of sensor technology helps improve accuracy in the observation of the patients’ movements. The data collection results can be processed by the sensor, and immediately converted to movement objectives that are tailored for the patient and which can be displayed on a mirror, for example.
The Internet of Things (IoT) is a network of material or non-material goods or objects (“things”) that are connected to each other and that can exchange data. An integrated computer identifies each “Thing” and can communicate via the Internet infrastructure.
As a sensor, or with the help of a sensor, the “Thing” can capture useful data, which it can then independently send on to other objects, either humans or machines. One example would be Internet-based building management systems (e.g., light, temperature, and humidity) that are adapted to each resident. The postulate is that the Internet of Things also allows large volumes of data to be gathered from remote locations, which in turn enables big data.
A digital twin is, according to [ASCM22], an exact virtual replica or model of a real-world process, product, or service used to digitally simulate, test, model, and monitor it.
Similar to digital architecture, this is an effective and efficient way to achieve a result that is better tailored to future users, i.e. personalized.
Big data is a broad term for data sets that are so large or complex that traditional data processing applications are unable to capture, store, and process them.
The term is therefore relative to technologies that are currently used, and like several other terms in this article, it appears to be more of a postulate than a reality at the moment. It is often applied to “advanced” methods of evaluating data with the aim of improving decision making, which in turn reduces risk or increases efficiency. The idea is that the large volumes of data will allow statistical analytics software to identify previously unknown correlations or trends, both in physical systems (e.g., meteorology, environmental research) and in socio-technical systems (e.g., businesses, government). Data protection is an important issue here.
Additive manufacturing is nowadays more widely known as 3D printing (although, in doing so, there is actually no printing involved). It is a process that offers the possibility of creating three-dimensional objects. A 3D model created by CAD software can be used to produce an item by building up successive layers of a material (plastic or metal).
One benefit of this process lies in the efficiency and speed with which the first item of a lot can be produced: The slow, expensive mould-making process is no longer needed. That means this process is particularly well-suited to making prototypes. Conventional (e.g., abrasive) methods may still be more cost-effective for mass production, even assuming that they are not a necessity for quality reasons. A second benefit is that lot size one can be produced cheaply. Totally different 3D shapes can even be produced in a container, i.e., in a single production batch. This makes the process particularly attractive for spare parts (assuming sufficient quality). Identifying the optimum arrangement needs an algorithm, in much the same way as a cutting optimizer is needed for 2D cutting, such as when using a laser cutting machine to trim sheet metal. For further information, see www.additively.com. Making toys at home, too, underscores 3D printing's potential for personalized production.
Personalized medication is a patient-focused approach that incorporates both medication and the dispensing process.
This is an important concept for health care of the future. The processes rely heavily on the use of various information technologies. In the case of solid forms (e.g., tablets), this can involve automatically dividing the blister packs up into individual doses and packing the individual doses according the patient's prescription for delivery to the patient at the appropriate time, with integrated tracking and tracing. For liquids, it involves the automatic production, tailored to the patient, of liquid medicines (e.g., for cancer treatment), again organized by date and time for delivery to the patient and, if necessary, with an appropriate cold chain, i.e., an end-to-end cooling system for transport from the producer to the user. Last but not least: 3D printing could in the future be used to produce biomedical fibre, which could enable dispensing devices for medication to be tailored very specifically to the patient.
Course section 1.3: Subsections and their intended learning outcomes
1.3 Logistics, Operations and Supply Chain Management Strategies in the Entrepreneurial Context
Intended learning outcomes: Differentiate between various entrepreneurial objectives in a company and in a supply chain. Explain resolving conflicting entrepreneurial objectives. Describe the customer order penetration point (OPP) and the coordination with product and process design. Produce an overview on the target area flexibility: investments in enabling organizations, processes, basic technologies, and technologies toward personalized production.
1.3.1 Company Performance, Supply Chain Performance: QCDF Objectives — Entrepreneurial Objectives in a Company and in a Supply Chain
Intended learning outcomes: Produce an overview on company performance, or supply chain performance. Identify entrepreneurial objectives affected by logistics, operations, and supply chain management in the target areas of quality, costs, delivery, and flexibility (QCDF). Describe target areas in supply chain performance across companies.
1.3.2 Supply Chain Strategy, Opportunity and Opportunity Cost
Intended learning outcomes: Produce an overview on the supply chain strategy and the business plan. Describe opportunity and opportunity cost.
1.3.2b Return on Net Assets (RONA), Net Working Capital and Supply Chain Initivative (SCI) — Resolving Conflicting QCDF Objectives
Intended learning outcomes: Explain the potential for conflicting QCDF objectives. Present terms such as return on net assets, net income, profit after tax, net working capital (RONA), and primary entrepreneurial objective. Disclose the objective of a supply chain initiative (SCI).
1.3.3 Customer Order Penetration Point (OPP), or Customer Order Decoupling Point (CODP), and Approaches such as Modular Product Concept, (Late) Customization, Postponement
Intended learning outcomes: Explain the determination of the (customer) order penetration point, or customer order decoupling point, and the stocking level. Describe the modular product concept, the concepts of customization and late customization as well as the postponement approach.
1.3.4 Target Area Flexibility: Investments in Enabling Organizations, Processes, and Basic Technologies
Intended learning outcomes: Present flexibility, agility and different aspects thereof. Differentiate between short, medium and long-term potentials developed by agile companies. Describe various enabling information technologies allowing worldwide running order coordination.
1.3.5 The Intelligent Supply Chain — Cyber-Physical System, Industry 4.0, Smart Sensor, Internet of Things (IoT), Digital Twin, Big Data, Additive Manufacturing, 3D printing: Enabling Technologies Toward Personalized Production
Intended learning outcomes: Produce an overview on terms such as intelligent supply chain, cyber-physical system, Industry 4.0, smart sensor, internet of things (IoT), digital twin, big data. Disclose the potentials of additive manufacturing (3D printing) and personalized medication.
