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6.3Technology Strategy and Business Industry
Learn MOT (management of technology), which leverages technology as a management resource, innovation that brings disruptive change, the technology roadmap that lays out future technology development, and practical AI applications including IoT, machine learning, deep learning, CNN (convolutional neural network), and generative AI, plus business systems such as POS and EDI, e-business such as e-commerce and fintech, and JIT, which eliminates waste in production.
In technology strategy and business industry, it helps to first grasp the big picture of how to leverage technology in management (MOT, innovation, roadmaps), then broaden out to the concrete AI technologies that have grown more prominent in recent exams, and finally to the business systems (POS, EDI, etc.) specific to each industry.
6.3.1MOT and innovation
- MOT (Management of Technology) is a management philosophy for connecting technical strength to business results and corporate value. It refers to managing through the barriers in commercializing technology—often described as the "devil's river," the "valley of death," and "Darwin's sea"—where strong technology can still fail to become a successful business.
- Innovation is creating unprecedented value through the reform of technology or mechanisms. Distinguish sustaining innovation, which merely improves existing products, from disruptive innovation, which disrupts and replaces existing markets and products. A technology roadmap is a plan diagram, laid out along a time axis, showing the technologies a company should hold or acquire and when and into which product/business they will be deployed—it connects technology strategy to the business plan.
6.3.2IoT and AI adoption
- IoT (Internet of Things) is a mechanism that embeds sensors and communication capability into various things to collect and interconnect data over the internet. A typical use is collecting a factory machine's operating status via sensors for remote monitoring.
- Machine learning is a technology by which a computer automatically learns regularities and patterns from data to make predictions or judgments about unknown data. Deep learning is a machine-learning method using multi-layer neural networks modeled on the human nervous system; its hallmark is automatically acquiring features from data rather than requiring a human to hand-design them.
- CNN (Convolutional Neural Network) is one of the representative deep-learning methods, a network structure with particular strength in image recognition. Its hallmark is the convolutional layer, which extracts an image's local features (edges, patterns, etc.) in stages; applications include automating the visual inspection of product appearance. Generative AI is AI technology that generates new content—text, images, audio, program code, and so on—typically built on large language models. Its application to business efficiency, such as drafting business documents or automating inquiry response, is spreading rapidly.
6.3.3Business systems and e-business
- POS (Point of Sale) is a mechanism that reads a barcode or similar at the point of sale and records/aggregates sales and inventory data in real time, capturing what sold, when, and how many, to inform ordering and merchandising decisions. EDI (Electronic Data Interchange) is a mechanism for electronically exchanging transaction data—purchase orders, invoices, etc.—in a standardized format between companies, eliminating paper slips and speeding processing while reducing errors.
- e-business is business activity broadly leveraging the internet. e-commerce (EC) is buying and selling goods and services over the internet (B2B between companies, B2C between a company and consumers, etc.). Fintech is a coined term combining finance and technology, referring to the family of services that use IT to reform mobile payments, money transfer, asset management, and the like.
- JIT (Just-In-Time) is a production-management approach that produces or procures only what is needed, when it is needed, in the amount needed. It aims to eliminate waste from excess inventory and work-in-process, raising production efficiency (the kanban system is a representative means of implementing it).
The staples: machine learning automatically learns regularities; deep learning uses multi-layer neural networks and also automatically acquires features; CNN is a deep-learning method with particular strength in image recognition; generative AI generates new content; POS collects point-of-sale performance data, while EDI electronically exchanges inter-company transaction data; JIT means only what is needed, when needed, in the amount needed. The difference between disruptive and sustaining innovation, and the meaning of each stage in MOT's "devil's river, valley of death, Darwin's sea," are also recurring points.
Consider parts maker C examining automation of its visual-inspection process. Skilled workers had inspected products for scratches and defects by eye, but labor shortages and inconsistent inspection accuracy were problems. C's technology department, from an MOT standpoint, considers how to commercialize image-processing research results sitting dormant in-house, and first draws up a technology roadmap laying out a timeline: "year one, a proof-of-concept inspection AI; year two, full rollout to the production line." For the technology, it chooses a deep learning model using a CNN, which has particular strength in progressively extracting local features (the shape and pattern of a scratch) from the appearance image. It judged this superior in accuracy to plain machine learning (judgment based on features a human predefines in advance), because a CNN can automatically learn scratch patterns from data. In parallel, it embeds IoT sensors into each inspection device, collecting inspection results and operating status in real time and aggregating them to the cloud. Introducing this automated inspection falls within the scope of sustaining innovation, progressively improving an existing inspection process, but C is also eyeing future use of generative AI to accumulate and analyze inspection data and automatically generate reports on the root cause of defects. On the production-management side, parts that pass inspection are supplied to the next process under JIT thinking—just enough, no more, no less—preventing work-in-process from piling up. It also switches order and delivery data exchange with supplier manufacturers from paper slips to EDI, and grasps parts-sales performance at stores in real time via POS, further improving the accuracy of demand forecasting.
| Term | Nature | Strength/characteristic |
|---|---|---|
| Machine learning | Automatically learns regularities from data | Features are often human-designed |
| Deep learning | Multi-layer neural network | Also automatically acquires features |
| CNN | A deep-learning method | Strong at image recognition (convolutional layers) |
| Generative AI | Generation of new content | Outputs text/images/code, etc. |
Trap: "deep learning is a method where a human designs and supplies the features in advance" is wrong—that describes an approach common in traditional machine learning; the hallmark of deep learning is that it also automatically acquires features from data. Also, "POS and EDI are both mechanisms for electronically exchanging inter-company transaction data" is wrong—POS collects one's own point-of-sale data, while EDI exchanges transaction data between companies: different scope. Furthermore, "disruptive innovation means progressively improving the performance of existing products" is wrong—that describes sustaining innovation; disruptive innovation refers to change that replaces existing markets and products.
6.3.4Section summary
- MOT = management connecting technology to business results. Distinguish innovation: sustaining (improves existing) vs. disruptive (replaces the market/product). Technology roadmap = a time-axis plan for deploying technology
- Machine learning = automatically learns regularities; deep learning = multi-layer NN that also auto-acquires features; CNN = a deep-learning method strong at image recognition; generative AI = generates new content
- POS = collects one's own point-of-sale data; EDI = electronic exchange of inter-company transaction data. JIT = only what is needed, when needed, in the amount needed
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Quick check
(just a quick review)Q1. You want to develop an AI model that automatically detects scratches and defects from a product's appearance image. Rather than having a human predefine the features, you want the model to progressively and automatically extract local features from a large volume of image data. Which technology is most appropriate?
Q2. Consider two situations: one where a company keeps improving an existing product's performance little by little each year, and another where an entirely new technology replaces existing markets and products altogether. Which term describes the latter?
Q3. A retailer wants to introduce two mechanisms: one that reads a barcode at the register to record and aggregate point-of-sale performance data in real time, and another that electronically exchanges purchase orders and invoices with a supplier manufacturer in a standardized format. Which pair correctly matches these two mechanisms?
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