Regardless of who you talk to, everybody agrees that the IoT will be the next big “thing” (no pun intended). The excitement for IoT is supported by:
- ever shrinking hardware
- ubiquitous connectivity
- rich IoT application platforms
- an increasing number of IoT use cases and industry applications
- the economic value that big data hold
Several of these IoT scenarios are very end-user oriented, e.g. driven by crowd funding and the Maker Movement. Examples include the Pebble smart watch, the innovative Tile product (a “Thing” locator) or the smart Hue light bulbs. However, many IoT examples are more focused on industrial applications, including fleet management, telematics, smart metering and smart grids, telehealth, and so on. In this post, I will introduce two use cases from retail & logistics and the industrial area with a particular focus on how big data can be leveraged.
IoT use case 1: Retail & logistics
Retail & logistics is a key area where IoT is expected to have a huge impact as an enabling technology. RFID (Radio Frequency Identification) has been used successfully in logistics to track containers, pallets and crates for some time now, primarily in closed loop systems and mostly with high-value goods. The massive investments in IoT technologies are promising to help reduce costs for RFID and similar technologies, eventually making the tracking of goods on an item-level a feasible business case. For retailers, this has many advantages, including inventory accuracy, reduction of administrative overhead, automated customer check-out processes and a reliable anti-theft system.
Other emerging technologies are so-called “beacons”. These beacons are indoor positioning systems, which can interact directly with modern smart phones, e.g. using Bluetooth Low Energy (BLE). A network of in-store beacons can identify the location of customers in a store and send them push notifications. For example, a user might create a shopping list on his smart phone and share it with the store app. Upon entering the store, the store app will display a map to the customer, which highlights all the products on his shopping list. Every time the customer gets close to a position where a group of products from his shopping list is located, the app will notify him and make a recommendation for a particular brand. At the check-out point, the system could identify all the products in the shopping cart automatically via RFID, create and confirm an invoice, and use the smart phone to process the payment. The store’s inventory system is automatically updated when the checkout process is complete.
The use of a NoSQL data repository would be of great benefit for storing all kinds of structured, semi-structured and unstructured customer related data, including shopping history and movements through the store. Advanced data analytics algorithms could be used to analyze the customer’s movements and past shopping decisions. This enables the IoT application to generate shopping recommendations that can be pushed to the customer’s smart phone while in the store, or to notify him of special offers – for example if the system detects that the customer is returning to an area in proximity to the store.
IoT use case 2: Industry
Industry 4.0, Smart Factory and Industrial Internet are some of the terms used to describe the social and technological revolution that promises to change the current industrial landscape. There are many examples discussed and explored in this area, from leveraging IoT supply chain optimization to the modularization of production lines with the help of intelligent products.
One interesting example that I explore here is related to the increasing use of hand-held tools in manufacturing, e.g. for the assembly of automobiles, airplanes, trains and ships. In recent years, these tools have become more powerful (e.g. torque) and are now equipped with long lasting batteries, enabling workers to use them without the limitations of power cables or a fixed connection to an air compressor. This greatly enhances flexibility, but also poses certain challenges from a manufacturing process point of view, which can be addressed by leveraging IoT capabilities.
One of the key IoT concepts is the development of intelligent, connected “edge” devices. One example for such an IoT device is a nut runner which is equipped with an on-board computer and wireless connectivity. The on-board computer supports many aspects of the tightening process, from configuration (e.g. which torque to use) to creating a protocol of the work completed (e.g. which torque was actually measured). In addition, the nut runner features a laser scanner for component identification.
By integrating such an intelligent edge device into the IoT, very powerful services can be developed that can help with supply chain optimization and modularizing the production line. For example, these intelligent tightening tools can now be managed by a central asset management application, which provides different services:
- Basic services could include features like helping to actually locate the equipment in a large production facility.
- Geo-fencing concepts can be applied to help ensure only an approved tool with the right specification and configuration can be used on a specific product in a production cell.
The central asset management system can help with optimizing tool maintenance, for example by periodically reading calibration information from the remote tools via the factory WLAN. The asset management application can serve as the bridge between the power tools and the ERP (Enterprise Resource Planning) and MES (Manufacturing Execution System) systems that control the manufacturing process. For example, the asset management system can distribute work orders and configurations to the tools. In addition, the asset management application can document each tightening process by creating inspection lots (e.g. using torque recordings from the tools) and associate them with the BOM (Bill of Material) in the ERP system.
Such a production documentation system can benefit hugely from big data and NoSQL technologies that allow the aggregation of large volumes of heterogeneous, multi-structured data about the production process, including legacy data from many different systems, in addition to images and film recordings from different production modules. In an age where manufacturers can suffer huge costs from large product recalls, this can be a very powerful tool.