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SAP Leonardo IoT and Industry Revolution 4.0

SAP Leonardo IoT and Industry Revolution 4.0

Revolution is inevitable, transformation happens in almost every sector, for example, mechanical industries, finance, technology, medical science, rocket science, and many more. But it creates a huge impact on how the world or the system functions. Some of them are easy to adopt and many of them take their own time to implement. This article aims to address the industrial revolution 4.0 and how SAP IIoT (Industrial Internet of Things) plays an important role. In addition to that this article describes how SAP is adopting the change and supporting organizations to migrate to the new industry normal. This article also explains the use cases in IIoT.

I. INTRODUCTION

INDUSTRY 4.0 is the digital transformation of discrete manufacturing and process industries. In other words, It’s an automation of manufacturing process, optimization, and streamlining of the business process using intelligent digital technology.

The industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers’ industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency as well as other economic benefits [1].

The IIoT is an evolution of a distributed control system (DCS) that allows for a higher degree of automation by using cloud computing to refine and optimize the process controls.

SAP Leonardo IoT enables the Intelligent Enterprise by creating live insights from connected things and business processes to deliver business outcomes. It helps the industry to transform from their traditional manual ERP solution to companies combining IoT with rapidly-advancing Artificial Intelligence (AI) technologies to make well-informed decisions with little or no human intervention.

What is Industrial revolution 4.0?

This section will cover all the four industrial revolutions:

First: Industrial Revolution 1.0

Transformed from hand production to mechanical production facilities with the help of water and steam power.

Second: Industrial Revolution 2.0

Transformed from stream-operated machines to electrical mass production.

Figure 1.1 Industry Revolution 4.0

Third Industrial Revolution 3.0

This is known as the digital revolution that involved electronic and computer software applications to further automate the production process.

Fourth Industrial Revolution 4.0

The era of machine to machine communication with the help of the Internet of Things and ML/AI.

II. FUNDAMENTALS OF IoT

Let’s start with the definition from Wikipedia. “The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals, or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction” [2].

IoT is structured with three important components. Let’s deep dive into each of the components before we get into real-life examples.

  1. Sensors
  2. Edge services
  3. Cloud Computing

Figure 2.1 Fundamentals of IoT

1. SENSORS

A sensor is a small device that detects the state of physical devices and transmits the information as digital signals to an edge or cloud solution that ingests and analyzes the data.

Physical devices cannot sense their status without sensors. IoT enables machines or devices to communicate with each other with the help of sensors.

Real-life examples:

  • Thermostats that measure humidity, temperature
  • Apple watch which detects motion and noise
  • Health fitness devices that detect movement, and position
  • Chemical sensors which measure contamination levels
  • Vision sensors recognizing light, colors, and even complex patterns like defects in materials.

2. EDGE COMPUTING

In IoT edge, computing stays closer to the IoT data source (at a physical location) to process, store, and analyze sensor data. Thus, edge computing helps in faster decision-making.

The need for edge computing was felt due to the exponential growth of IoT devices. Edge computing receives the information from IoT devices and transfers the data to the cloud.

Billions of devices are generating data every day and data is growing exponentially. Edge network reduces the load on the cloud by managing data locally and avoids transmitting all the raw data from local sources to the cloud. This is why edge processing and pre-processing of sensor data play such an important role in IoT today.

At times, there is a need for storing the data locally and temporarily for data processing to avoid intermittent network issues. Some of the best examples for edge computing are:

  • In transmitting big data from vessels on the high seas, the challenge is often about bandwidth. Same with offshore oil rigs, retail stores.
  • In manufacturing, we have shop floors equipped with many different sensors monitoring and controlling the production process.

3. CLOUD COMPUTING

Cloud computing is on-demand computing services such as storage, remote servers, networking, data analytics, ready to use ML/AI algorithms over the Internet. It reduces the cost of owning own software and hardware and maintenance cost such as electricity, cooling devices etc. With the cloud, it is easy to scale up or scale down the storage based on the organization’s needs.

III. INTELLIGENT ENTERPRISE WITH SAP IOT

Traditionally, IoT is seen as a network of physical devices and the focus is set very much on how they connect and exchange data. But at SAP, we see much more in it.

We see IoT as a key enabler of the Intelligent Enterprise where data enables intelligence and feeds process automation and innovation. Most enterprises have systems that capture operational data about customer transactions, procurement, vendor contracts, manufacturing and spending, and human resource management.

Reports and dashboards provide insights and help to predict what will happen next.

But in order to influence what happens next, companies need more insights into interactions that people have with their products, their employees, their business, and their brand.

Figure 3.1 SAP IoT and Intelligent ERP Suite

In order to connect the dots and understand the interdependencies, a business needs an experienced platform that captures it all in one place. SAP Intelligent Enterprises collect insights from customers, employees, products, and brands at every touchpoint.

Qualtrics is SAP’s solution to optimize customer, employee, product, and brand experience.

The SAP intelligent suite provides a suite of applications to run day-to-day business operations like manufacturing, supply chain, finance, logistics etc.

The SAP intelligent technologies’ platform includes data-driven insights, intelligent robotic process automation (RPA), artificial intelligence, and IoT cloud and edge.

IV. SAP LEONARDO IoT

The SAP Leonardo Internet of Things (IoT) services represent a network of physical objects known as the things that are used to collect and exchange data [3]. It helps to set up a thing model, for companies, and service personnel in a company and their relationships. It also provides a collection of REST-based as well as OData-based services to store and retrieve data efficiently for a thing, company, and service personnel.

SAP Leonardo IoT enables the Intelligent Enterprise by creating live insights from connected things and business processes to deliver business outcomes.

Figure 4.1 SAP Leonardo IoT: System Architecture [3]

  1. Devices can be connected directly to SAP could platform IoT Services or it can be connected through other cloud service providers such as Azure IoT Hub, AWS IoT Core etc.
  2. Devices can be connected to SAP Edge Services. It sends the data that is really important to SAP Leonardo IoT Cloud.
  3. SAP Leonardo IoT business services, which are needed to build an IoT application. These main business services include thing core services, data ingestion services, big data storage, and data processing services like rules, events, and actions.
  4. SAP platform also provides the analytics capability.
  5. SAP Leonardo IoT offers tools such as the Thing Modeler to create a thing model that is the digital representation of the device and its sensors. With the SAP Web IDE tool, templates specific to SAP Leonardo IoT enable you to create new SAP Fiori apps from scratch based on IoT.
  6. SAP Cloud Platform Integration iFlows establish the connection between SAP Leonardo IoT and backend systems.
  7. Finally, SAP, customer, or partner applications use SAP Leonardo IoT’s APIs to build new applications.

V. USE CASE

USE CASE 1. AUTOMATIC INVENTORY REPLENISHMENT

Fruits Inc. is one of the leading producers of fruit preparations for dairy, bakery, and confectionary products (business-to-business [B2B]). Fruits Inc.’s suppliers provide raw materials, such as sugar to Fruits Inc.’s production plants where they are stored in silos with IoT-enabled sensors. The company has the following pain points:

  1. Inaccurate fill level measurements
  2. No real-time visibility into consumption
  3. Manual order for replenishment
  4. Product quality/spoilage

Let’s take a look at the example how SAP can be integrated with IoT sensors and deliver the auto-replenishment in Silo.

Figure 5.1 Automatic Replenishment business flow [4]

  1. Sensors attached to receptacles that measure real-time fill-level, material consumption rate, temperature.
  2. Fill-level sensors in silos usually measure the distance from the sensor to the surface of the material inside the silo to determine the reorder level.
  3. Sensors send the fill level, temperature, and also the humidity information to SAP Leonardo IoT.
  4. In SAP Leonardo IoT, the sensor data is then enriched with business context from the ERP system, such as the material type, the respective plant, and the storage location.
  5. An automated integration from SAP Leonardo IoT to the ERP system triggers the automatic purchase requisition to the relevant supplier based on the business rules defined in SAP ERP application.
  6. Business rules in Leonardo IoT send alerts and notifications if any critical situation occurs. So, for example, a low fill level, a high temperature, or the humidity being too high, which could spoil the material.

Figure 5.2 SAP S/4 HANA with IoT integration system flow

The process steps involved in extending the replenishment process in SAP S/4HANA by integrating with IoT are as follows [5]:

  1. Silos are equipped with relevant sensors.
  2. To collect data from the sensors in SAP Leonardo IoT, the first step is to create a thing model and onboard the silos. Onboarding creates a thing instance—known as a digital twin—for each silo. The thing model consists of several PSTs, each one containing a set of properties. These properties can be basic data, such as serial number and the type and capacity of the silo, or measured data properties, such as fill level, temperature, and latitude and longitude.
  3. Streaming rules can check during data ingestion to determine whether incoming sensor values violate predefined thresholds – Streaming rule to check the fill level (rule condition: fill level < 30)
  4. Events triggered by rules can trigger the following different types of IoT actions:
    1. In the first example, the streaming rule checking for low fill levels triggers an IoT action, which uses an API to call SAP Cloud Platform Integration to create a purchase requisition in SAP S/4HANA to replenish the material running short in the SAP Leonardo IoT is shipped with payload templates and iFlows to simplify this configuration for companies.
    2. In the second example, the same rule triggers a second IoT action, which creates an in-app notification alert of a low fill level in one of the onboarded silos. Its text contains as variables the name of the affected silo and the current fill level in tons.
  5. SAP Leonardo IoT includes SAP Web IDE with IoT application templates. A wizard- based approach allows for code-free IoT application development. As the source code is available, it can be customized beyond the shipped templates via custom coding.

Table 5.1 Use cases for IoT monitoring and Supply Chain

USE CAE 2. IoT BASED CYCLE COUNTING

What is cycle counting?

Cycle counting is a method of auditing physical inventory stored in a plant or warehouse location. Inventories are counted several times in a fiscal year based on the product category, cost of the item, and its nature. Fast-moving items are counted more frequently compared to slow-moving items.

Cycle counting helps to maintain the right inventory levels and avoid the balance sheet errors at the end of the quarter-end or year-end activities. Cycle counting is very tedious and consumes lot of time to count the items one by one. There are cases where companies stop production to avoid material consumption at the shop floor while counting in progress. It impacts the production throughput.

Companies can introduce the sensors at the warehouse on storage bins which will provide the real-time inventory situation. This is achieved even in complex business environments. For example, Vision sensors recognizing light, colors, and even complex patterns like defects in materials.

Real-life example – Samsung refrigerator with media Hub comes with a sensor which adds to the grocery shopping list as soon as the item is consumed.

USE CASE 3: REALTIME LOGISTICS

Logistics plays an important role in retail, pharma industries, discrete manufacturing in order to obtain highest service level to meet the customer demand.

Introducing IoT helps in on time supply, and avoids product wastage (for example, perishables in retail sector). It also holds the importance of maintaining the product condition in pharma industry.

Figure 5.3 Outbound Shipment Truck with Telematics Device

  • Shipment delivery adherence by shipment by transporter, by type of trucks, by material, by route
  • Transportation cost by customer, by shipment, by route, by material
  • Product condition issues by transporter, by route, by material
  • Predict delivery time to give dealer/customer near accurate time of shipment arrival
  • Correlate late arrival with transporter, truck, route, weather data, and route terrain data to initiate proactive corrective measures
  • Change delivery document status
  • Change transportation order status
  • Arrange new truck in case of breakdown
  • Optimize route and shipment release time for in-time delivery
  • Truck idle time alert
  • Storage condition deviation alerts
  • Real-time estimated time of arrival
  • Truck breakdown alerts
  • Route deviation alert

Some of the challenges that companies face in enterprise IoT are as follows:

  • Getting the business ready for Industry 4.0
  • Enhancing the Line of Business processes with operational data from IoT
  • Making sense out of IoT data
  • Semantically integrating sensors and devices from different vendors
  • Integrating existing IoT data into their business processes
  • Using IoT data to develop their own IoT-enabled business applications
  • Driving business decisions based on real-time operational data from sensors
  • Translating sensor values to insights and enabling business outcomes
REFERENCES
[1] Boyes, Hugh; Hallaq, Bil; Cunningham, Joe; Watson, Tim (October 2018). "The industrial internet of things (IIoT): An analysis framework". Computers in Industry. 101: 1–12. doi:10.1016/j.compind.2018.04.015. ISSN 0166-3615.
[2] https://en.wikipedia.org/wiki/Internet_of_things
[3] https://help.sap.com/viewer/fffd6ca18e374c2e80688dab5c31527f/1912a/en-US
[4] Figure 5.1 https://open.sap.com/courses/iot4/items/kr6VJXLL9MTr2Hal1x93Q
[5] Use Case 5 - Authors: Sijesh Manohar, PVN Pavan Kumar, Shyam Ravindranathan, use cases: SAP Leonardo Internet of Things in “Internet of Things with SAP Implementation and Development”,1st edition pp 415 Rheinwerk Publishing · Boston, MA
About Author:

Muralikrishnan Panneerselvam received Master of Computer Applications in 2006 from Anna University, India, Chennai. He is currently pursuing job as Senior Manager in Supply Chain Management. His research interest in IoT, ML/AI in supply Chain Management, Digital Transformation

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