Digital transformation differs between sectors. For banks and media companies, it involves managing digital assets such as bank accounts, videos, and films, without the need to produce physical goods. In contrast, industrial sectors like mining, water, oil, and food and beverage must integrate digital processes while still producing tangible products to meet customer demands.

It’s not surprising that companies managing digital assets were among the first to be disrupted in the digital era. Netflix revolutionized the media market, and now media companies face a completely different kind of competition than they did 30 years ago. Consider how banks manage customers; they’ve transitioned from a physical presence to an app-based presence.

When examining industrial companies, it becomes apparent that initiating a digital transformation journey is challenging due to the presence of physical assets that produce goods. Whether it’s a company manufacturing producing hazardous chemicals, food products for consumption, or when water is transported for over hundreds of kilometers, industrial companies operate critical assets that are managed daily to deliver products to their customers.

This means that the goods produced must meet quality standards, the production process is overseen by safety systems, and pharmaceuticals are manufactured in accordance with strict Standard Operating Procedures (SOPs).

Digital Transformation for industrial companies

I will discuss the architectural requirements for industrial companies in a functional manner, detailing platforms to describe each layer namely;

• Layers or platforms
  • Control layer
  • Operational layer
  • Business layer
  • Knowledge management
  • Integration
  • User interface

This is continuation of the article “digital transformation: your key to success” section “Leveraging technology to drive Digital Transformation”

Control layer:

The control layer represent the assets, machines, the physical world. For example, in a food manufacturing company, various machines control the production process, such as weighing scales, mixers, boilers, dryers, and baking machines, to create products for customers. These machines are managed by automation systems like PLC or DCS systems, which may operate in isolation or in a networked manner. These control systems not only manage and regulate the physical assets but also generate a significant amount of data that can be archived in specialized data historians or control systems.

In summary, this layer controls the physical assets to ensure products are consistently produced with safe and high quality. Overcoming this will be one of the challenges in initiating the digital transformation journey. For instance, operations often rely on isolated or manual systems, such as paper or Excel. This indicates that digital transformation will pose challenges beyond just the technical aspects. It will be crucial to begin by communicating with the people involved and initiating change management, thereby integrating them into the digital transformation process.

Technically, the systems should be accessible in a secure way with the assets performing to specifications. Therefore, the initial step is to comprehend the current state and the potential impact of these changes to start with your digital transformation

To summarise;

The control layer is all about automation and managing existing control systems controlling assets. It is crucial for ensuring the safety, reliability, scalability, and adaptability of operations. Key aspects include:

• Automated control systems: these systems handle real-time control of processes, machinery, and equipment.
• Safety measures: ensuring safe operation by monitoring and responding to anomalies of the assets.
• Efficiency optimization: fine-tuning control parameters for optimal performance.
• Change management: facilitating updates and modifications to control logic.

At the control layer, assets are organized in a “disconnected” manner. They produce according to specifications, but lack an overarching view of the activities in other parts of production. To address this, an operational layer is required where both control and operational information are integrated into a single platform, referred to as the operational platform. The requirement for your digital transformation.

The operational layer

When examining the control layer, we find that we lack the comprehensive information necessary to consistently make optimal decisions due to a missing overall perspective.

Exploring the transformation and optimization possibilities becomes possible when the information from the control layer or assets is fully accessible at the operational level. The data from the assets at the operational level is contextual, trustworthy, reliable, and available to operators, planners, maintenance staff, supply chain, finance, and others. We will delve deeper into this in the “knowledge” section. The

The opportunity we have now is that best decision can be made since everybody has access to the same information and it provide us a way to send back relevant information to the control layer.

To summarise;

In the operational layer, operational intervention takes place, and while operational information is accessible, asset control is not; this occurs at the control layer. The importance of this layer is in carrying out operations effectively, providing scope for enhancement, agility, and adaptability. Key aspects include:

• Human-aided operations: people operate the operation with the assistance of smart assistants.
• Process execution: ensuring smooth execution of operational tasks.
• Adaptability: responding to dynamic conditions and unforeseen events.
• Performance monitoring: tracking operational KPIs.
• Two dimensional: ; it receives data and it instruct actions to the control layer

The operational platform operates at the level where operational decisions are made, understanding the control level holistically and the business constraints and operational efficiencies to achieve digital transformation. This is the domain where assets are managed and operated, both by systems and personnel, enhanced by intelligent assistants that augment human intervention. The platform is vital for operational management, ensuring assets are used both effectively and efficiently.

Developing an operational platform offers the chance to detach from the physical world while still accessing the asset information. It opens up possibilities for simulating operations and guiding the control layer or assets with optimal settings.

The business layer

In the business layer, we consolidate business information and digitize the decision-making process using workflows, among other methods. For instance, when a customer demand is received via email, an intelligent agent deciphers the demand and converts it into a production order after verifying the contracts. The planning is optimized for the most efficient operation, running scenarios to enhance energy usage, understanding the financial implications of various options, and connecting with different warehouses to explore inventory optimization opportunities, and so forth.

After this stage, the production order details are sent to the operational layer, and any deviations from the plan are communicated back to the business layer to execute new scenarios. This ensures that the control, operational, and business layers are in continuous, two-way digital communication.

The scenario described does not address the role of people in the digital value chain. However, people are essential in the decision-making process due to their expertise in creative thinking and solving complex problems.

While digitization and automation is ideal for repetitive tasks, it falls short in addressing exceptions, one-time issues, and complex inquiries. In these instances, human intervention is necessary.

To summarise;

The business management layer focuses on delivering customer value propositions and integrating operations to balance commercial, operational, and control requirements.

• Customer value: aligning operations with customer needs and market demands.
• Integration: bridging the gap between operational efficiency and business goals.
• Profitability: maximizing revenue while minimizing costs.
• Strategic decision-making: using data-driven insights for business planning.

Customer value propositions are the goal, ensuring the integration of operations across commercial, operational, and control requirements. The primary objective is to automate processes to the extent that human intervention is necessary by exception. This layer is pivotal for aligning the company’s operations with its strategic goals, particularly in delivering value to customers as defined by the company’s value proposition.

The knowledge layer

Before introducing the knowledge layer, we must revisit the origin point where an event occurs. I will examine this within an industrial context and outline how an event progresses through the hierarchy to generate actionable insights, thereby enhancing business value at each stage. A good read is the article

Event;

An event is an occurrence or change within an environment that potentially impacts operations. For simplicity, we will use an example of a sensor at the control level.

Data;

The sensor measures operational states such as temperature, pressure, speed, RPMs, etc., and is captured by the data infrastructure. This raw, unprocessed data records events with details like timestamps and numbers.

Data alone is often too detailed and lacks context, rendering it of limited use for decision-making. Hence, when organizations claim to possess a lot of data, it is still not fully utilisable.

Information;

When context is applied to data, it becomes information. Effective information can answer basic questions like who, what, where, and when. We’ve now transformed a number into a meaningful metric, such as representing speed in km/hr from a specific machine.

Knowledge;

This is where the transformation occurs, as we capture knowledge from information. This involves recognizing patterns and relationships to create business value, such as predicting machine failures, determining optimal process settings by combining process and energy data, or identifying new process improvement methods. Amazon is among the companies that have successfully implemented a knowledge layer as part of their digital transformation journey, enabling them to continuously enhance their processes and offerings, which is quite impactful.

Wisdom;

Instead of wisdom, I prefer to use “action” instead. With the created patterns, the best insights are converted into optimal actions, steered by the acquired knowledge. The more refined, the more trustworthy the knowledge, the more precise the actions.

In summary, as we transition from an event to an action, the business value escalates from basic data collection to strategic, proactive management. Each phase contributes an additional layer of interpretation and utility, transforming raw data into actionable insights.

The knowledge management layer collects insights from real-time data spanning across various processes, including control, operation, financial and commercial aspects. Its importance is rooted in enabling informed decision-making and fostering ongoing improvement. The primary components include:

1. Operational knowledge mining: extracting insights from historical and real-time data.
2. Business process mining: analysing workflows and identifying bottlenecks.
3. Success metrics: measuring performance against predefined goals.
4. Predictive analytics: Anticipating future trends and challenges.

The user interface

In the real world, different personas consume the available information from the various layers. For example, a manager role is to manage the assets and therefore will need specific information from certain layers to make informed decisions.

In summary;

The user interface acts as a channel for interaction between different personas and systems, ensuring smooth integration across all underlying layers. Its functions include:

1. User experience: providing an intuitive interface for persona’s to interact with the system.
2. Real-time responses: facilitating agile decision-making based on data, information or knowledge.
3. Data visualisation: making complex information accessible and understandable.
4. Interoperability: Ensuring different platforms communicate effectively between each other.

The user interface act as the point of interaction between the system and its users. It is critical for providing an accessible and intuitive means for users to engage with the system, monitor operations, and enter commands.

In summary, this functional architecture creates a solid foundation for agility in digital transformation. By harmonizing these platforms, companies can adapt swiftly to changing environments, optimize operations, and deliver value to customers.

Outcome: agile digital transformation architecture

To start your digital transformation, it’s essential to dismantle silos and adopt platform thinking to foster an agile architecture.

Such an architecture facilitates quick adaptation to change, promotes ongoing enhancement, and empowers the company to rapidly meet market demands and technological progress. Creatin and integrating these layers or platforms ensures that the company’s operations are in sync with its customer value proposition, which is vital for sustaining a competitive advantage.

In essence, each layer of the functional architecture enhances the company’s overall agility and efficiency. Knowledge management yields insights for strategic decision-making, business management aligns operations with customer value, operational management optimizes asset use, and control management delivers the automation and oversight needed for safe and proficient operations. Collectively, these platforms establish a foundation for an agile architecture that propels a company forward in the digital transformation journey.