This alignment involves the assessment of implementing chosen business strategy through appropriate IT strategy and the articulation of the required I/S infrastructure and processes. In contrast to that strategy execution logic, this perspective is not constrained by the current organization design but instead seeks to identify the best possible IT competencies through appropriate positioning the IT market place as well as identifying the corresponding internal I/S architecture. The strategic management process also defines the changes in the I/S infrastructure that were necessary to execute this technology strategy.
This perspective views the business strategy as the driver. However, involves the articulation an IT strategy support the chosen business strategy and the corresponding specification of the required I/S infrastructure and process. The top management should provide the technology vision to articulate the logic and choices pertaining to IT strategy that would best support the chosen business strategy while the role of the I/S manager should be that of the technology architect-who efficiently and effectively design and implements the required I/S infrastructure that is consistent with the external of the IT strategy.
AI in production: A game changer for manufacturers with heavy assets
Today, artificial intelligence is commonplace. Navigation systems in cars, fitness apps, Alexa and Siri, Amazon, Netflix, weather forecasting, and high-speed stock trading are among current must-have AI applications. Now, even manufacturers with heavy assets, including cement companies, are launching pilot projects to determine if and how AI might benefit their operations.
Traditionally, these manufacturers have financed improvements as capital expenditures. AI offers a less costly alternative by enabling companies to use their existing software to analyze the vast amount of data they routinely collect and, at the same time, customize their results. In doing so, they gain a better understanding of today’s evolving technologies and the value they deliver.
While AI technologies have made tangible improvements to supply chains and administrative functions, they have so far had scant presence in production which is interesting, given that cement plants were early adopters of automation and control systems and have used digitized sensors and signals for decades.
The case for manufacturers with heavy assets to apply AI
For decades, companies have been “digitizing” their plants with distributed and supervisory control systems and, in some cases, advanced process controls.
Operators still rely on their experience, intuition, and judgment. For example, today’s downsized teams of control-room operators are expected to manually monitor a multitude of signals on numerous screens and adjust settings as needed. At the same time, they must troubleshoot and run tests and trials, to name just a few of the tasks that strain the limits of their human capacity. As a result, many operators take shortcuts and prioritize urgent activities that don’t necessarily add value.
This heavy reliance on experience makes it difficult to replace a highly skilled operator at retirement. Since variations in operators’ qualifications can affect not only performance but also profits, AI’s ability to preserve, improve, and standardize knowledge is even more important. With respect to operational improvement and dynamic adaptability, artificial intelligence can outperform conventional decision-support technologies.
Analysis
In response to strong market demand, a cement company had embarked on a throughput upgrade. Hardware upgrades had produced an 8 percent fee-rate gain and installing an equipment vendor’s off-the-shelf advanced process-control solution brought an incremental 2 percent gain. But they wanted to move the needle even further.
For companies with heavy assets that have not kept up with latest advances in analytics and in decision support solutions that apply AI the best possible IT competencies through appropriate positioning in IT market place will be technology transformation alignment perspective. The business strategy of the cement company requires multiskilled project managers and AI creation experts that have technical management and business skills. These have volatile margins and capital market pressures which keep the opportunity cost of not adapting to AI is very high. This requires the development of such an I/S infrastructure that can read, interpret and use the machine generated data to improve performance and address the changing need of customers. Their IT strategy involves defining the key technology scope and the associated critical competencies and committing to a technology alliance. They need to understand the issues in migrating their technology architecture including the need to invest in development of AI base data architecture. This industry has started routine capturing and storing machine data that can be used to create algorithms.
Here in this example of heavy asset firm, business strategy is anchored on following competencies:
1. Captured millions of lines of data from hundreds of process variables.
2. Prepared and analyzed the data using advanced analytics tools and techniques.
3. Mapped the data against automation process flows.
4. Constructed the offline optimizer using design software and applying neural networks and other advanced analytics techniques and algorithms.
5. Created the online optimizer version and connected the asset optimizer to the automation and control system via data interfaces.
6. Went online in autopilot mode after a series of tests and trials, with the asset optimizer operating autonomously without operator intervention.
This example highlights the impact of business strategy on IT strategy and the corresponding implications on I/S infrastructure and processes techniques used to aid executives in the development of the strategy include technology forecasting and the variety of architectural planning approaches. The performance criteria are based on technology leadership often utilizing a benchmarking approach to asses the position of the firm in the IT market place.
Conclusion
How a cement company benefited from AI asset optimizers
The installation significantly improved profits within a few weeks. Performance reviews at four and eight months after installation showed that the AI asset optimizer had consistently outperformed the existing advanced process-control system by significant percentages in both feed rate and specific energy consumption (as shown below). Activating AI boosted asset performance and profit per hour for both the vertical mill and the kiln, while adhering to set-point constraints in a precise and secure manner.
The cement company’s results confirmed that algorithms and models created with advanced analytics techniques can significantly improve the yield, energy consumption, and throughput performance of heavy-asset operations and immediately enhance profit. Specifically, using existing information and software, AI can deliver improvements without capital-intensive equipment upgrades and thus produce attractive returns quickly. In addition, AI generates machine learning that is easily transferred to similar assets and sites, which adds to its appeal as an investment.
Asset-optimizer solutions have been developed and successfully deployed in chemicals, metals, mining, and other heavy-manufacturing environments, demonstrating that AI solutions are viable and economically attractive to a range of companies with heavy assets.
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