Practical ML to raise efficiency of businesses

Recent advancements in the area of artificial intelligence and machine learning have provided a foundation for new technologies, including robotic process automation, natural language processing, computer vision and reinforcement learning. In turn, these developments in artificial intelligence field and advancements in computer science have affected how organizations approach, design and execute business processes.

Businesses have potential to utilize machine learning and deep learning to forecast business processes for making decisions at a runtime. Overall, organizations can also rely on data for the identification, discovery, analysis, improvement, implementation, monitoring, and controlling of business processes in business process management (BPM).

Big Data and Analytics

A convergence of breakthrough technologies in big data and data analytics provide a critical solution to meet business objectives. Big data and advanced analytics can play a key role in raising productivity of knowledge-intensive tasks, maximizing company’s assets, and facilitating personalized digital experience.

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In 2013 report “Game changers: Five opportunities for US growth and renewal” McKinsey Global Institute has predicted big data analytics to contribute $325 billion to the US GDP by 2020.

Sectors across the economy can find new efficiencies by harnessing the deluge of data being generated by transactions, medical and legal records, videos, and social technologies—not to mention the ubiquitous network of sensors, cameras, bar codes, and transmitters embedded in the world around us. Thanks to advances in cloud computing and the development of software that can extract useful information, this sea of data can now be transformed into insights that create both efficiencies and innovative services.

McKinsey Global Institute (2013)

Examples

New York City–based Taboola is one of the world’s leading discovery platforms. It helps more than 1.4 billion monthly users explore what’s interesting and new across the open web, generating approximately 30 billion personalized content recommendations every day. When its on-premises solution required additional capacity and ongoing management, Taboola moved to Microsoft Azure Data Explorer to power its next waves of growth. Within a week of implementation, Taboola was able to transform billions of raw data logs into actionable, quality insights at unprecedented speed and scale.

Microsoft Customers Stories (2020)

Founded in 2011, Dodo Pizza is the largest pizza franchise in Russia and one of Europe’s fastest-growing restaurant chains. As a technology-driven business aiming to be a front- runner of tomorrow’s digital society, Dodo uses a cloud-based system known as Dodo IS to collect and process massive volumes of operations data and report real-time business analytics in every location. As a result, its franchises are highly efficient, reactive, intelligent entities, with kitchen and delivery staff able to offer the standout customer experience that has set the company on its nine-year growth trajectory.

Microsoft Customers Stories (2020)

Business Process Management

The Business Process Management (BPM) is the science and practice of overseeing work to ensure consistent outcomes and to leverage opportunities for process improvement. BPM activities are commonly organized along lifecycle phases: identification, discovery, analysis, improvement, implementation, monitoring, and controlling.

Data-driven approach to analysis, monitoring, and controlling phases helps us to analyze and monitor running processes. Business leaders and management can determine how well they are performing on core objectives and performance metrics. Furthermore, collected data and estimations serve as vital inputs for business architecture and pipelines during the redesign phase.

Data-driven approaches can make use of data not only for process discovery or analysis, but also in monitoring to gain predictive insights. Companies are willing to shift their resources from design phases (discovery, analysis, and improvement), where data is exploited in offline mode, to runtime phases such as monitoring, where data is used in real-time to forecast process behavior, performance, and outcomes. In general, process outcomes reflect the quality of a result delivered to actors involved in a process.

Explainable AI

Predictive process monitoring at runtime is especially growing in importance. Predicting the remaining cycle time, compliance, sequence of process activities, the final or partial outcome, or the prioritization of processes helps organizations to make decisions and gain valuable insights in a rapidly evolving environment.

Heuristics

Emerging technologies affect how organizations execute and coordinate tasks internally as well as dealing with an end client. In general, we can see a new technology’s notable impact from the very beginning of the lifecycle. Well-documented heuristics might assist with re-configuring the design of the system.

Many of the available heuristics explicitly refer to information technology as means to achieve process improvements. For instance, the task automation heuristic suggests that one should take an existing task and subject it to automation. In its best implementation this heuristic ideally produces a faster and cheaper execution of the task.

Besides, the interfacing heuristic constitutes the idea that organizations can use standardized interfaces to integrate their operations with information systems of partners and clients in order to make processes more accessible, intelligible and reliable. A standardized interface has potential to result in fewer errors, faster processing and lower costs due to less rework.

Finally, the task composition heuristic refers to combining small tasks into composite tasks and dividing large ones into smaller versions. Solving for multiple tasks at a time should decrease the setup time. Meanwhile, reducing large tasks to smaller ones should raise the quality and flexibility.

References

  1. Kratsch, W., Manderscheid, J., Röglinger, M. and Seyfried, J., 2020. Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction. Business & Information Systems Engineering, 63(3), pp.261-276.
  2. Microsoft Customers Stories. 2020. Taboola harnesses big data to power the future of online advertising. [online] Available at: https://customers.microsoft.com/en-us/story/837528-taboola-entertainment-azure-en-united-states [Accessed 27 July 2021].
  3. Microsoft Customers Stories. 2020. Data-driven Dodo Pizza raises performance with Azure Data Explorer. [online] Available at: https://customers.microsoft.com/en-us/story/851838-dodo-pizza-consumer-goods-azure-en-russia [Accessed 27 July 2021].
  4. McKinsey Global Institute, 2013. Game changers: five opportunities for US growth and renewal. https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Americas/US%20game%20changers/MGI_US_game_changers_Executive_Summary_July_2013.ashx.
  5. Reijers, H., 2003. Design and control of workflow processes. Berlin: Springer.

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