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Enhancing Operational Efficiency: Applying Operations Management Theories in Data Analytics and Research
Enhancing Operational Efficiency: Applying Operations Management Theories in Data Analytics and Research
In the dynamic landscape of data analytics and research, integrating operations management theories can be a powerful strategy to enhance efficiency. Traditionally associated with manufacturing, these theories find new relevance in optimising processes at organisations like Decimal Point Analytics (DPA) where the emphasis is on delivering enhanced value to customers through streamlined operations.
We focus on identifying and using optimal resources to deliver our customer requirements at the highest possible standards. When a US-based banking consultancy firm approached us to streamline their operations, process data analytics helped them apply statistical methods and automation to great effect – they achieved 80% efficiency in many of their processes.
At DPA, we are able to do this consistently for our clients by regularly assimilating learnings from various operations management theories into our processes. Assimilation starts with the clarity that none of these theories can be implemented in isolation. The results of implementing these learnings also depend on how well we understand the problem to be solved and identify the correct tools to solve it.
In this article, we discuss how operations management theories find practical application in data analytics and research.
As the name suggests, BPR calls for a radical redesign of business processes to achieve dramatic improvements in performance. Often used in conjunction with data analytics, the objective of redesigning the process here is to help operations managers root out inefficiencies and bottlenecks, eliminate non-value-added activities, and reduce costs involved in production.
Analysing data related to the resource utilised at each step will lead process managers identify process inefficiencies as well as arrive at insights required to make informed decisions on how the inefficiencies can be addressed and improved. Similarly, data analytics can shed light on bottlenecks, areas of resource waste and deviation.
We employ tools like management information software (MIS) and timesheets to identify changes in resource use and include this information in our communication with clients informing them of these changes.
Sigma is unequalled in its ability to systematically remove defects from a process by identifying and correcting the root-causes. This data-driven methodology for process improvement works for various industries and sectors, offering a vast range of techniques for data analytics and research.
The Six Sigma methodology is implemented in five phases as described below:
Definition: This entails stating the problem, output to be improved, as well as the customers, and process associated with the problem.
Measurement: Data gathered from the process is used to establish a baseline for improvements to be made.
Analysis: Data is analysed to find the root-causes of defects.
Improvement: This includes development, testing, and implementation of solutions.
Process controls: These are implemented to ensure the improvements made are sustainable.
At DPA, we use a combination of statistical tools and manual reviews to pinpoint and record deviations from the normal process. As part of our process control measures, we maintain corrective action and preventive action (CAPA) logs for each instance of a defect or error. Each deviation, error or defect is meticulously recorded and analysed to identify the root-cause of the unexpected event.
The findings from the analysis are used to guide process improvements preventing the recurrence of these errors. As an additional step, the operations team discusses possible risks in the future and formulates process quality control measures to mitigate them.
As a well-known management philosophy and methodology, TOC focuses on identifying and unblocking process constraints to optimise overall performance and achieve the organisation’s strategic goals.
In the context of data analytics and financial research processes, TOC is implemented to unearth the most important constraint or bottleneck and systematically improve it until it is no longer a limiting factor. This opens up pathways for the organisation to exploit the constraint and achieve financial goals, deliver on-time-in-full to customers, reduce lead time, and more.
Key steps for implementing TOC:
Identify the constraint: This could be the major pain point obstructing the data analytics or financial research process.
Exploit the constraint: Doing so will help the organisation make the most of existing capacity and resources.
Subordinate everything else: The constraint takes precedence over all other activities to ensure they do not hinder its performance.
Elevate the constraint: This involves increasing its capacity and resources.
Repeat the process: Once the initial constraint has been improved, the next constraint should be identified and addressed.
For instance, if the constraint is identified as the time taken to gather and analyse data, the team can focus on exploiting this constraint by streamlining data collection processes, with technological interventions.
At DPA, any new project begins with an intense documentation exercise, that includes process mapping and meticulous recording of steps along with other process documents. The process map and list of steps are further used to identify any process limiting factors and in turn, automation opportunities. The analysis of data related to time spent on various activities, the MIS, ongoing work at various stages, and employee productivity provides a wide spectrum of inputs that guide the team on the next steps in their process optimisation journey.
Implementing TOC can bring about a host of benefits including reduced lead time, better control over operations, and achieving financial goals while delivering value to customers.
The way we bring together data analytics and operations management theories at DPA demonstrates a holistic and pragmatic approach to process enhancement. By understanding the interplay between these theories and implementing them collectively, you can anticipate substantial efficiency gains, while delivering quality data with greater efficiency to your customers.
Each of the methodologies that we have discussed here are, in fact, making significant impact to our own processes and operations at DPA. With our expertise in data and advanced statistics supplemented by a substantial understanding of the financial markets and continued support, you can be assured of achieving superior operations management practices upheld by robust theoretical frameworks.
