by Lio Grealou , senior advisor to QR_ and founder of Xlifecycle Ltd. Origionally written and based on conversations taking place in July 2022.
Image: Processes are contextual; they must be aligned to data, teams, and organizational maturity: on the one hand, the lack of process definition might lead to sub-optimum operations and a perceived lack of control, whereas on the other hand, too constraining processes might hinder creativity and experimentation (image credit: PEXEL)
Effective business processes foster collaboration, data traceability, and outcome consistency, by structuring related activities and interdependencies to maximize quality, minimize cross-functional delivery time, and streamline individual contributors.
When it comes to business process design, re-engineering and operational optimization, there is typically no one-size-fits-all. Process design implies structuring information / data, activities, events, roles, interactions, and decisions, continuously balancing across six perspectives:
1. Organizational perspective: how a business operates, considering either start-up or established operations, and associated operational maturity.
2. Data perspective: what type of information, data lifecycle, functional flows, data volumes, structure complexity, quality acceptance criteria, control level, etc.
3. Collaborative perspective: how teams communicate and exchange data, what decisions they make from it, and when.
4. Outsourcing perspective: how organizations work with suppliers and customers, and how they capitalize on the collective knowledge base.
5. Governance perspective: how cross-functional teams track progress, manage issues, risks, changes, approvals, decisions, etc.
6. Tool and IT platform perspective: how data is stored, controlled, accessed, integrated, and how out-of-the-box processes are implemented and configured per the best practices embedded in the respective systems.
Business process design, re-engineering, and improvement are integral to operational effectiveness and efficiency. Several process mapping and analysis techniques are available, from data flow diagrams to decision analysis, data modelling, business rules analysis, key performance indicators, process modelling, problem tracking, organizational modelling, use case definition, system and functional assessment, etc.
In this article, I elaborate on the value of effective process design and mapping, discussing the QR approach towards value-driven process solutioning and problem solving.
Business process design: where to start
Business process mapping is core to business analysis and continuous improvement. As defined by the International Institute of Business Analysis per their Business Analysis Body of Knowledge (BABoK), a business process represents “a set of defined ad-hoc or sequenced collaborative activities performed in a repeatable fashion by an organization. Processes are triggered by events and may have multiple possible outcomes. A successful outcome of a process will deliver value to one or more stakeholders” (IIBA, 2005).
Product development operations require a combination of formal and informal practices, covering communication, collaboration, learning, people, and data management—tailored throughout the lifecycle of the product and based on organizational maturity. Most core engineering process principles are similar across organizations; 80% of which can be implemented based on experience combined with out-of-the-box platforms and industry best practices, whereas the remaining 20% might require bespoke configuration tailored to the business context.
Value from business process re-engineering
Originally developed by the Business Process Management Initiative, business process modelling notation (BPMN) techniques help with graphical process representation and associated technical documentation based on internationally recognized notation standards. It brings consistency when performing process analysis, definition, improvement, and re-engineering.
Process maturity is a relative measure which links to a capability maturity model (CMM), aiming at addressing conderations such as:
· Is the process fit-for-purpose to support the relevant delivery operations, without hindering the required flexibility?
· How is the process helping (in business benefit terms) teams and individuals make timely decisions?
· How and when is the work completed and documented?
· What is the level of integration across functions, and how are the teams empowered?
· Is the process effective (well defined, meeting business objectives) and efficient (seamless in its execution, and repeatable)?
On the one hand, existing process improvement drives bottom-up adjustments, extensions, issue resolution, etc. Continuous improvement is core to any operations and part of the learning organization; however, it relies on an progressive changes and limited disruption.
On the other hand, process re-engineering can drive more top-down opportunities for improvement, with the potential to significantly transform how companies operate—requiring associated organizational change management, user education, and / or job design alignment.
In either case, business process analysis covers AS IS process and pain point mapping, fit-gap analysis, value stream mapping and improvement opportunity identification. This is coupled with TO BE process definition, keeping in mind the big picture, driving transition and deployment strategies. Digital tools and enterprise platforms, integrations, and other technical automations are essential enablers to implement new and improved business processes, covering access security, role-based interactions, advanced search capabilities, reconciliated business analytics, etc.
Combining process implementation and execution
Processes are defined for a given purpose and context. New processes are typically designed following a right-to-left approach, initially working backwards from new product development delivery milestone. This is most relevant for start-ups, new business units, or when driving business transformation roadmaps. An iterative approach is also essential to factor the relevant learning in the transformation and focus on right-sized solutions. Furthermore, it is sometimes required to cut short long-lasting process debate and focus time-to-value with quick-win implementations. As a matter of fact, organizations benefit from punctual decisions and the associated operating velocity.
Looking for business process experts? Look no further: QR makes the difference when it comes to product development process engineering and leadership, combining a tailored and holistic approach across both process implementation and execution:
Process implementation, including a strong focus on interdependencies and maturity, building a high-level view of when the data
need to be available, from one business function to another.
Process execution: running operations, as a key user group and / or in a support capacity to the business, understanding which decisions will be made from the process and associated data, empowering downstream activities to reinforce collaboration.
As organizations scale and mature, it is important to identify clear process ownership, from champions to data stewards, as gatekeepers of the process and the data it governs.
In addition, business analytics contribute to measuring and improving both how processes are implemented and how they are run. Essential knowledge can be gathered from multiple perspectives and experiences to define effective implementation roadmaps. In any case, it requires strong stakeholder management throughout and keeping at all times a critical eye on the bigger picture.
What are your thoughts?
Thanks to Luyao Zheng, consultant at QR_ for her valuable contribution to this discussion.
References:
IIBA, International Institute of Business Analysis (2005); A Guide to the Business Analysis Body of Knowledge (BABoK v2.0).