Going for gold • 4
Inspired by an article in Harvard Business Review about the underlying quests for corporate transformation (Anand & Barsoux, 2017), I have identified seven arenas where the power of service design can transform organizations, teams, and people. In this blog post, I explore Service Design for Operational Excellence.
4. Service Design for Operational Excellence
Purpose: Crafting approaches, strategies, services, processes, and tools to improve service productivity, quality, and profitability. Service quality is defined as a “high standard of performance that consistently meets or exceeds customer expectations” (Wirtz & Lovelock, 2016).
Note: See also my blog posts Going for gold • 5 and Lean & mean innovation machine • 1.
Common themes: The Toyota Way. Lean thinking. DOWNTIME (8 types of waste). Customer focus and feedback (Voice of the Customer). Service performance, productivity, and quality. Process mapping, journey mapping, and flow charting. Service quality gaps. Standardizing and optimizing workflows. Digitizing and digitalizing processes. Standardisation. Demand patterns, demand management, and capacity management. Automation and RPA (robotic process automation). Continuous learning and improvement. Kaizen. Data-driven, fact-based decision making. Simulation and modelling. Agile ways of working. ISO certification. Return on Quality. Etc.
Project archetypes:
Designing for waste elimination. Streamlining service processes (for value facilitation and co-creation) to reduce variability, inefficiencies, and environmental impact while enhancing service productivity and quality. Key strategies include identifying and reducing sources of waste (think: DOWNTIME); eliminating non-value-added activities, ensuring every step adds value from the customer’s perspective; digitizing and digitalizing processes and workflows; and establishing robust protocols and procedures for effective complaint handling and service recovery.
Note: See the four project archetypes below for additional strategies to minimize waste and maximize customer value.
Example: Amazon uses data-driven decision-making, lean supply chain practices, and advanced automation to streamline operations.
Designing for standardization and adaptability. Balancing standardization and adaptability to create reliable and repeatable systems, processes, and servicescapes that can be easily replicated or scaled (across different contexts, locations, and time zones) while maintaining the same levels of service quality, productivity, and profitability. This may involve taking a modular approach, where certain components are standardized to ensure consistency and efficiency (such as CRM systems, production processes, and onboarding), while other components are adapted to fit local needs and preferences (such as service delivery processes, marketing activities, and DEI training).
Note: Replicating involves duplicating service processes and systems exactly as they are in different contexts or locations. Scaling involves expanding service capabilities to handle increased volume, complexity, or geographical reach. (Inspired by Grönroos, 2007; Normann, 2000.)
Example: McDonald’s standardizes cooking processes, equipment, and training programs while localising (to a certain degree) restaurant designs, menu items, and marketing campaigns.
Designing for automation and augmentation in a team-centric way. Automating tasks and workflows using the power of NLP, RPA, and Intelligent Automation to improve accuracy, reduce time-to-completion, ensure reliability, enhance service productivity, elevate service quality, free up capacity, and improve quality of life (see, e.g., Gupta, 2023; Porwal, 2024). Additionally, by augmenting human capabilities with next-gen AI and XR technologies, project teams can evolve into ‘superteams,’ where human and non-human team members work seamlessly side by side (Schwartz et al., 2020). This approach is particularly valuable during moments in the project lifecycle that benefit from increased firepower and alternative perspectives – such as sensemaking, systematic ideation, or participatory decision-making. Successful implementation of automation and augmentation requires ethical guidelines, redesigned workflows, robust tools, emotional intelligence, data governance, and intentional upskilling.
Note: Enabling or emerging technologies for automation and augmentation include ML (machine learning), NLP (natural language processing), RPA (robotic process automation), adaptive AI, IA (intelligent automation, infusing RPA with AI), cobots (collaborative robots), digital twins, MR (mixed reality, combining elements of both VR and AR), blockchain, IoT (Internet of Things), AI-powered collaboration tools, drone technology, edge computing, edge AI, etc.
Example: The Salesforce Einstein Automate platform combines RPA with AI capabilities to help clients automate a wide range of tasks and workflows, such as customer support, personalized content, patient scheduling, inventory management, loan approval, risk assessment, and so on.
Designing for optimal utilization. Balancing demand and capacity to utilize staff, labor, equipment, and facilities as productively as possible, particularly in the face of fluctuating demand. Strategies to manage capacity include stretching capacity levels and adjusting capacity to match demand. Strategies to manage demand include reducing demand in peak times, increasing demand during low periods, configuring effective queuing systems, implementing reservation systems, and reducing perceived waiting time. (Wirtz & Lovelock, 2016)
Example: Starbucks uses demand forecasting and workforce management techniques to balance staffing levels with customer demand.
Fostering culture of continuous improvement. Building and nurturing a culture of continuous improvement to sustain operational excellence and competitiveness. This involves engaging all employees in identifying and implementing improvements. It also requires consistently acting on feedback from customers, employees, and other stakeholders. Additionally, promoting life-long learning where employees continually acquire new skills and knowledge, is essential. This bottom-up and outside-in approach fosters accountability, empowerment, and customer-centricity.
Example: Ritz-Carlton conducts daily line-ups where employees gather to share stories and discuss ways to enhance service, fostering a culture of continuous improvement and exceptional customer service.
Alternative/complementary methodologies & toolkits: Lean Service for optimizing service delivery and improving workplace efficiency (with tools such as value stream mapping, service blueprinting, 5S, Kaizen, and root cause analysis). Six Sigma for improving service quality by removing the causes of defects and minimizing variability in service production and delivery processes (tools: DMAIC, VOC, SIPOC, fishbone diagrams, control charts, FMEA, etc.). Total Quality Management for enhancing the quality and performance of service delivery (tools: PDCA, SERVQUAL, QFD, service blueprinting, Pareto analysis, root cause analysis, etc.). Theory of Constraints for addressing critical bottlenecks in service delivery (tools: current reality tree, future reality tree, five focusing steps, etc.). Business Process Reengineering for fundamentally rethinking and redesigning core service processes (tools: value stream mapping, process mapping, benchmarking, gap analysis, root cause analysis, etc.). Robotic Process Automation (RPA) for automating routine, repetitive, and rule-based tasks in service production and delivery processes, Intelligent Automation (IA) for handling non-routine, complex tasks and end-to-end workflows with cognitive abilities, and Hyperautomation for automating as many business and IT processes as possible. Queue management software for managing customer flows in real-time and simulation tools for predicting and optimizing queue performance (by modelling different scenarios). Agile and the Scrum framework for managing and delivering projects in service organizations (tools: sprint planning; product and sprint backlogs; daily scrums/standups; sprint reviews and retrospectives; epics and user stories; etc.).
Supplementary methodologies & toolkits: Design thinking and human-centered design. CX and EX management. Knowledge management. Change management.
Exploring the problem space: Understanding the broader context. Identifying customer needs and pain points. Mapping existing processes. Collecting and analyzing quantitative data. Identifying areas of inefficiency/waste. Framing opportunity spaces for improvement. Determining ambition levels. Establishing objectives, defining KPIs, and setting baselines. Crafting tentative North Star. Designing provocations to challenge assumptions, provoke reactions, and stimulate discussions. Framing or reframing challenges/problems. Etc.
Exploring the solution space: Generating, screening, and prioritising improvement ideas. Continuously testing and adapting tentative solutions through storytelling, rapid prototyping, experimentation, simulation, and piloting. Defining stakeholder and business impact. Crafting compelling stories and value cases for change. Identifying roadblocks, creating roadmaps, defining requirements, and mobilising resources for implementation and sustained success. Establishing a culture of continuous improvement and operational excellence – through Kaizen principles, leadership support, upskilling, continuous experimentation, feedback loops, customer involvement/co-creation, revamped performance management systems, revised KPIs, and transparent communication. Etc.
Project sponsors: CIO, COO, CHRO, CFO, CEO, or equivalent
Desired outcomes: ↑ efficiency, ↓ organizational waste, ↑ compliance, ↑ quality, ↑ cost savings, ↑ flexibility, ↑ employee engagement, ↑ customer satisfaction, ↑ customer loyalty, ↑ core/process innovation, ↑ organizational learning
Note: For a rock solid introduction to service processes, service productivity, service quality, and service performance in service organizations, check out chapters 8, 9, 14, and 15 in Wirtz & Lovelock (2016).
Power tip: For all project types, harness the power of data analytics – including descriptive, predictive, and prescriptive analytics – to gain deep insights into past performance, forecast future trends, and recommend optimal courses of action.
Service Design for Ethical Circularity will be covered in the next blog post.
References
Anand, N. & Barsoux, J-L. (2017, Nov–Dec). What everyone gets wrong about change management. Poor execution is only part of the problem. Harvard Business Review.
Grönroos, C. (2007). Service management and marketing: Customer management in service eompetition. John Wiley & Sons.
Gupta, S.K. (2023). The importance of human-centered automation in manufacturing. Forbes.
Normann, R. (2000). Service management: Strategy and leadership in service business. Wiley.
Porwal, Y. (2024). From RPA to Intelligent Automation to Hyperautomation – the progression of business process automation. Binmile.
Schwartz, J., et al. (2020, May). Superteams. Putting AI in the group. Deloitte.
Wirtz, J. & Lovelock, C. (2016). Services Marketing: People, technology, strategy (8th ed.). World Scientific Publishing.
