What’s the Problem? A comprehensive problem definition approach to maximize outcomes

Many factors contribute to the success or failure of enterprise projects, whether they be intricate analytical implementations or complex, large-scale transformations. But failure can often be traced back to one of the most critical stages of any project: problem definition.

Though simple on the surface, correctly defining the problem to be solved is the most challenging and most important part of a successful project. Broad problem statements, like “change an organizational activity”, often miss the contextual political, organizational, and other factors specific to the situation. Narrow problem statements, like “implement a new tool”, can jump to a solution too fast, missing other parts of the problem and leading to a limited result.

Instead of being treated as an almost administrative step, problem definition should be viewed as a process that encourages ideation, facilitates discovery, and enables choices among possible approaches. There are three stages of assessment that enable this process.

Defining a complete future state

First, it is important to clearly understand all critical aspects of the current state and the desired future state of the business when the planned project is implemented. This ensures that the team developing the project defines clear and focused end goals from a business perspective, such as building capabilities that improve decision making or improving the efficiency of existing activities. It also allows the team to identify interconnections between different areas that may need to be changed in parallel to achieve the future state.  For example, a system implementation almost always implies changes in decision making and processes, which may require parallel action.

Using organizational lenses to understand context
Second, the team developing the project should consider the impact of organizational factors on the project like operating models, political interactions, functional connections, and established processes.  Going through these lenses systematically, it is possible to classify specific factors as constraints around which solutions must be designed or areas of change that need to be addressed as part of the solution. This further opens up the definition of the problem to include not only the immediate need but the context in which a project will need to operate to be successful.

Assessing capabilities and maturity in a structured way
Finally, it is important to understand the capabilities of the organization from analytical, technological, and talent perspectives. This step is particularly critical for the team developing the project to assess before making decisions about systems or methods to solve the problem.  A successful solution looks very different and incorporates different elements for an organization that is very mature in its use of systems but is lacking in its analytical capability and talent versus an organization with top talent but limited technology horsepower and no analytical frameworks.  This assessment during problem definition helps the team define what will deliver results today and seamlessly enables definition of what a roadmap to future capabilities may look like beyond the initial solution.

We can see how these stages of problem definition enhance the success of a project in two case studies on cost reduction and process innovation.

Cost management at a call center

A services client is looking at process improvements in its call centers to increase efficiency and reduce operating costs by 10%.

Future state analysis: The client’s problem definition clearly articulates the desired end state through a specific cost reduction metric. But a quick current state analysis shows that quality of service had also been steadily declining over the past year in conjunction with previous cost reductions.  In this case, we discuss objectives with the client and recommend expanding the problem definition to include a reversal of quality of service performance alongside reducing costs.  This means coming up with a solution that makes the process both more efficient and more effective.

Organizational analysis: When looking at the call centers from an organizational perspective, we find that the team structure is extremely siloed, with teams split by different activities within the call resolution process. This appears to be leading to significant inefficiencies, and quick mapping of process steps and times confirms major bottleneck exist between teams.  When looking at the call centers from a political perspective, we find that the team with the most technical expertise influences decisions the most during the process and therefore influences behavior across less technical teams dealing with customers.  As a result of the two analyses, we recommend not only finding efficiencies in the current process but evaluating team structures and job design to integrate team members with different expertise into functional cells that can more rapidly address customer needs.

Capability analysis: We find that the client has a well educated talent base and very robust training processes in place, further enhancing the possibility of cross-training, more advanced decision-making, and development of strong functional cells to address problems more effectively.  From an analytical and technology perspective, the call centers are very limited in their applications.  This opens up the opportunity to rapidly introduce fundamental analytics to improve decisions and speed up the process more than through process changes alone.  It also presents the opportunity to lay out a roadmap for further cost reductions and quality improvements through longer-term automation and advanced analytics without jumping to those solutions right away.

By defining the problem with the aid of these three assessments, we are able to help the client move from viewing the problem as a cost issue that can only be addressed through process changes to viewing the problem as a cost and quality issue that can be addressed through a mix of process, organizational, and analytical changes in the same timeframe.

Process innovation for retail returns

A retailer is evaluating multiple issues in its returns process driving up costs and impacting customer service.  They have decided the solution is to accept and resell all returns at their stores rather than dealing with the complexity of further returns management.

Future state analysis: The end state objective of improving customer service and reducing cost is a clear objective.  But the best end state to achieve both is less certain.  When evaluating the initial solution, reselling all product in the stores ends up presenting additional complications.  First, the stores are not equipped to handle the volume of returns even when setting aside sales floor space to move returned products faster.  Second, to move product, the stores would have to set discounts at a very steep rate, recouping almost no margin on most products, raising questions of whether using resellers or liquidation may offset added returns processing costs.  Third, brand perception concerns start to arise from having substantial returns in view.  This analysis leads us to recommend a more comprehensive solution that incorporates an appropriate level of store reselling and returns processing and liquidation outside the stores themselves.

Organizational analysis: We then look at the problem through an organizational lens by quickly mapping the way products and information flow between customers, stores, logistics, suppliers, and resellers.  This mapping identifies multiple disconnects that cause the process to be less efficient and effective in terms of recouping costs. From a political viewpoint, many teams are also not as engaged in the process as store operations and are not structurally aligned to collaborate on returns.  Therefore, the objective of making the process simple for stores is dominant.  This indicates the need for cross-functional leadership from a strategy or finance team that has a view of the total business objective and can push the necessary teams to collaborate in a new way.

Capability analysis: The retailer had strong teams across the organization. But from an analytics and technology perspective, it is facing many issues.  Analytical capabilities within teams are limited and across teams are non-existent while systems are in various stages of maturity and are not set up to communicate with each other.  In this case, optimizing the process will not solve the whole problem, and a roadmap to build in the right types of ongoing analysis and data integration is essential to making the project successful.

When we help the retailer take a bigger picture view of the problem and potential avenues to an effective solution, we are able to see many more possibilities than the pre-defined solution that was initially presented.  We are also able to identify the major organizational and capability roadblocks that must be addressed for a change in the process to be successful.  This leads to a more complete and effective solution overall.


Both of these examples illustrate the value of a robust problem definition activity at the start of any project.  This approach can help companies transform the way they define and address their business challenges. It allows problems to be evaluated within the relevant business context, creates a wider choice of possible solutions, aligns resources with desired outcomes, reduces risk of failure and ultimately leads to better, faster, and more complete realization of results.

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Financial lever analysis: Advanced analytics to align strategic, operational and financial priorities


Operational transformation, cost optimization and innovation have become the strategic drivers of enterprise value in an increasingly complex and competitive business environment. Most companies use a mix of related assessments to prioritize, plan and realize their innovation and transformational agendas. These tools most frequently include financial analysis to project total performance, strategic analysis to assess the impact of individual initiatives, and operational analysis to align the business to strategic and financial goals.

However, the pace and complexity of change in today’s competitive marketplace accentuates some of the inherent limitations of this composite approach. For starters, this approach requires a rigorous and time-consuming information collection routine and is predominantly driven by historic data and experience. Neither of these characteristics are particularly helpful in coping with the real-time dynamics and variability of a changing business environment. It is also not capable of reflecting the relational impact of multiple internal and external variables that can drive decisions in a new direction. As a result, the cross-enterprise consequences of even the simplest strategic choices can be missed or completely discounted.

The financial lever analysis method effectively addresses both these limitations. One, it directly interlinks financial, operational, and strategic variables and models the financial impact of each decision across the P&L, balance sheet, and cash flow statement for more accurate, fast, and consistent assessment. Two, its forward-looking modeling capability enables the use of up-to-date information and forecasts for real-time assessment of the enterprise-wide impact of all decisions and initiatives on an ongoing basis.

Financial lever analysis can be applied to multiple needs throughout an organization, from cost optimization initiatives for a specific function to enterprise-wide transformation and innovation programs. Here, we look at how the technique can empower three critical components of strategic decision making for any given objective:

  1. Accurately defining interconnected causal links between operational choices and financial impacts
  2. Drilling down to real root causes and quantifying the effects of controllable changes on strategy and performance
  3. Assessing specific projects and their total implications for the business real-time


Accurately defining interconnected causal links between operational choices and financial impacts

Most companies have an intuitive but broad understanding of the relationship between operational activities and financial performance: for example the impacts of pricing, product, placement and promotional decisions on revenue or the cost composition of different SG&A lines. However, when initiatives are deployed, these simplified connections often miss hidden costs or benefits in another area of the business.  A granular understanding of the precise causal links between operational choices and financial performance is not often available.

System modeling enables companies to make direct links between multiple operational and financial variables and generate more nuanced insights into the cause and effect between variables. Modeling these insights in full sets the stage for companies to focus on initiatives and metrics that truly drive enterprise performance.

In the case of revenue, for example, there are multiple levers that drive growth, including the 4Ps but also including cost-based decisions and external factors like customer, industry and macroeconomic trends. These can all be defined in a system model comprised of process, statistical and feedback loop modeling that not only defines the direct links between these factors but also defines their magnitude of impact and hidden interrelationships.  From this, it is possible to understand different permutations of multiple factors when they are changed together. For instance, the effect of a combined pricing and product decision can have an exponentially different effect to applying either of those decisions individually, and that in turn can have a different impact if projections for customers and competitors change dramatically.

This model allows companies to build a more accurate picture of the revenue impact of different choices, thus streamlining and focusing the decision making process on accurate results. The same technique can also be applied across cost lines on the P&L and to other financial metrics extending to the balance sheet and cash flow statement.

Drilling down to real root causes and quantifying the effects of controllable changes on strategy and performance

Strategic transformation is a time and resource intensive exercise. Large scale transformations typically comprise a number of major change initiatives focused on different parts of the enterprise that require the involvement of multiple stakeholders. In such a complex scenario, it become necessary to accommodate varying perspectives on strategic choice and value. Financial lever analysis can help speed up this process, enable participants to focus on true root causes, and gain more buy-in by identifying objective and quantifiable drivers.

Whether building on a robust financial model or pursued independently, process-based techniques provide decision makers with a more nuanced and systematic understanding of the deep factors underlying performance and impacting strategic choices. Techniques like root cause analysis, statistical factor analysis, failure mode analysis, bottleneck analysis, and barrier analysis can all help identify quantifiable levers as well as more nuanced factors and roadblocks that impact a performance. In addition to pairing well with systemic models, these process-based techniques are best applied in combination with facilitated stakeholder engagement to bring out all potential root causes and factors and to ensure focus and objectivity.

Assessing specific projects and their total implications for the business real-time

The system model can also be used to assess the true impact of individual projects on a case-by-case basis. Once the fundamental understanding of interconnections is laid out, even if it’s not fully modeled, it can be quickly leveraged to evaluate the systemic impact of any project. For example, a cost reduction initiative in one area may trigger a counterproductive reaction in revenue or drive costs up in another part of the enterprise.  This becomes easier to see and manage when using financial lever modeling, and It becomes feasible to make calls on projects much more quickly.

A preemptive understanding all the key drivers and their interconnections and impacts further allows companies to accurately test the performance and indirect impacts of projects with a controlled pilot test prior to full rollout. With a better understanding of interconnected levers, the test can be designed to monitor the right metrics for all potential impacts and adjust necessary aspects before a launch.


Each of these three methodologies can operate on a standalone basis.  However, they are most effective when applied as a unified model. This combination of approaches has been proven to help business leaders orchestrate and optimize strategic decisions across the business.  As businesses face increasingly rapid and variable change, being able to define interrelationships accurately and quickly will be increasingly critical.

Utility analytics: applying new techniques for smart network and customer management

Digitization is opening up a host of new opportunities for the energy, environment and utilities industry to streamline productivity and efficiency, enhance customer engagement and develop new service models. This is imperative to anticipate and manage a changing environment like demanding digital customers, stringent regulations, distributed and variable energy sources and emerging energy management technologies.

The industry’s transformation to a next generation digital model will be driven by a combination of cutting edge analytics and technologies to explore new applications of data.  On the operations side, analytics can help utilities more accurately map supply to demand, shift to a predictive/preventive maintenance and outage management model and automate real-time controls to enhance network safety, reliability and resilience. On the customer side, utilities will be able to design more personalized customer engagements based on individual usage insights rather than broad consumption patterns.

This level of analysis for both operations and customers will be critical to sustain a robust level of service in the still evolving marketplace for decentralized and distributed energy resources. Here, we look at three examples across the utilities value chain where companies can combine advanced analytics and new technologies to drive more granular management of the total utilities system.

  1. Dynamic production forecasting and risk analytics
  2. Network digitization and optimization across providers
  3. Partnership-led approach to enable smarter demand-side energy management


Dynamic production forecasting and risk analytics

Forecasting demand and production is one of the most significant and complex activities for the utilities industry, be it energy producers, waste processors or water providers. This is already complex due to the interaction of environmental, seasonal, and behavioral variables that can impact both demand and supply. And it will only become more complex as new forms of customer demand like electronics usage patterns, internet of things (IoT) developments, and electric vehicles come into play alongside new supply side patterns like distributed energy and more highly variable renewable forms of energy. Finally, infrastructure limitations create tight demands and require more accuracy in forecasting and planning than ever before.

Advanced neural network forecasting that incorporates traditional and new data variables is becoming a reality and can enhance utilities’ efforts to plan available supply and accurately meet demand in a highly variable environment.  This modeling is a form of artificial intelligence that can work with limited or very diverse variables to build up forecast accuracy over time.  It is self-learning, meaning that it can incorporate new variables and adjust forecasts quickly to get better and better at accurately predicting demand.  As part of this modeling, existing data like localized weather data and its implications for both supply and demand can also be incorporated in more precise ways.  Taking this approach, utilities can begin with existing data and over time work up to more sophisticated and accurate forecasts and planning.

Network digitization and optimization across providers

Infrastructure modernization is a major focus for energy, environment and utility companies around the world.  To justify this heavy capital investment, companies will have to demonstrate incremental value from a data-driven approach and make smart decisions about investing in lowest-cost, highest-impact technologies to deliver more value-adding information.

On the analytics, side, the application of advanced techniques in the right decision-making and process context can open up more opportunities for fast and effective operational choices on optimization of network capacity, modeling of peak flows and correction of bottlenecks. The most important aspect of this activity is being able to get to action quickly.  By evaluating the decision-making process, organization, and priority level of different types of issues, it is possible to automate certain analytical procedures through algorithms and design a relevant “control tower” for key decision makers to assess total performance and top priority issues so they can choose the right path more accurately, quickly and consistently.  This can enhance network reliability, security and cost management as well as create a robust foundation for deploying more sophisticated techniques and technologies.

On the data side, there are several interventions that can start to be made with limited capital investments thanks to new technologies coming on the market.  Two of these are blockchain databases and internet of things (IoT).  Blockchain has emerged out of the financial industry, and interesting applications are being discovered across other industries as well.  For energy, environment and utility companies, the main application is being able to share supply and demand data quickly across multiple players in distributed systems at a low cost and with the right data protections in place.  IoT presents new opportunities to build on existing network monitoring and data capabilities to get down to the asset level at a competitive cost.  Historically, sub-metering and asset-level monitoring has been a fairly costly investment.  But as monitoring technology becomes more commoditized and the right infrastructure for data capture and analysis comes into play, the investment begins to look more manageable.

By pursuing incremental improvements in both analytics and technology and prioritizing investments based on their level of impact, energy, environment and utility companies have an opportunity to move faster on infrastructure modernization and gain value along the way.

Partnership-led approach to enable smarter demand-side energy management

Many utilities are limited in their ability to accurately assess customer energy, water, and other utility usage.  However, the proliferation of connected IoT home appliances with integrated smart energy management solutions opens up a new source of energy usage insights for utility providers. Rather than rely on aggregate consumption trends providers now have the opportunity to develop insights on specific customer and household profiles real time.

Utilities can start by establishing collaborative partnerships with smart home utility management solution providers to collect usage data at a much more granular level than they can today. These detailed data sets can help utilities build more accurate and insightful profiles of energy usage over daily, seasonal and annual time frames as well as begin to segment customers into more detailed demographic profiles across their service areas. This in turn can enable development of more relevant pricing and service offerings as well as feed into demand forecasting models to more accurately map supply to demand. Additionally, this data can be leveraged to create new incentives that empower customers to manage energy usage more effectively and participate in demand side management at higher levels with more impact on grid performance.

Once utilities can access more information on their customers, it opens up many opportunities to offer better service, prices and packages, manage demand and supply, and extend demand side management programs for grid performance.  The developments in IoT and customer energy management make this a ripe area for development.


Digitization and more effective analytics are becoming critical for utilities as we gear up for grid modernization and more dynamic supply and demand.  Rather than waiting for large, long-term investments, utilities will be well served by experimenting with incremental analytics and technology. Dynamic forecasting, network optimization and creative demand-side energy management are just a few areas where data and technology can be applied in new ways to capture value more quickly. Experimenting with these and other techniques will be essential to manage multiple transitions quickly and at a low cost.

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Professional services: Leveraging insights to transition from service providers to strategic partners

The professional services industry has been on a steady evolutionary trajectory since it emerged as a cost-saving outsourced services model a couple of decades ago. From operating call centers to executing non-core repeatable processes, the industry has expanded its professional services portfolio towards the value end of the outsourcing opportunity. Nowadays the emphasis is predominantly on managing and improving strategic business processes, enabling business outcomes and enhancing enterprise value.

This transition to a value-based strategic services model has opened up a range of new growth opportunities as well as challenges for the industry. Though cost is no longer the key differentiator, professional services firms are under constant pressure to minimize costs even as they bring in new talent and technologies to compete in this changing environment. Technology itself has already transformed traditional operating models in areas like outsourced front office, HR and accounting services. Going forward all aspects of the professional services portfolio will be impacted by technology.

An analytics-led model will enable professional services firms to balance the pressures of leveraging new technologies and improving operating efficiencies without losing focus on delivering strategic business outcomes and enterprise value to their clients. A structured approach that combined multiple techniques from analytics, technology automation and process design will help them prioritize high-impact technology investments, improve operational efficiencies and create strategic differentiation and competitive advantage.

Here is how this approach can be applied to three of the industry’s key challenges:

  1.  Predicting and managing HR capacity and needs
  2. Improving quality
  3. Enabling strategic client value through insights

Predicting and managing HR capacity and needs

Talent, with the right skills and capabilities across domains, functions and business processes, will be a key lever for growth and competitive advantage as professional services firms focus on delivering more strategic business value to their clients. It will be a significant challenge for professional services companies to forecast and manage HR capacity and ensure it is aligned with business strategy and client needs.

A blend of analytics and process can help improve decision making in this area. Professional services firms can start with an accurate forecast of customer demand based on statistical modeling that accounts for specific demand drivers, past trends in seasonal and annual growth and future customer projections. Concurrently, they can evaluate patterns in headcount turnover, training time, and processing times based on productivity and quality metrics to determine the headcount needed to meet this demand.

Building on this baseline analysis, companies can then look at different capacity strategies and their relative merits in terms of productivity, quality and utilization. For example, in some cases there could be a benefit to cross training resources to work on multiple clients depending on seasonal or daily/weekly demand.  The emphasis in these cases should be on balancing training and transition costs against the incremental benefits of maximizing utilization.

Testing different scenarios and strategies helps companies work out a balanced capacity plan that optimizes and maximizes resource utilization across clients and activities. Apart from delivering a direct impact in terms of productivity gains and cost savings, this level of analysis also does not require heavy investments in IT or technology solutions.

Improving quality

Quality of service and outcomes is becoming a key competitive differentiator as professional services firms engage with core enterprise applications and processes. Though industry quality frameworks have been constantly evolving, blended analytics, process, and automation techniques can be used to create a clear and differentiated leadership position in a competitive market place.

This combination helps companies identify quality issues, trace them back to their root causes and then deploy solutions that reduce the risk of manual quality errors. The starting point is to leverage customer data, process time data, and process mapping to pinpoint and resolve quality issues at source. Once this assessment framework is in place, it can be leveraged to continuously monitor and improve quality over time. From there, it becomes possible to identify steps in the process where a combination of analytics and automation can be deployed to completely eliminate manual processes with high risk of error. This stage is essential to remain competitive from both a productivity and quality perspective, and it can only be executed effectively based on a full analysis of where errors are occurring in the existing process.

As in the previous example, this approach can deliver quick yields in terms of quality management and control and can be integrated quickly and at low infrastructure costs without requiring expensive or elaborate IT solutions.

Enabling strategic client value through insights

The ability to convert data into insights and drive strategic business value for clients will be the only sustainable competitive advantage in an outcome-based professional services model. Most firms already have access to a range of client business data. The leaders will be those companies that can blend an understanding of their clients’ strategic priorities and extract insights from the data to deliver relevant insights and build more strategic long term partnerships with their customers.

A strategic assessment of a client’s business priorities and needs can help professional services firms focus on generating analytical insights related to new growth opportunities and cost savings that are relevant for their clients.  This begins with strategic analysis of the client company followed by an assessment of the data the professional services firm has at its disposal.  From there, firms can begin assembling the data they have in new ways to extract insights that are aligned with the client’s business priorities and share these insights alongside existing services.

This often includes presenting data and insights in more visually informative ways but also extends to more analytical assessments like building forecasts, identifying activity clusters among similar data points and more prescriptive analyses to help clients make actionable decisions. Initiating an insight-led partnership with customers and focusing on these more advanced insights gives companies the opportunity to become a strategic partner rather than an outsourced provider to clients.


By leveraging data in new ways and combining analysis with process improvements and strategy, professional services firms can address cost and technology pressures and drive towards automation and higher productivity while in parallel moving up the value chain in their client services. Adopting a holistic approach defined by their unique business context and strategies will also be key to streamlining the industry’s transition from outsourced service provider to strategic client partner.

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Healthcare Analytics: Transitioning iteratively to value-based, data-driven healthcare service

Healthcare industry stakeholders have been opening up to the potential for big data and analytics to help them achieve key priorities.  However, this process faces many hurdles in terms of data integration.  First, data is still siloed across discrete provider, payer and ancillary services networks. Second, integrating Electronic Medical Records (EMR) from across these disparate networks is a complex process. Third, there are substantial privacy concerns and regulations that govern the distribution and sharing of sensitive patient information.

Stakeholders are by and large aligned to addressing these challenges. As the sector progresses to “One Patient, One Record”, stakeholders will need to invest in more robust data and analytics solutions to extract value from this unified source of data. This will help the sector address multiple challenges from rising costs to patient service as it transitions to a value-based care model.

As the data comes together, there are significant parallel opportunities to begin leveraging existing data from the existing ecosystem and deliver incremental value-adding analytics. Analytics pursued in the right context at the right level can deliver higher care quality while maximizing efficiency of clinical operations and minimizing cost without waiting for extensive systems to be completed.

In order to make the most of data available today, stakeholders will be best served by blending strategy, design, analytics, process engineering and technology solutions that produce distinct and measurable outcomes. Here we take a look at how a blended solutions approach can enable the industry to solve specific challenges with the data they have today and progress in their decision making:

  1. Optimizing the patient journey
  2. Resource planning and allocation
  3. Managing costs 

Optimizing the patient journey 

Patient turnaround time, typically expressed as average patient wait time and length of stay, is a key measure of the patient healthcare experience. This metric is an indicator of the efficiency, timeliness and quality of care delivered by a healthcare system. Understanding and optimizing the patient’s journey holistically will enable providers to identify and rectify inter and intra procedural redundancies and bottlenecks, improve processes and enhance outcomes, efficiencies and experience.

The dynamics of ER make it the ideal test-bed for optimizing patient journey within any healthcare system. The first step is using industrial engineering techniques like process mapping that allow providers to map current patient flow, from check in to discharge or admission, across all patient touchpoints in the ER ecosystem. Providers can then accurately measure process times for all patient facing activities, from enrollment to wait times between processes, and identify performance inhibitors and bottlenecks.

IT integration also plays a critical role in building a cross-ER view of the patient’s journey. This exercise can reveal multiple opportunities to streamline patient flow. For instance, a directed decisioning system can provide all ER nurses with case-specific information that can be used to proactively prepare the testing/consulting resources required to accelerate a patient’s journey through the system. Robust clinician support models leveraging machine learning can combine patient medical history with current symptoms to dynamically assign priority and resources to the most critical patients, thus enabling more positive outcomes and enhanced care quality. These types of interventions can be done as overlays on existing systems to reduce costs.

Resource planning & allocation 

Staffing is the single largest source of cost in a healthcare system and at the same time critical to manage from the perspective of quality of care. Most healthcare systems still lack full analytics capabilities required to plan schedules, forecast long-term resource needs and drive meaningful process improvements. 

Understanding the causal relationships between multiple external drivers and patient flow is an important starting point from which initial forecast models can be developed.  These forecasts can be built out iteratively, incorporating new data on patient flow, demographic changes, and other variables that may impact customer volume.  This enables providers to optimize effectively between staff deployment, care quality and financial productivity. It is then possible to accurately model patient demand using multiple analytical techniques like regression, neural networks and machine learning. Correlating new sources of data like seasonal diseases can further enhance the granular accuracy of this forecast.

It is then possible to layer in process analysis with sets of scheduling rules, like shift length and number of days, to identify potential efficiencies and to define optimal work schedules. This consists of measuring work times, defining standard activities and work flows, and then building up a complete picture of the standard times and variability in processing patients.

With a robust and data-driven scheduling process in place, providers can then focus on more strategic assessments of long term capacity requirements, identify root cause process issues and streamline the entire process through iterative steps over time.

Optimizing cost 

On the operational side, there are many untapped opportunities to achieve sustainable costs savings of 10 percent or more through data-driven initiatives. This extends from physician performance analytics to HR benefits, purchased services, staffing to demand, clinical operations efficiency and inventory and supply chain.  Looking across the healthcare value chain, there are multiple places where analytics can yield significant results in terms of costs and efficiencies.

Although hospitals systems are formed with the intention of integrating and reducing costs, redundancies often remain. But it is entirely possible to eliminate these redundancies and reduce waste spending by standardizing and centralizing operations. The first step is to map and analyze inventory and supply chain processes. A deep understanding of existing processes and structures combined with advanced demand modeling techniques and enhanced by disruptive technologies can help healthcare providers create more accurate inventory forecasts. This can in turn help providers optimize supplier SLAs to ensure that they are aligned with the cost and quality metrics required for efficient inventory management.

The healthcare industry faces a unique set of challenges that have thus far resulted in a piecemeal and limited approach to big data and analytics. However, the sector is well poised to rise to the analytics opportunity and transform its approach to delivering care.

Manufacturing analytics: Integrating business activities

Manufacturing has a rich tradition of analytics – from leveraging analytics for process design and engineering to using highly analytical techniques such as lean, just-in-time and six sigma to optimize productivity and costs on the shop floor.

Industry 4.0 represents the sector’s tipping point from the traditional into the digital age. This shift to Smart Manufacturing will entail not just a connected, productive and efficient shop floor but also a holistically intelligent manufacturing value chain. Manufacturers need to build on their traditional analytics foundations to bring in new technologies and capabilities to cope with a diverse range of incredibly diverse and exponentially complex digital age datasets. Concurrently, the sector also needs to ensure that the capabilities to convert data into actionable insights and strategic outcomes are aligned to address some of its key challenges today.

The biggest challenge for manufacturers will be to quickly and consistently demonstrate the enterprise value of their analytics investments. Manufacturers therefore need a two step approach to accelerate and maximize the value of their analytics investments. One is to take a systems thinking approach to map the business value of all the interactions, interconnections and interdependencies across their entire manufacturing value chain. This empowers manufacturers to define an investment strategy that is focused on addressing their immediate business challenges and on delivering sustainable business value. Two is to create a tool, technique and technology agnostic solution that custom blends strategy, design, analytics, process engineering and technology to address the unique needs, challenges and objectives of the business.

We can see how this approach works in three different contexts in the manufacturing value chain:

  1.  Integrated data & process design for efficient production
  2. Reducing supply chain variability through forecasting and planning
  3. Leveraging customer data for new product development

Integrated data & process design for efficient production

Manufacturers have always relied on a range of data sources and types to inform their productivity, efficiency and quality initiatives on the shop floor. Industry 4.0 programs have only expanded the scope and variety of these data feeds. But the approach is often bounded by a traditional view of the drivers of productivity, efficiency and quality. Therefore, production or production-adjacent data typically dominates production floor analytics.

A systems thinking approach considers interactions and interdependencies across the enterprise ecosystem, rather than just the production sub-system. An organic data-enabled understanding of how different subsystems interconnect and interact allows manufacturers to create more integrated and effective solutions. For example, analytics can help manufacturers identify/understand customer concerns about products or service levels. These concerns can then be mapped back to the root cause, like the specific production techniques/processes that need to be optimized or component sourcing strategies that need to be honed. By linking data sources across the manufacturing enterprise and understanding how different subsystems interact, manufacturers can effect more impactful, sustainable and valuable transformations to their production environments.

Reducing supply chain variability through forecasting and planning

Traditionally the supply chain has been viewed as a procurement activity distinct from production. But an intelligent, agile and integrated supply chain is the central nervous system of every smart manufacturing enterprise. An Industry 4.0 supply chain integrates with all critical business processes and systems, leverages real-time analytics to create a single source of truth and enables real-time decision making capabilities across the enterprise/supplier ecosystem.

Systems thinking enables manufacturers to map the organic flow of data and analytics across the organization. Understanding how actions, problems and decisions are connected allows them to use the right modeling techniques to enable the right decisions in an integrated environment. For example, manufacturers will be able to share any new analytical models that they use for customer-driven demand forecasting with their suppliers through integrated supply and operations planning. Manufacturers need to create interfaces for real-time data exchanges, build out further analytics based on that data and accurately map procedures between entities. This will give a deeper understanding of supply chain bottlenecks and improve real-time decision making.

Leveraging customer data for new product development

The systems thinking view can even be extended to new product development that incorporate data and analytics. This applies for both consumer and B2B customers who are both increasingly seeking IOT solutions for various needs.

With this mindset, the customer can be viewed as their own ecosystem where data can be captured, linked to other sources, analyzed, planned and measured. This data can then inform the design and development of new products that truly achieve the IOT expectations of the customer. In this case, both the right analytics and the right process mapping is needed to accurately reflect customer activities and needs. Beyond the development phase and post-launch, manufacturers can still leverage aggregate analytics based on customer data to continue informing production and the supply chain.

These are just three areas where a systemic approach to strategy combined with a holistic approach to problem solving can help manufacturers maximize the RoI of their technology investments.

In the connected manufacturing paradigm that is Industry 4.0, a discrete approach to problem solving, at the level of individual subsystems, will only yield incremental results. Manufacturers need to build a connected and organic understanding of their manufacturing ecosystem in order to build the capabilities required to create sustainable differentiation and competitive advantage. A systems view of the manufacturing ecosystem combined with some holistic problem solving frameworks will allow manufacturers to unlock more value from their investments.

To learn more, please contact us at:

Caroline Conway:

Suresh Kumar:

Retail Analytics: Enabling contextually-relevant decisions


The value of data is to empower enterprise decision making: to enable fast, effective decisions with a measurable impact on operational and strategic performance.

Fast and effective decisions are rooted in context. In the retail industry, the current context includes technological disruption, changing consumer expectations on access and convenience, and exceptional cost pressures. Layered on top is the subjective context of unique challenges and strategic objectives experienced by each retail firm.

Big data and analytics programs have to be designed to meet retailers’ contextual needs if they are to successfully enable decisions. However, the current approach to big data and analytics in retail is still largely focused on getting data and technology as structured as possible. The potential for analytics to help retailers solve some of their most pressing business problems and drive critical business decisions remains untapped.

To overcome this, the sector has to adopt a more holistic problem solving approach, one that is tool, technique and technology agnostic, in order to extract sustainable value from its big data investments. The emphasis has to be on relevant problem solving built on a deep understanding of the strategic objectives and business context of each retailer.

With this subtle shift, retailers can more quickly shift to tailor made solutions that solve relevant problems through a blend of strategy, design, analytics, process engineering and technology rather than data and analytics in isolation. There are countless opportunities across operations, merchandising, supply chain, and home/back office using this approach.

Here, we take a look at three examples:

• Streamlining customer returns
• Optimizing future assortments
• Structured cost reductions

Streamlining customer returns

A streamlined returns process is as much about ensuring a frictionless experience for customers as it is about maximizing value recovery for retailers. This experience is becoming more essential in a clicks-to-bricks omnichannel world, and as customers shift online, the volume of returns is becoming more critical for retailers to manage effectively. The conventional approach where teams deploy discrete solutions in silos to streamline their part in the returns process will no longer work. Instead, retailers need to bring multiple organizational functions like technology, operations, merchandising, and supply chain together and heavily leverage a blend of strategy, data, analytics, and process design to define a complete solution.

A holistic approach begins with analyzing returns data both from the perspective of customer experience and recovery value. Defining the baseline by mapping current data and process flows allows retailers to set strategic goals and align all functions to a unified purpose. Once clear goals are set, process redesign, pricing and costing analytics, and network modeling techniques can be combined to build a comprehensive roadmap to reach new levels of customer experience and financial value. This then carries through to implementation where linking multiple datasets into a single source of truth and providing all stakeholders with the right analytics tools based on that source will enable each group to evaluate returns performance within their area while staying connected to the overall goal of better customer experience and cost recovery.

Optimizing future assortments

Retailers are also being challenged to reevaluate their assortments both short and long term as customer demands evolve rapidly. In both cases, retailers are faced with the twin challenges of making sense of data that may not be comprehensive enough and analytics solutions that are unable to cope with the dynamic interplay of variables impacting assortment. In the fast-paced world of merchandising, the technology-led approach of amassing all relevant data and building the most sophisticated models cannot match the dynamism and speed of customer changes and merchandising decision making.

The alternative is to start blending customer data and assortment analytics with the distinct practices of strategy and design thinking. Instead of starting with data, a retailer must start with the core decisions that need to be made and then design a framework to assess the impact of a variety of merchandising decisions on performance. This allows merchandisers to identify the levers that actually drive changes, which is especially relevant when data is not complete or linked together. Once this framework is in place, it is possible to focus on the more granular interactions of assortment, price, promotion, placement, and factors like cannibalization using the data that is already available and applying advanced optimization to provide relevant guidance. This can finally be integrated into the buying process by using design thinking techniques to develop tools that enhance merchandising decision making and enable merchants to incorporate the quantitative data with their qualitative knowledge to make final assortment decisions.

Structured cost reductions

An across-the-board cost cutting exercise may seem like a quick and easy way to deal with the increasing cost pressures on retail businesses. But in the long term, this approach is often counterproductive to performance while still leaving most of the waste on the table. Advanced analytics incorporated with budgeting and operational decision making allows retailers to assess cost drivers and predict future costs across sites based on site-specific drivers like sales, customer traffic, store size, and other variables. By modeling predicted behavior, retailers can then set accurate budgets and target and prioritize outliers for cost reduction programs that address the issues specific to each site.

This integrated strategy and analytics approach is quick to deploy as a surgical strike across the enterprise. It can be used to target specific cost areas, like labor, maintenance, goods not for resale, utilities, and other cost lines across the organizational footprint including repeatable head or back office activities. By using data and analytics to get a more strategic view of costs down to the site level, retailers can make substantial cost reductions while maintaining performance.

These are just a few examples of how the holistic problem solving approach – blending multiple practices and techniques, including analytics – can help retailers address some of their most pressing and immediate business problems. There are numerous instances, across the diverse and complex retail value chain, where a problem solving approach rather than a technology-first approach will yield faster, more effective, and more sustainable results.

Big data and advanced analytics will play a central role in translating data into actionable insights and business outcomes for all retailers. But retailers that adopt a more holistic approach accounting for their unique business context will be able to differentiate themselves through the short term decisions and long term strategic decisions they can make.

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