Leveraging Human Capacity Planning for Efficient Infrastructure Provisioning

Transcription

1 Leveraging Human Capacity Planning for Efficient Infrastructure Provisioning Shashank Pawnarkar, Tata Consultancy Services Today there are several infrastructure capacity planning techniques are available to make effective use of IT infrastructure and to enhance the infrastructure performance. The infrastructure capacity planning is not only based on IT infrastructure technology but it is based on business environment and business inputs. An analytical model based on historical business data, past trends, parameters such as peak business time, users skillsets and transactions volumes are of paramount importance to decide the infrastructure capacity. The Human Capacity Planning technique involves calculating optimal number of skilled resource required to complete business operation at particular time. This papers discusses how business inputs based on Human Capacity Planning helps to improve and optimize, efficient Infrastructure provisioning. Introduction In typical infrastructure capacity planning the inputs are based on sizing, peak load, server utilization and data/transactions volume, whereas the business driven Human Capacity Planning gives the exact demand pattern, stating optimal number of resources required when (time and date), forecast of volume (number of transactions) and the time taken to complete the truncation. The output of Human Capacity Planning will aid in determining the exact count of resources (number of users) required, amount of volume transactions, demand patterns which further helps to calculate exact requirement on server infrastructure. The Human Capacity Planning provides exact patterns of peak load of usage during the year or month or day or time, and thus, Human Capacity Planning output complement the traditional method of Infrastructure Capacity Planning. The offshore BPS (Business Process Services, often called as BPO Business Process Outsourcing) typically provides back office business services to global Customers spread across multiple countries and multiple geographies wherein offshore agents with varying skillsets works in different shifts and delivers the work in stringent SLA (Service Level Agreement). Even though there is a fluctuation in workload demand and volumes, agent has to deliver the work with very high accuracy and within shortest possible time. Any miscalculation in demand forecast often leads to more number of human resources when work volume is more or high number of resources when work volume is low. These kind of misjudgment of Human Capacity Planning results in heavy loss of productivity and increase in operational cost and at the same time core IT servers are underutilized or over utilized. In case of peak demand the server response is degraded and resulting in further delayed server response, loss of productivity for agents and loss of business credibility to the organization. The average transaction handling time (AHT) is key success parameter for the agent and in turn for BPS operations. While core IT applications and infrastructure plays a crucial role in determining response time and AHT, Customer are not very formal and clear about how they can assert that their system performance is adequate enough for the BPS to work well. The BPS works in constraint that Customer own and controls

2 the IT applications and infrastructure which are difficult to change according to the need of BPS workload, and hence it is best strategy to provide minute level Infrastructure Capacity Planning inputs to the business. Figure 1: Infrastructure Capacity Planning driven by Human Capacity Planning We have a leading edge Human Capacity Planning solution which analyzes structured and semistructured data enabling consistent business insights and predicting accurate human workforce capacity, thus drive productivity savings of millions of dollars in operating costs. The Human Capacity Planning solution leverages analytics, forecasting and optimization techniques to predict accurate human resources based on demand and volume. The forecasting is typically based on regression techniques whereas optimization is based on linear programming. Human Capacity Planning Solution In BPS world, operating discipline is very essential to remain competitive and then if you have to have leading edge, you need to have comprehensive Human Capacity Planning solution which will cater to typical BPS processing needs be it in terms of human resource planning, allocating the work to the Capacity and then monitoring of the work execution to ensure that SLA are gainfully met with optimal manner. The Human Capacity Planning solution wherein, leveraging advance analytics, forecasting and optimization happens at a granular level (up to 15 minutes of time period) and taking into account, operational environmental dependencies like shift timing, skill wise AHT (Average Handle Time), volume forecast and SLA (Service Level Agreement). Challenges in Human Capacity Planning: 1) BPS Operations are often challenged with high work volumes with available staff. At times, staff may over utilized with high work volumes, or underutilized during periods of low work volumes. Many a times, this is cumbersome and ineffective process which leads to situation of over allocating resource when demand is low and under allocating resources when volume demand is high. Floor managers often adhere Spreadsheets to manage the Human Capacity Planning. In such a

3 scenario, getting comprehensive understanding of all the parameters which impact Capacity Planning was a mammoth task. 2) In the BPS environment, Customer expects service provider to meet the operational SLA under varying degree of workload, and that too at a lower cost but with higher accuracy. Without the technology solution, the BPS floor managers were struggling with under/over staffing, mismatched skill set, ability to predict the workload volume, and all this was getting translated into higher cost of operations and also missing the operational SLAs, and thus customer dissatisfaction. Missing of SLAs would sometimes also cause financial penalties to the customer. Challenges in Infrastructure Capacity Planning 1) One of the key challenges is to determine the right amount of resources required for the execution of work in order to minimize the financial cost and to maximize the resource utilization. It is necessary to utilize infrastructure resources properly to reduce the cost while maintaining the Quality of Service parameters like throughput, response time, and performance while adhering to Service Level Agreements (SLAs). 2) The BPS agents works on core IT applications and server infrastructure provided and supported by the Customer that means in cases of peak workload, BPS agents are restricted by the available server infrastructure which often cause delay in completing agent s tasks resulting in operational inefficiency and missing target SLAs. Alternative Solutions Evaluated Agent Management System (AMS) and Work Force Management (WFM) tools (Internal) were evaluated before building new solution. Our findings are summarized in sections below - 1. Agent Management System Agent Management System (AMS) has been designed to generate workload/schedule using volume based SLA. For example, if from 3:00 PM to 3:30 PM, 100 transactions have been received, and 80% must be finished before 3:30 PM. The key requirement for the Capacity Planning solution is to generate workload/schedule using time based SLA. Since time based SLA and volume based SLA are two different paradigms, AMS does not fit the requirements of the Capacity Planning Solution. 2. Work Force Management Tool Work Force Management (WFM) tool is a tool for Queue Management. However, in WFM, all the key characteristics required for generation of workload are coded into the tool and are not user configurable. Moreover, this tool does not cater to our requirements for country specific SLAs. Since the key requirement of the Capacity Planning Solution was to have end user configurable solution taking into account Process Characteristics, BPS People Profiles, SLA requirements and other environmental attributes to enable semiautomated Queue Management, WFM was not deemed fit. 3. Other Commercial Solutions Other commercially available solutions were also evaluated. None of the solutions had ability to deal with Varying/Dynamic Cut-Offs Human Capacity Planning - Solution Description The Capacity Planning solution uses a scientific approach to arrive at the most optimal resource plan to meet the required SLAs. The solution uses industry standard mathematical algorithms to analyze, forecast, schedule and monitor resource requirements using techniques such as regression analysis and linear programming.

4 Figure 2: High level schematic of Human Capacity Planning Solution The typical inputs to Capacity Planning algorithm are: - AHT (Average Handling Time) as per various products and sub-products - Skill set - Work shifts (given that typically BPS business runs 24x7) - Operational SLAs (which can be both fixed and variable) - Resource cost - Target utilization level - On floor constraints (like shift timings/duration, allowable overtime). These inputs parameters worked upon along with: - Historical data of workload (solution would typically need 3 years of historical data) - Customer prediction of upcoming volume (could be for next 1 month or quarter) Mathematical Models: The heart of the Capacity Planning solution is the Mathematical Model. Capacity Planning Solution uses industry standard mathematical models to predict future volume using historical volume. The solution supports five different forecasting models viz. 1. Time Series Decomposition 2. Single Exponential 3. Double Exponential 4. Triple Exponential (Winters Model) 5. Auto ARIMA (Auto Regressive Integrated Moving Average Model)

5 Figure 3: Volume Forecast based on Mathematical Models Volume forecast using all five models is generated however best fit model is recommended by the solution basis MAPE (Mean Absolute Percentage Error), AIC (Akaike information criterion) /BIC (Bayesian Information Criterion) parameters. Auto ARIMA model offers advantage that data elements are regressed and averaged to fit an approximation to any time series, thereby giving high accuracy of prediction. Volume forecast is generated at the granularity of 15 minutes interval throughout the day. This forecast can be downloaded in the form of spreadsheet. In addition to volume forecast, Human Capacity Planning solution also generates Shift Roster which tells number of human resources needed to finish incoming transactions while meeting SLAs. This Mathematical model executes the in two stages: - Forecasting - Optimization The Forecasting pertains to arriving at the resource count needed to manage the projected workload. The number of resources needed for the required time period can have granularity up to the minutes as per the required AHT to process the incoming workload. That is, forecasting algorithm can let us say recommend number of resources needed at 15 minutes interval if AHT/SLA for a specific tasks demand such fine grained projection. The Optimization following the Forecasting then works recommending how the resources should be in the working in the shifts taking into account all BPS shift related nuances (like shift index, shift start time, shift end time, breaks) in addition to nuances related to workload processing (like skills specific AHT, product/sub-product wise SLAs). The solution can generate shifts schedule on its own or can use predefined shits schedule. The Forecasting/Optimization is supported by built-in What-If Analysis component, which enables to

6 analyze impact of changes inputs parameters on generated roster. Case Study Human Capacity Planning for a Global Logistics Major A global logistics major has outsourced data entry operations for 13 countries. The business has lot seasonality impact due to which the staffing requirements keep on varying and it is imperative for offshore to plan and roster accordingly so that they do not carry excess or low staff than required. This criticality is compounded by a metric named cut-off; there are 165 cut-offs across the day that needs to be adhered to. Hence it was of paramount importance to identify a solution to this complex problem which would help the client meet its objectives. Inputs to the Human Capacity Solution: Figure 4: Input Data points to Human Capacity solution Outcome of Human Capacity Solution - The Capacity Planning Solution provides predicted volume at the granularity of 15 minutes for every type of transaction. Volume forecast can be used to identify monthly/weekly trends in the incoming volume and accordingly plan for infrastructure provisioning needed for each type of transaction.

7 Figure 5: Output of Human Capacity Solution which provides minute level granular volume details and resource (user) count Infrastructure Capacity Planning With 200 or more concurrent users do the data entry operations, the server response time is often degraded. The Human Capacity Planning helps to find out volume forecast and shift roster (number of users) at granularity of 15 minutes for a given transaction type. We can also find out volume demand for various application related resources looking at transaction types. All these combined are inputs for making efficient infrastructure planning such that IT applications would scale or descale depending on the volume and number of users. Benefits of Human Capacity Planning Solution In BPS industry, customer continuously expects service provider to optimize the operations efficacy, which essentially translates into do same with less and then do more with less. Qualitative Benefits 1. Effective Resource Management - The solution gives us the insight that for optimal human resource management understanding nuances of floor operations and projects number of resources required to fulfil the goal thereby provides salability to meet the high volume workload. For example, while managing the Human Capacity Planning, it is important to understand constraints like resource cap, acceptable number of overtime, number of shifts, resource shrinkage. When it comes to Work Allocation understanding dependencies on cross skilled resources, collaboration needed between the resources is important. Also when it comes to Queue Monitoring, how Maker/Checker headcount can be re-purposed to manage the time stringent SLAs. 2. Dynamic Change Management - The solution is focused on need of having specific resource at specific time to meet the SLA and thus to avoid any delays and inaccuracy in operations. Key decision making data points are made readily available to BPS floor managers at pre-defined interval enabling right time view, which helps in managing SLAs under varied workload scenarios. The solution addresses business problem such as systematic tracking of the resources, nurture the growth of resources and improve the overall value to the organization. 3. Risk Management While carrying out the BPS operations, when SLAs are not met then BPS service provider carries the risk paying penalties for missing the SLAs. More often than not this risk is linked to financial losses borne by the customer, and thus can be a huge amount. But at the same time, if SLA miss gets translated to loss of BPS customer reputation in the market place, then BPS Service Provider also has the risk of losing the business. Quantitative Benefits

8 Operational Efficiency - solution helped in 99.7 % SLA compliance leading to improved Customer satisfaction Typically 40 percent minimum improvement in Accuracy in Human Capacity Planning Typically 70 percent Time Savings Employee Satisfaction score improved from 5 to 9 (on a 10 point scale) Cost Saving The solution has helped in cost savings of USD 150 Millions Applicability and Future Roadmap: This solution is applicable wherever schedule and workload generation is required to manage high volume BPS operations with optimal utilization of infrastructure capacity. The solution adds value in cases where application response time is extremely important but at the same time there is no or limited control on server infrastructure to meet the varying workload capacity. Conclusion To be able to effectively estimate the infrastructure provisioning, the input from business environment are critical and that is where human capacity planning data points helps to predict the volume demands and number of users. The business inputs such as past history, skillset, peak times are leveraged using advanced analytics and mathematical models. The solution not only makes efficient and optimal use of human resources but hardware resources can gainfully be managed at the minute level granularity. The solution demonstrates that hardware or server capacity and performance is enhanced with right level of business inputs. References [1] Brockwell, Peter J., and Richard A. Davis, eds. Introduction to time series and forecasting. Vol. 1. Taylor & Francis, [2] Brusco, Michael J., and Larry W. Jacobs. "Optimal models for meal-break and start-time flexibility in continuous tour scheduling." Management Science (2000): [3] Caggiano, K. E., Jackson, P. L., Muckstadt, J. A., & Rappold, J. A. Efficient computation of timebased customer service levels in a multi-item, multi-echelon supply chain: A practical approach for inventory optimization. European Journal of Operational Research, 199(3), (2009) [4] Timothy Wood, Ludmila Cherkasova, Kivanc Ozonat, Prashant Shenoy. Predicting Application Resource Requirements in Virtual Environments, HP Laboratories Technical Report, [5] Waheed Iqbal, Matthew N. Dailey, David Carrera. Black-Box Approach to Capacity Identification for Multi-Tier Applications Hosted on Virtualized Platforms, Cloud and Service Computing (CSC), IEEE International Conference, 2011