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Resource Planning For Business Services

Over the past several decades mathematical models of supply chains have been developed and used for resource planning. Significant gains in supply chain efficiency have been attributed to the use of such models, together with the supporting IT infrastructure. Manufacturing resource planning (MRP), which automated the calculations of material requirements within manufacturing, evolved into enterprise resource planning (ERP), which monitors manufacturing enterprise processes and provides an information base for mathematically based advanced planning.
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Some supply chain problems are now considered solved; active research on other more complex problems continues. But virtually all deployments of advanced planning methods for supply chain use information derived from ERP systems. The use of both information systems and analytic tools in the business services industry lags behind the supply chain counterparts. ERP, Customer Relationship Management (CRM), and other classes of enterprise software developed for other segments can address some of the data management issues in business services. However, the lack of a standard method for representing the resource requirements for business services and the capabilities of the resources used to deliver business services makes it difficult to directly apply the analytic tools developed for manufacturing and supply chains to business services.

Key processes included in the planning of resources for business services include:

  • Forecasting demand for services (for example, the number, size, and content of major contracts);
  • Forecasting the time-phased demand for the resources used to produce the services;
  • Evaluating costs and determining constraints associated with the acquisition, training, and termination of resources;
  • Evaluating proposed allocations of resources to known forecasted activities; and
  • Pricing, including the specification of service level agreements, for business service contracts.

As with conventional goods, creating and delivering a business service requires the use of resources, whether capital assets such as IT infrastructure, consumable resources such as service parts and materials, labor hours by skilled employees, or intangible assets such as an individual’s skills or an organization’s proprietary data or processes. For conventional goods each unit of production is typically associated with a well-defined set of resource requirements: a bill of materials (BOM), processing time on a sequence of machines, and labor hours. These resource requirements are specified either with certainty or with well-characterized variability in product structure records. The MPR process uses the product structure data together with demand information for those production units sold as end products to generate time-phased requirements for each resource.

The information content and nomenclature used within MRP is standardized and well documented, with education programs and certification administered by professional organizations such as APICS. Of particular importance is the use of multiple levels or hierarchies in the product structure records: raw materials are combined to form subassemblies, subassemblies are combined with one another and additional raw materials to form higher-level subassemblies, and end products are assembled from subassemblies at various levels and additional raw materials. Other useful abstraction, such as feature ratios (frequency with which an optional part is included in a product), planning BOMs (which specify only key resource and planned usage rates which are refined as designs are finalized), offsets (which specify the time, relative to product completion, at which a resource is required) and usage dates (which specify the time period during which a resource can be used, and allow compact representation of engineering changes and equipment changes), facilitate the effective use of the massive amounts of data required to describe product production in resource planning processes.

In business services, the "unit of sales" is typically a contract describing business functions that will be done by a provider for a client over a specified period of time, a payment structure, and related obligations of the client and the provider. Exactly how the business functions will be provided, that is, what resources will be used and when they will be used, may not be specified. In addition, the quantity of business function may not be explicitly specified, especially when the payment terms reflect a "pay per use" structure.

It is often possible to decompose a business function in a hierarchical manner into a sequence (or linked collection) of process or tasks. A variety of business process modeling tools can be used to capture this decomposition. However, the information captured in these tools is not easily extracted for use in resource planning processes and may not include all of the factors, such as branching, rework, and manual interventions, that are required for effective resource planning. Significant work, including analysis of existing business services contracts and the corresponding business processes and subtasks, is needed to develop the appropriate data representations for multilevel services offering structures. However, such information is of indisputable value in resource planning.

It is also possible to specify a means by which resources can be used to perform each task. Project management software, spreadsheets, and task-specific tools such as IT modeling tools are used to capture this information. However, dedicating specific resources to specific tasks or clients can lead to low levels of resource utilization. Therefore a business service provider typically serves multiple clients from the same pool of resources. In the case of flexible resources, time-varying demand, and client tasks with differing resource requirements, determining the best mix and allocation of resources to meet service requirements is non-trivial. Mathematically based resource allocation models can be effectively used in cases where resource capabilities and availabilities are well specified.

Most business services, indeed, most services, involve a significant labor component. Effective labor resource planning requires defining the attributes used to categorize human capital, modeling the role of social capital, and analyzing the value of flexibility within organizations and workforces. IT systems for describing the skills, capability, and availability of labor resources are emerging, but have not achieved the level of maturity that MPR systems had two decades ago. Further, business services involving process outsourcing often result in the transfer of employees from a company to its supplier. Even if both parties utilized advanced workforce classification systems, it is unlikely they used the same classification system, thus complicating the representation of the capability of the new merged workforce.

Although one typically thinks of services as being delivered directly from the provider to the receiver, there is, in fact, a significant level of subcontracting in the services industry, and aggregators and distributors of service capability are emerging. The ability of a provider to effectively select and manage subcontractors would be significantly enhanced by some standard for describing a subcontractor’s capability and availability.

Many business services involve a significant degree of automation and/or financial tracking, which can be a source for valuable data that can be analyzed to find predictive relationships. Data analysis of labor hours reported in execution of software integration contracts has resulted in resource templates that can be used to accurately estimate the resource requirements of such projects as a function of contract value. These templates serve as a "bill of resources" that play an analogous role to a bill of materials in manufacturing, specifying the relative quantities of service production resources that must be applied in combination and over time to fulfill a unit of service consumption. However, to achieve their full potential, the bill of resources data must be augmented to capture some representation of variability, especially across individuals with varying experiences and skill sets

Because of the dynamic, time-sensitive nature of service production and consumption and the significant labor component of business service production, resource requirement variability—both in the number of service units and in the composition of individual service units—must be a consideration in any approach to supporting services decision processes. This is a critical aspect for successfully managing a business service enterprise, and a significant opportunity to apply quantitative modeling to gain competitive advantage.

In business services the set of resources associated with an offering typically has significant variability depending of the specific features of the transaction. Further, the ability to substitute one resource for another is generally higher in business services than in traditional manufacturing. Therefore information systems used to support resource planning for business services must represent the variability of task work content and delivery rates. Further, in some services businesses, the provider has some ability to influence the work content of tasks and/or the arrival rate of tasks through pricing, through response rate, and through the allocation of resources to individual tasks. Thus analytic models that capture these interactions would be valuable to both support execution decisions, and to make longer-term capacity planning decisions.

Just as supply chain simulation has been valuable in understanding the effects of variability, the value of information, and the potential impact of improved decision making, simulation models of the engagement process might be useful from the receipt of the request to proposal through the delivery of service and collection of payment, may provide significant insight into the value of information and process transformation in the business services industry.

For related reading, see [15, 16, 39, 62, 111].

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Author

Brenda Dietrich (dietric@us.ibm.com ) is senior research manager of IBM's Optimization Center, IBM Thomas J. Watson Research Center, Yorktown Heights, NY.

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DOI

10.1145/1139922.1139950

July 2006 Issue

Published: July 1, 2006

Vol. 49 No. 7

Pages: 62-64

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