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Specifying Information Systems for Business Process Integration – A Management Perspective1 Joerg Becker, Alexander Dreiling, Roland Holten, Michael Ribbert
University of Muenster Dept. of Information Systems
Leonardo-Campus 3 48149 Muenster, Germany
{isjobe|isaldr|isroho|ismiri}@wi.uni-muenster.de
Abstract Supply chain management and customer relationship management are concepts for optimizing the provision of goods to customers. Information sharing and information estimation are key tools used to implement these two concepts. The reduction of delivery times and stock levels can be seen as the main managerial objectives of an integrative supply chain and customer relationship management. To achieve this objective, business processes need to be integrated along the entire supply chain including the end consumer. Information systems form the backbone of any business process integration. The relevant information system architectures are generally well-understood, but the conceptual specification of information systems for business process integration from a management perspective, remains an open methodological problem. To address this problem, we will show how customer relationship management and supply chain management information can be integrated at the conceptual level in order to provide supply chain managers with relevant information. We will further outline how the conceptual management perspective of business process integration can be supported by deriving specifications for enabling information system from business objectives.
Keywords Business Process Integration, Supply Chain Integration, Supply Chain Process Management, Customer Relationship Management, Managerial Views, Business Objectives, Data Warehousing
1 Introduction In order to ensure customer satisfaction, knowledge about customers is vital for supply chains. In an ideal supply chain environment, supply chain partners are able to perform planning tasks collaboratively, because they share information. However, customers are not always able or willing to share information with their suppliers. End consumers, on the one hand, do not usually provide a retail company with demand information. On the other hand, industrial customers may hide information deliberately. Wherever a supply chain is not provided with demand forecast information, it needs to derive these demand forecasts by other means. Customer relationship management (CRM) thus provides a set of tools to overcome informational uncertainty.
Efficient supply chain management requires the integration of business processes between supply chain partners. As a result, supply chain process management becomes necessary, 1 This work has been funded by the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung), record no. 01HW0196.
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focusing on global supply chain processes. One of the main resulting managerial activities is the optimization of supply chain processes beyond the borders of participating companies (Hammer 2001).
Efficient optimization activities require information systems (IS), especially if they have reached the degree of complexity inherent in supply chain process management or customer relationship management. IS are vital for business process integration from an operative perspective, by enabling data exchange and integrated process flows between supply chain partners. On the other hand, IS support the managerial activities of monitoring and controlling supply chain processes. Decision support systems in particular are designed to assist managers in making better decisions (Todd, Benbasat 1999). In this context, IS are the enablers for creating competitive advantage (Johnston, Vitale 1988; Porter, Millar 1985). Because IS play such a central role, the perception, that IS form the vital backbone of an organization, instead of being simple business support tools (Henderson, Venkatraman 1999; Li, Chen 2001; Venkatraman 1994) has increased significantly since the so-called information revolution (Porter, Millar 1985).
Today, the development of information systems is faced with increased pressure from the business perspective. Ongoing discussions on the business value of IS (Hitt, Brynjolfsson 1996; Im, Dow, Grover 2001; Mukhopadhyay, Kekre, Kalathur 1995; Subramani, Walden 2001; Tam 1998) clearly point out that the risk awareness of IS development projects has changed. High costs and high overall failure rates of IS projects (Standish Group International 2001) have emphasized these discussions even more. Finally, a focus on IS project planning methods is needed, because inadequate IS project planning may lead to project failure. Keil states that a significant number of IT projects (30-40%) exceeding predefined time restrictions and allocated resources, but never reaching their objective, will ultimately escalate and fail (Keil 1995; Keil, Mann, Rai 2000).
From an IT perspective, a broad variety of methods, architectures, and solutions aim at supporting the IS development process (Hirschheim, Klein, Lyytinen 1995). As an example, data warehouse architectures are well understood and data warehouse projects have been conducted over a long period. Nonetheless, many data warehouse projects fail for several reasons (Vassiliadis 2000). Some reasons for failure of data warehouse can hardly be influenced, such as bad source data quality. Other reasons can be influenced during the project, such as the involvement of management as targeted users of the system or management support both of which contribute to system quality and system success (Wixom, Watson 2001). The high failure rate, especially of complex IS projects, indicates that some so-called best practices for IS development are inadequate. There is a continually increasing need for methodological approaches that are theoretically sufficiently well-founded to handle complex IS projects (Jiang, Klein, Discenza 2001).
The conceptual specification of information systems for business process integration from a management perspective, is an open methodological problem. Data warehouses support the management perspective technically, but their implementation is extremely costly. It is thus all the more important for the development of data warehouses to be effective and efficient. Effectiveness is achieved if the data warehouses support the desired managerial analysis. Efficiency targets the amount of necessary resources for development. The critical factor for a data warehouse project’s effectiveness is its value for future business. It is determined by the ability of a data warehouse environment to support essential managerial analysis. This paper aims at achieving effectiveness of data warehouses for business process integration. We introduce a specification approach for managerial views on business processes. These views are derived from business objectives, which are an essential output of managerial
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work. Approaches, focusing on managerial objectives as input for information systems specifications have been found useful within the domain of requirements engineering (Rolland, Prakash 2000). Our approach supports the conceptual management perspective of business process integration.
As an introduction to the topic, in Section 2, we first provide an overview of supply chain management and customer relationship management and outline how these concepts can be integrated. Conceptually, we focus on the impact of information sharing on supply chains and describe how estimated information can replace shared information, if it is not shared by single supply chain partners. Technically, we provide an overview of enterprise application integration (EAI) for integrating business processes along supply chains. For the technical integration of business processes from a management perspective, we introduce the concept of data warehousing and describe a framework for supply chain process management.
In Section 3, we introduce the MetaMIS approach for the specification of managerial views on business processes as a support tool of the conceptual management perspective. The problem of deriving MetaMIS specifications is targeted by the definition, decomposition, and transformation of business objectives into MetaMIS model constructs. We discuss the theoretical background of business objectives, integrate them into MetaMIS, and introduce an elaborate example of an objective system. Section 4 deals with transforming the objective system into MetaMIS specifications, which can be used to derive data warehouse structures. Finally, the findings are summarized and future prospects discussed.
2 Integration of Business Processes
2.1 Supply Chain Management The objectives of supply chain management are the design, operation and maintenance of integrated value chains, so as to satisfy consumer needs most efficiently by simultaneously maximizing customer service quality (Bechtel, Jayaram 1997; Christopher 1998; Hewitt 1994). SCM is currently accepted as a concept integrating inter-organizational business processes. In order to fulfill its objective, it must include other concepts such as efficient consumer response (ECR), quick response, continuous replenishment and customer relationship management (Bechtel, Jayaram 1997; Stadtler 2000). The design of supply chains requires the specification of business processes and supply-chain-wide planning routines. These specifications are imperative for the development of information systems which form the backbone of any supply chain integration (Miller 2001; Rohde, Wagner 2000). Information systems are widely perceived as the enabler for supply chain integration (Bechtel, Jayaram 1997; Hewitt 1994; Meyr, Rohde, Wagner 2000). Partners in a supply chain have to perform their activities in the most efficient way, by concentrating on their core competencies (Christopher 1998).
The Supply Chain Operations Reference (SCOR) model provided by the Supply Chain Council, is a reference model for structure, processes, and information flows within an inter-organizational supply chain (SCC 2001). The SCOR model contains measures for operational control and best practices for supply chain design. Five main processes characterize the SCOR model: Plan, Source, Make, Deliver and Return. The SCOR model is structured in four hierarchical levels. The main processes are defined at the top level (level one). At the second level, these main processes are clustered into process categories which depend on the underlying process model. There are three relevant business categories for the SCOR model at this level. These are "Make-to-Stock", "Make-to-Order", and "Engineer-to-Order". Additionally, at level two, some enabling processes are identified. The highest level of detail
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within the SCOR model is the third level, where each process category from level two is refined by inter-related process elements. The processes and their relationships are defined by means of tables. Level four is not covered by the SCOR model, since it would contain a detailed description of the internal business processes of the cooperating enterprises. As a result, the SCOR model needs to be extended by a framework adjusting internal and external business processes. This enables the alignment of an existing process infrastructure with inter-organizational processes that result from the SCOR approach.
Parallel to the SCOR model, several standardization initiatives have published documents that focus on the design of supply chain processes. The RosettaNet consortium, for example, is a non-profit group of more than 400 companies in the information technology and electronics domain. It aims at standardizing the trading networks between these companies, by providing standards for business documents such as purchase orders. Furthermore, so-called partner interface processes (PIPs), e.g. acknowledgement of receipt, serve the purpose of defining process interaction between trading partners (RosettaNet 2003).
2.2 Impact of Information Sharing on Supply Chain Management Information sharing is one of the basic supply chain management concepts. Evidence of the positive effects of information sharing can be found through various approaches, where savings are estimated in an information sharing supply chain environment using simulation models (Aviv 2001; Cachon, Fisher 2000; Gavirneni, Fisher 1999; Lee, So, Tang 2000). The focus of this section is not to quantify the effect of information sharing along supply chains and thus proving the effect, but to assume a positive effect and justify it with simple model-based explanations.
The integration level of material and inventory management and the structure of order costs are the main parameters of supply chain management (Christopher 1998). We illustrate this by using a simple model of inventory development and the effects of an integrated material and inventory management on order costs (see Figure 1). Two variables are relevant for calculating the economic ordering quantity. These variables are warehousing costs and fixed costs per order (in the interest of simplicity, costs per unit are assumed to be constant and will therefore not be considered; the results would be the same if discounts on certain order sizes were deducted). Warehousing costs increase linearly with increasing order quantities, since they are linked directly to the inventory level. Fixed costs per order decrease with an increasing order size, because fixed costs are spread over more units. The total cost function is the sum of these two functions as shown in the top left model of Figure 1.
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Effects of Reduced Ordering Costs
Effects of integrated Material and Inventory Management
Base Model
inventorylevel
time
safety stock
average levelordering level
delivery time
cost
order size X
total cost warehousingcosts
fixed costper orderper unit X
min total cost
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timedelivery time 1
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cost per order
min total cost 1
inventorylevel
time
safety stock 2
average level 2
ordering level 2
delivery time 2
ordersize
warehousingcosts
total costs 1
total costs 2
Source: (Holten et al. 2002).
Figure 1: Effects of information on material and inventory management and ordering costs
The development of inventory over time is shown in the top right model of Figure 1. A certain safety stock is required to guarantee production in cases of supply shortages. For a start, we assume stock above this level. Furthermore, we assume a linear consumption function over time. Based on a given delivery time, we can determine the reorder point for the economic ordering quantity.
It is important to understand that information itself has no direct business value. The effects of information on business are always indirect. Two relevant effects of improved information availability for the management of supply chain processes can be explained using the simple model in Figure 1. Firstly, information availability enables an enterprise to reduce the average stock level by reducing safety stocks and delivery times. Using information correctly, ensures that required materials can be delivered on time. This effect is based simply on the exchange of information between partners during the course of the business process (see the center model in Figure 1). If production planning systems of manufacturers and scheduling systems of suppliers are provided automatically with point-of-sale information from the retail partner, planning tasks can be performed with improved quality.
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This results in a reduction of safety stocks and delivery times, since delivery time entails not only shipment time, but also the time taken to organize the entire business transaction. Furthermore, delivery time can be influenced in make-to-order scenarios by the lag until production for an order commences and the time it takes to produce, pack, and deliver the products to the place the logistics partner can collect them. The structure of production, logistic and organization times can be optimized and decreased dramatically. The effects discussed so far, clearly provide a case for an integrated material management such as vendor managed inventory (VMI).
Secondly, information availability enables an enterprise to reduce the average stock level by increasing order frequencies. This effect is based on the duration of contracts between supply chain partners. Based on long-term agreements, the costs per order can be reduced, because some uncertainty for suppliers and manufacturers is eliminated.
In the simple model introduced in Figure 1, this results in reduced costs and a reduced optimal ordering quantity (see bottom left model in Figure 1). This implies increasing order frequencies, which is economically logical (see bottom right model in Figure 1). To benefit from this effect, which leads to a reduced average inventory level because of reduced order sizes, an integration of material and finance management is necessary.
Throughout the paper, we will use a consistent example to explain our concepts. Our example company is part of a supply chain which decided to decrease delivery times, in order to increase customer satisfaction. Products are directly shipped from company warehouses according to customer orders. The company stocks a small number of products and attempts mainly to produce just-in-time. An example of a managerial objective focusing on profiting from the positive effects of information sharing along supply chains is the following (the discussion on management objectives and their role in specifying MIS will be continued in more detail in Section 3):
• Objective Delivery Time Reduction of Business Unit Automotive Supplies: Decrease the average delivery time of all products of business unit Automotive Supplies to a maximum of 24 hours within the next year.
Most supply chains can potentially achieve higher customer satisfaction by reducing the delivery times of ordered products. The additional effort required to decrease the delivery times, can be justified with the savings from decreased stocks along the supply chain. The savings can be passed on to the customer, invested in improving customer service or in strengthening the supply chain. To decrease delivery times of ordered products, the efficiency of operative business processes along the entire supply chain needs to be increased. A major managerial responsibility is to define business objectives and undertake the necessary steps to deploy improved business processes. Furthermore, control mechanisms need to be implemented to monitor the degree to which business objectives have been reached.
2.3 Customer Relationship Management Every supply chain ultimately provides an end consumer with a product or service. The end consumer’s decision to buy or not to buy a product influences a supply chain’s economic success, and is thus the critical element. In the interest of the entire supply chain and especially the final partner interfacing with the end consumer, this decision needs to be influenced positively. Customer satisfaction can be addressed in several ways. In the 1990’s, a new strategic approach called relationship marketing evolved. Originating in the service or industrial marketing literature, relationship marketing focuses on the development and
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cultivation of long-term profitable relationships (Berry 1983; Grönroos 1994; Peck et al. 1999).
Following Payne, et al. (Payne et al. 1998), relationship marketing considers relationships “in every direction”. The customer relationship management approach, focuses only on profit-enhancing relationships with customers (Ahlert, Hesse 2002; Greenleaf, Winer 2002). Based on the notion of a customer life cycle (Ives, Learmonth 1984), a relationship can be seen as an investment, where, for example, customer relationship campaigns are conducted to achieve positive customer values at the end of the life cycle. Depending on the different phases of the customer life cycle, recruitment, retention, and recovery (Bruhn 2001), different needs of the customers occur and must be satisfied. A consideration of the special needs of customers, combined with individualized marketing campaigns, leads to higher sales (Gillenson, Sherrell, Chen 1999; Stone, Woodcock, Wilson 1996) and increased retention of existing customers (Buchanan, Gilles 1990). Keeping existing customers is about five times more profitable than finding new ones (Buchanan, Gilles 1990; Reichheld 1996). Modern IS, using large amounts of customer data, enable CRM and one-to-one marketing on a mass scale (Gillenson, Sherrell, Chen 1999).
Deriving knowledge about customers is one of the main challenges confronting analytical CRM. Usually, customer buying behavior is analyzed to forecast potential products, points of time, or quantities of future orders. This knowledge is used mainly by companies in order to provide customers with what they require at a given time and place. Furthermore, this knowledge is useful for manufacturing industries, because they can adjust their product development to market requirements. In turn, this may lead to decreased delivery times, which as pointed out above, contributes potentially to customer satisfaction.
2.4 Impact of CRM Information on SCM As indicated above, the design of supply chains requires the specification of supply-chain-wide planning routines as a special component of the development of information systems. The concept of advanced planning incorporates integrated supply chain planning as a core concept. Advanced planning systems (APS) support this integrated planning task (Rohde 2000). Demand planning data and demand fulfillment data at the sales stage, is fed back into distribution planning and transport planning at the distribution stage. Ideally, industrial customers are able to provide their suppliers with precise demand information, obtained from collaborative forecasting with their industrial customers. Unfortunately, end consumers generally do not provide retail companies with demand information, and some industrial customers are unable or unwilling to provide demand information.
Missing demand information within supply chains, prohibits the notion of integrated supply chains. To compensate for potential losses which arise from non-integrated supply chains, CRM information can substitute missing demand information to a certain degree. For example, if the component supplier is neither able nor willing to share demand data, business processes between the component and part supplier need to be optimized using CRM methods initiated by the part supplier.
At the retail stage, CRM provides a set of tools for increasing the forecasting quality of retailers. Using CRM information within the supply chain, potentially maximizes the end consumer’s satisfaction in several respects. Firstly, efficiently derived high-quality forecasting information shared with suppliers enables stock and cost reductions within SCM. Secondly, the out-of-stock problem can be reduced through higher demand data quality, given that demand will be satisfied. Thirdly, the bullwhip effect resulting from non-stationary demands can be reduced if the entire supply chain is provided with high-quality demand data
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for the final supply chain partner. If the quality of forecasted customer demand is sufficiently high as to almost correspond with actual demand, the effects of sharing this information will be as they have been proven for information sharing supply chains (Aviv 2001; Cachon, Fisher 2000; Gavirneni, Fisher 1999; Lee, So, Tang 2000).
Apart from customer-related information gained by CRM, market-related information is required within the strategic planning of a supply chain. Market research is a tool for decreasing the risk of marketing and resulting product development decisions (Proctor 1997). Whereas CRM focuses on forecasting the demands of known customers, market research provides information on markets. Both CRM information and market research information need to be provided to the preliminary supply chain, in order to increase the quality of operative demand-planning processes. For this purpose, CRM should be applied to industrial customers within the supply chain. Additionally, market research is required at every stage of the supply chain. Figure 2 contains the information flows necessary to implement this concept.
Part Supplier ComponentSupplier
ProductAssembler End ConsumerBasic Material
SupplierRaw Material
Supplier
End ConsumerMarket
ComponentMarket
PartsMarket
Basic MaterialMarket
Raw MaterialMarket
Supply Chain InformationCRM Demand InformationMarket Research
Legend
Figure 2: Supply Chains and Markets
In Section 2.2, an example of a managerial objective has been introduced, that aims at profiting from the positive effects of information sharing along supply chains. The definition of the objective remains constant in a non-information sharing environment. If the customers of business unit Automotive Supplies are end consumers, they will not share demand information with our example company. Demand information may also be not available, if industrial customers are unable or unwilling to share demand information. In any event, the company needs this demand information and therefore needs to implement forecasting analysis tools. The set objective of decreasing delivery times, which are affected partially by the time required to proceed with the customer order, its production, packing, shipment, is still that of decreasing delivery times. The difference lies in the application of methods of analytical CRM, instead of information sharing as a basic concept of SCM at the operative level.
2.5 Technical Integration of CRM and SCM Data The integration of supply chain management and customer relationship management can be assisted by efficient information systems and information technology. In order to support the
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integration of SCM and CRM, the implementation of IS needs to target the operative as well as the management perspective of the integrated concepts.
From an operative perspective, enterprise application integration aims at combining IS of business partners by transmitting data between them (Buhl, Christ, Ulrich 2001). Even if these IS are highly heterogeneous, the data in exchanged documents must not be changed, misinterpreted, or lost. Data exchange is facilitated by schema matching mechanisms. Several protocols and document standards are used for EAI purposes, such as XML or EDIFACT. Software products such as Microsoft’s BizTalk Server, provide a software platform for exchanging business documents. Technically, data can be exchanged using the Internet.
From a management perspective, data warehouses provide an accepted architecture for the development of decision support systems. A data warehouse stores materialized views on relational representations of business processes, in order to provide relevant information for managerial decisions (Inmon 1996; Inmon, Hackathorn 1994; Inmon, Welch, Glassey 1997). The warehouse is the central layer of a theoretically ideal three-layer architecture connecting online transaction processing (OLTP) systems and components enabling online analytical processing (OLAP) (Becker, Holten 1998; Chaudhuri, Dayal 1997). Contributions within the field of data warehousing range from technical discussions of databases and algorithms enabling OLAP functionality (Agarwal et al. 1996; Cabibbo, Torlone 2001; Codd, Codd, Salley 1993; Colliat 1996; Gyssens, Lakshmanan 1997; Vassiliadis, Sellis 1999) to studies on the information search behavior of managers (Borgman 1998) and to papers concentrating on methodologies for information systems development (Golfarelli, Maio, Rizzi 1998). Recently, methodological contributions (Jarke et al. 1999; Jarke et al. 2000) propose a quality-oriented framework for data warehouse development. OLAP supports adequate navigation for the purpose of managerial analysis, through so-called multi-dimensional information spaces. Business process data from OLTP systems are the source of OLAP analyses. Typically, the integration of OLTP systems and a data warehouse is based on tools performing extraction, transformation, and loading tasks (ETL) on the source data (Inmon 1996; Widom 1995).
At an intra-organizational level, business-supporting information systems produce data about business transactions. For the purpose of CRM and logistical optimization (as the intra-organizational fundament of inter-organizational SCM), this data can be analyzed, enabling analytical CRM and logistic optimization. Due to the fact that this data is encoded in various operational data sources, there needs to be an integrating layer to derive relevant managerial information from these data sources. This can be achieved by local data warehouses. From these data warehouses, several management reports can be generated, which support corresponding managerial activities.
To support supply-chain-wide information sharing, data from operational data sources of single supply chain partners, need to be integrated in a supply-chain-wide data warehouse. This data warehouse then serves as a basis for generating managerial reports at the inter-organizational as well as intra-organizational levels. Figure 3 shows an architecture enabling the integration of inter-organizational and intra-organizational data for the purpose of supply chain process management analysis.
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Figure 3: Technical Integration of CRM and SCM Data
The architecture serves analytical CRM as well as analytical SCM, implying operational CRM and SCM components within the local information systems environments. Thus, the management reports at the inter-organizational and intra-organizational levels, contain CRM and SCM information, enabling the optimization of business processes from both perspectives.
Even if the data warehousing architecture is well understood, data warehouse success is linked directly to its additional use for the business. Additional use can be achieved by supporting the process of providing essential managerial analysis more efficiently than in the past. For this reason, the development of data warehouses needs to be supported from a conceptual perspective, an unresolved methodological problem, which is considered in the next section.
3 Business Objectives and Managerial Views on Business Processes As depicted in Section 2, supply chain management and customer relationship management enable the integration of business processes at a conceptual level. SCOR and RosettaNet are approaches for implementing supply chains from an operative perspective, supporting processes of trading networks. From a technical perspective, business process integration is targeted by several communication protocols, architectures, concepts, and software products.
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As one of the concepts, EAI aims at integrating information systems of business partners. From a management perspective, data warehousing provides an accepted architecture for managerial views on business processes. Data warehouses can be used for managerial intra-organizational as well as inter-organizational analysis.
Even if the introduced concepts are well-understood by researchers and practitioners, there remain resolved methodological problems. Besides the operative and technical perspective, business processes have to be integrated from a conceptual management perspective. For this purpose, in this section we discuss the specification of managerial views on business processes with MetaMIS. Furthermore, we provide a detailed introduction to the definition of business objectives, which will be integrated into MetaMIS to create MetaMIS models. Finally, a detailed example is introduced, consisting of an objective system. The objectives will be decomposed to their defining components, which will be transformed into MetaMIS models in a comprehensive discussion in the next section.
3.1 Specification of Managerial Views with MetaMIS From a conceptual management perspective, the MetaMIS approach aims at specifying managerial views on business processes (Holten 2003). The MetaMIS approach is anchored by a meta model featuring several concepts necessary to define a specification language for managerial views on business processes and activities (Becker, Holten 1998; Holten 1999; Holten 2003). MetaMIS models feature a degree of formality, which allows for deriving data warehouse structures from these models (Holten 2003). The MetaMIS approach has been validated at the Swiss reinsurance company Swiss Re, where the managerial activity Group Performance Measurement has been modeled (Holten, Dreiling, Schmid 2002).
MetaMIS commences with the definition of dimensions (concept Dimension). Dimensions are defined by hierarchically-ordered dimension objects (concept Dimension Object), e.g., products, customers, points in time, or customer sales representatives. Based on the enterprise theory of Riebel (Riebel 1979), dimension objects can be understood as entities subject to managerial analysis. In order to prevent information overflow, subsets of existing dimensions (dimension object hierarchies) need to be defined. For this purpose, dimension scopes and dimension scope combinations are introduced (Holten 1999; Holten 2003; Holten, Dreiling, Schmid 2002) (concepts Dimension Scope and Dimension Scope Combination). Dimension scopes are sub-trees of dimensions. Dimension scope combinations comprise dimension scopes, creating navigation spaces for managerial analysis. Dimension scope combinations define a space of multi-dimensional objects. Referring to Riebel’s enterprise theory, the concept Reference Object denotes vector types within this space. Reference objects are “measures, processes and states of affairs which can be subject to arrangements or examinations on their own” (Riebel 1979, p. 869).
The next concept required is Aspect. Aspects can be either qualitative (concept Qualitative Aspect) or quantitative (concept Quantitative Aspect, Synonym to Ratio). Management ratios are vital for specifying information in management processes. They belong to the class of interval or ratio measures (Adam 1996; Hillbrand, Karagiannis 2002; Holten 1999). Ratios are core instruments for measuring the value of companies (Copeland, Koller, Murrin 1990), the business performance (Eccles 1991; Johnson, Kaplan 1987; Kaplan, Norton 1992; Kaplan, Norton 1996; Kaplan, Norton 1997; Lapsley, Mitchel 1996) and for analyzing the financial situations of enterprises (Brealey, Myers 1996). Synonyms found in the management accounting literature are, e.g., operating ratio, operating figure, performance measure. Ratios like “gross margin” define dynamic aspects of business objects and have clearly specified meanings. Their calculation is defined by algebraic expressions (e.g. “profit
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= contribution margin – fixed costs”). Qualitative aspects can be used, if business facts are measured by categorical values, such as efficiency or quality (Becker, Dreiling, Ribbert 2003). They belong to the class of nominal or ordinal measures.
Aspects are organized into aspect systems (concept Aspect Systems). Aspect systems are structured hierarchically according to an aspect’s importance for a managerial analysis. A drill-down logic is implied for aspect systems, which is to be separated especially from an algebraic definition of ratios. Aspect systems are assigned to dimension scope combinations (navigation spaces), in order to create business facts (concept Fact), such as the number of products sold in a certain region by a specific customer sales representative or the turnover achieved with one customer. Business analyses usually require a comparison of business facts. In order to conduct such dynamic analyses, fact calculations can be defined, involving a variable number of business facts which are processed according to calculation expressions (Holten, Dreiling 2002) (concept Calculation Expression). Examples of fact calculations are the profit-growth rate of a business group or the variance between planned and actual turnover of a product group. Dimension scope combinations, aspect systems, and fact calculations are combined into an Information Object. Thus, it is a relation between a set of reference objects and a set of aspects with the element types being business facts. Figure 4 shows a segment of the meta model underlying the MetaMIS approach.
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Fact
Combined ReferenceObject
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<Identifier> Entity Type
<Identifier> Relationship Type
<Identifier>ReinterpretedRelationship Type
Specialization (Types: - u unequivocally, e equivocally - t total, p partial)
(min,max)Connector ( - min minimum cardinality, - max maximum cardinality)
Source: (Holten 2003).
Figure 4: Segment of the MetaMIS meta model
An unresolved methodological problem is how the crucial modeling constructs such as dimensions, dimension scopes, dimension scope combinations, aspects, and information objects are derived. In the next section, we will show how business objectives can be used to derive MetaMIS structures together with personnel from various business domains. This closes the loop from defining business objectives to monitoring if they have been accomplished.
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3.2 Objectives from a Business Perspective The designer of an information system for managerial analysis needs to know which managerial analysis the systems must to support, information that only managers or management supporters can provide. To assist the complex process of obtaining information requirement models, e.g., MetaMIS models (Holten 1999), we will show, how managers’ information requirements can be derived from corporate objectives. With respect to the integration of objectives into the MetaMIS approach, we discuss several objective types found in the literature. Parallel to this, we introduce meta model concepts for the different objective types in order to construct a meta model of objectives. The base concept introduced is Objective.
According to Porter (Porter 1979), the most abstract and general business objectives are defined in a business strategy. A business strategy deals with defending and strengthening a competitive business position. It must focus on five contending forces (Porter 1979), which are threats of entry, powerful suppliers, powerful buyers, substitute products, and jockeying for position. Based on the identification of these forces, the company is able to define its strengths and weaknesses. Knowing the strengths and weaknesses, a strategy can be formulated consisting of the three major aspects of positioning the company within the industry, influencing balance and forces, and exploiting industrial changes. Following Porter (Kotler 1999; Porter 1996), strategic positioning targets performing different activities than competitors or performing similar activities of competitors in different ways. Operational effectiveness is achieved, if activities are performed better than the ones of competitors. Clearly defined strategic objectives and operational effectiveness are essential to superior performance and long term profitability (Porter 1996). In order to take strategic objectives into account for constructing our objective meta model, we divide Objective into two specializations of which one is the entity type Strategy, General Conditions, and Guidelines.
Hierarchically structured objectives can be represented as a pyramid, where the degree of measurability increases towards the bottom (Steiner 1969). Three different hierarchical levels form the top of the pyramid (strategy or general conditions). These are business mission (Meffert 2000), corporate identity (Birkigt, Stadler, Funck 1993), and policies and practices (Ansoff et al. 1990). Following this categorization, we introduce the meta model construct Business Mission, Corporate Mission, and Policy and Practice.
A major difficulty of business strategies is their non-operational character. Operational objectives are defined by a certain measure, level, time frame, and reference (Adam 1996). Objectives need to be defined operationally in order to be manageable (Latham, Kinne 1974). Usually, business strategies are not measurable. Nonetheless, operational effectiveness requires the definition of operational objectives. In order to align business strategy and operational effectiveness, the business strategy needs to be broken down into operational objectives in several steps. In the words of Porter “the essence of strategy is in the activities” (Porter 1996), which means that operational objectives enable management to do the right things (defined by the business strategy) right (by derived operational objectives).
Types of operational objectives that can be derived from business strategies are general goals, organizational unit goals, business unit goals, and marketing-mix-based goals. General goals specify aggregated operational objectives. They can be seen as benchmarks, which help managers from different organizational units to specify their objectives, such as revenue or cost on a corporate level (Kupsch 1979). Organizational unit goals specify general goals at an organizational unit level. Examples are planned production department costs or planned sales department revenues (Meffert 2000). Business unit goals break down organizational unit goals to the business unit level. Marketing-mix-based goals further split
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up business unit goals into, e.g., planned prices, promotions, places, and products. Operational Objective is introduced to the objective meta model as the second specialization of Objective. Both existing specializations are unequivocal and total, meaning that every objective either has an operational or a strategic character. Operational Objective is devided unequivocally and totally into the specializations General Goal, Organizational Unit Goal, Business Unit Goal, and Marketing-Mix-Based Goal.
The Balanced Scorecard is another approach that breaks down general business objectives into operational ones (Kaplan, Norton 1992). The BSC is a top-down approach that provides managers with a comprehensive framework, translating a company’s strategic objectives into a coherent set of performance measures (Kaplan, Norton 1993). Four different perspectives are provided. Information about traditional financial measures are enhanced by measures of customer performance, internal processes, and innovation and improvement activities (Kaplan, Norton 2000). Thus, the BSC enables balancing between external measures such as income or revenues and internal measures like product development and learning (Kaplan, Norton 1993). Furthermore, the BSC shows cause-and-effects links, which avoid trade-offs among different success factors.
For constructing the objective meta model we need to structure objectives. Objectives can be organized hierarchically. Each objective can be part of more than one hierarchy, which leads technically to an Objective Structure as a relationship type connecting Objective with itself. We can furthermore add specializations, e.g., the categorization of Objective into the more commonly used terms Strategic Objective, Tactical Objective, and Operative Objective. The introduced specializations, however cannot be regarded as an exhaustive list of possibilities. Other specializations may exist beyond theses introduced. Depending on the modeling purpose they can be specified. Figure 5 contains the meta model constructs that have been introduced above.
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u,t StrategicObjective
TacticalObjective
OperativeObjective
u,t Business Mission
Corporate Identity
Policy andPractice
Objective
u,t
OperationalObjective
Strategy,General Condition,
and Guideline
u,t General Goal
Organizational UnitGoal
Business UnitGoal
Marketing-Mix-Based Goal
ObjectiveStructure
(0,m)
(0,m)
Legend
<Identifier> Entity Type
<Identifier> Relationship Type
<Identifier>ReinterpretedRelationship Type
Specialization (Types: - u unequivocally, e equivocally - t total, p partial)
(min,max)Connector ( - min minimum cardinality, - max maximum cardinality)
Figure 5: Specializations of Objective including Objective Structure
Objective systems, especially large ones, face a major problem: they are usually inconsistent, which means that achieving one objective, inevitably leads to the failure of another. The inherent problem, as to how strategies are formed in organizations, is targeted by major research projects in the management research community (Allison 1971; Ansoff 1965; Barbuto Jr. 2002; Barnard 1938; Granger 1964; Mintzberg 1973). However, we do not aim to support the definition of consistent objective systems. In fact, we assume that inconsistencies can be overcome by the approaches presented in the literature. We do support the monitoring of given objectives by comparing them to actual business developments.
3.3 Integration of Objectives into MetaMIS Thus far, the MetaMIS approach for the specification of managerial views on business processes has been introduced, followed by a discussion on business objectives. To integrate business objectives into MetaMIS, the set of meta model constructs introduced in the last section, needs to be extended and connected to already existing MetaMIS modeling constructs.
In contrast to non-operational objectives, operational objectives can be integrated into MetaMIS, because their defining components measure, time frame, reference, and level can
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be transformed into MetaMIS constructs. In order to measure an objective, we introduce the construct Objective Measure. Different objectives may have different objective measures. Financial ratios like earnings or costs are represented by the construct Quantitative Measure. Qualitative aspects such as efficiency or quality are subsumed by the construct Qualitative Measure. Quantiativ and qualitative measures are generalized into the construct Aspect.
Operational objectives need to be achieved within a certain time frame, e.g., one year. Furthermore, operational objectives consist of another mandatory component, a reference. Every objective must refer, for instance, to a product, product group, service, customer, or management unit. The construct Reference Object represents both time frame and objective reference as required components for defining operational objectives.
Finally, the construct Objective Level is necessary to define a quantitative or qualitative level to which the objective has to be accomplished. The objective level combines an objective measure with a reference object. Having defined the objective measure, e.g., average delivery time and a reference object such as ‘business unit Automotive Supplies, any product’, we have to set the average delivery time of any product of the business unit Automotive Supplies to a value of, e.g., 24 hours. The meta model consisting of the introduced constructs and their relationships is shown in Figure 6.
OO-OL-AS
Reference Object
Objective MeasureQuantitative Aspect(Ratio)
Qualitative Aspect
u,t
(0,m)
ObjectiveLevel
(0,m)
(0,m)(0,m)
Objective
u,t
OperationalObjective
Strategy,General Condition,
and Guideline
ObjectiveStructure
(0,m)
(0,m)
Legend
<Identifier> Entity Type
<Identifier> Relationship Type
<Identifier>ReinterpretedRelationship Type
Specialization (Types: - u unequivocally, e equivocally - t total, p partial)
(min,max)Connector ( - min minimum cardinality, - max maximum cardinality)
Figure 6: Objective Meta Model
MetaMIS already contains the constructs Reference Object, Quantitative Measure, and Qualitative Measure (see Section 3.1). The decomposed objective references will be transformed into dimension objects (see Figure 4) which will constitute dimensions. Thus, we
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derive an initial set of information on the construction of navigation spaces for management analysis, which will be discussed in more detail in Section 4.
3.4 Defining and Decomposing Business Objectives – A Sample Case After we discussed the definition of operational objectives consistent with a business strategy, described a way to decompose them into their defining components, and structured these components within a model, we now introduce a comprehensive case. The main objective has been introduced in Section 2.2 and is consistent with the general goal of long-term profitability according to the management approach CRM:
• Objective Delivery Time Reduction of Business Unit Automotive Supplies: Decrease the average delivery time of all products of business unit Automotive Supplies to a maximum of 24 hours within the next year.
The time frame for the objective is next year. Furthermore, it refers to all products of business unit Automotive Supplies. The time frame combined with the reference constitutes the reference object. Average delivery time is a quantitative measure. If a time value (not a time frame) is assigned to the reference object, this value becomes a business fact. Since delivery time is composed of production time and shipment time, the objective is broken down into two sub-objectives. The first sub-objective has been set as follows:
• Objective Increase Production Efficiency: Increase production efficiency at assembly line V8 engine in factory alpha from level 8 to level 9 within the next year.
As in the case of the main objective, the time frame is next year. The reference of the objective is assembly line V8 engine in factory alpha. Efficiency is a qualitative measure, which can be expressed by the values (categories) zero to ten. The efficiency categories can be calculated by algorithms, which consider various influencing variables or are derived by an auditing process, where trained personnel set the efficiency based on their observations. The second sub-objective to decrease delivery times refers to the shipping efficiency:
• Objective Increase Shipping Efficiency: Increase shipping efficiency of products shipped out of factory alpha by any logistic partner from level 8 to level 9 within the next year.
The time frame again is next year. It refers to factory alpha, any logistic partner, and any product and is measured by the qualitative measure efficiency. Both sub-objectives are measured qualitatively. In order to derive the efficiency measures for both sub-objectives deterministically, each is split up again into three sub-objectives. Production efficiency is described by the following objectives:
• Objective Rejection Rate Reduction: Decrease the average rejection rate of product group Original Equipment – Engines products at assembly line V8 engine in factory alpha from 0.4 to 0.2 percent within the next year, without increasing the rejection rate of other product group's products assembled at this line,
• Objective Machine Defect Rate: Decrease average machine defect rate of machines at assembly line V8 engine in factory alpha from 0.7 class A defects per week to 0.3 within the next year,
• Objective Lead Time Reduction: Achieve an average lead time reduction during production of any single product of product group Original Equipment – Engines at assembly line V8 engine in factory alpha from 256 minutes to 240 minutes within the next year.
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On the other hand, shipment efficiency is broken down into these three objectives:
• Objective Decrease Just-In-Time Deviation of Logistic Partners: Decrease the average just-in-time deviation of any logistic partner for any product shipped from factory alpha with an appropriate transportation to five minutes within the next year (Just-In-Time deviation is the time difference between planned and actual collection of a customer order by a logistics partner),
• Objective Decrease Packing Time: Decrease the average packing time of factory alpha warehouse workers for any customer order to one hour within the next year,
• Objective Reduce Warehousing Costs: Reduce the total costs of factory alpha warehouse to € 500,000 within the next year.
These six objectives can each be decomposed into their defining components. Table 1 gives an overview of the entire objective system by decomposing each objective to time frame, reference, measure, and level.
Main Objective Sub-ObjectiveLevel 1
Sub-ObjectiveLevel 2 Reference Time
Frame Measure Level
business unit automotive supplies, any product next year average delivery
time 24 hours
assembly line V8 engine, factory alpha next year efficiency 9
Rejection Rate Reduction
products of product group Original Equipment - Engines, assembly
line V8 engine, factory alpha next year average rejection
rate 0.2 percent
Machine Defect Rate
machines, assembly line V8 engine, factory alpha next year average machine
defect rate0.3 class A
defects per week
Lead Time Reduction
single products of product group Original equipment, assembly line
V8 engine, factory alphanext year average lead time 240 minutes
factory alpha, any logistics partner next year efficiency 9
Decrease Just-In-Time Deviation of Logistics Partners
factory alpha, any logistics partner, product next year average just-in-
time deviation five minutes
Decrease Packing Time
factory alpha warehouse workers, customer, order next year average packing
time one hour
Reduce Warehousing
Costsfactory alpha warehouse next year total costs 500,000 €
Delivery Time Reduction of Business Unit Automotive Supplies
Increase Production Efficiency
Increase Shipping Efficiency
Table 1: Operational Objective Components
The defining components of decomposed operational objectives are structured according to the model constructs introduced in Figure 6. In order to monitor the degree to which an objective has been accomplished, operational objectives need to be transformed into plan scenarios. After the end of the planning period has been reached, deviation analyses help to compare these plan scenarios to the actual business development. The next section shows how objectives can be transformed into plan scenarios.
4 Deriving MetaMIS models from Business Objectives
4.1 Constructing Dimensions Having defined operational objectives and structured them hierarchically, we are now able to create a conceptual model of the information system supporting managerial analysis. We first
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need to define dimensions which consist of hierarchically-structured dimension objects. As a first step, the initial set of objective references taken from the definitions of operational objectives can be decomposed. The objects of the Reference column in Table 1 represent such decomposed objective references, which will be redefined as dimension objects and structured hierarchically. They thus form the basic structure of what will be a dimension. This process is complex creative work. Even so, without a methodological approach such as the one presented here, no assistance with this process would be available.
Questioning managers on basis of the specified operational objectives is imperative for deriving further insights into the structures of the information systems supporting managerial analysis. Our example objective Rejection Rate Reduction states that rejection rates of other product group’s products must not increase. This inevitably leads to the question as to which other product groups should be considered for managerial analysis. The plan scenario that needs to be set up, will include the objective level of the product group Original Equipment – Engines, which needs to be decreased according to the objective. Furthermore, it includes the objective levels of all other product groups which must not exceed the respective levels from the previous year.
The identification of dimensions can be assisted by answering the question of whether the elements of operational objective references are structured in an n:m relationship or in a 1:m relationship. The first case implies the modeling of two dimensions (because dimensions are hierarchical constructs of dimensions objects) whereas in the latter case, only one dimension is modeled. This decision needs to be made carefully. It needs to be identified whether this 1:m relationship occurs only temporarily, just as objective references of operational objectives, or generally. If it occurs generally, it is imperative to know, if the relationship might be changed by an ongoing business strategy. As mentioned above, identifying dimensions is a complex process which directly influences data warehouse structures. It can be seen as a strategic decision during the MIS specification process.
In our example objectives from Table 1, there are eleven types of fundamentally different entities, business units, assembly lines, warehouses, factories, product groups, workers, products, logistic partners, time entities, orders, and customers. Now, does an assembly line always belong to one factory or can it be spread over more than one factory? Is it possible that a factory runs more than one assembly line? Do workers work in one factory (at one assembly line) or are they allocated to more factories (assembly lines)? Is a product always assigned to exactly one product group? Questions like these have been made possible by the definition of operational objectives with the proposed method. They need to be answered by responsible personnel from business domains to specify the management-supporting information system.
Implying 1:m relationships between business units, product groups, and between product groups and products, these three different entity types can be aggregated within one dimension Product. If, furthermore, all other entity types are bound by n:m relationships, each will be structured in one dedicated dimension. Only warehouses and assembly lines have been aggregated within one dimension, because allowing analysis between these entity types would serve no purpose.
To distinguish plan scenarios from actual business developments, we need the dimension Version. Version is a dimension consisting of the dimension objects Actual, and several plans such as Plan, Plan optimistic, Plan pessimistic, or Forecast. Due to the fact that we transform objectives into plan scenarios to compare them to future business development, we need to add a dimension object of Version to each business fact. If it is a planned fact, a reference to a plan-version is necessary. In case of actual business facts, the Version dimension object
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Actual is referenced. Deviation analysis later compares business facts that differ only in the reference component of the dimension Version. Figure 7 contains all dimensions necessary to build the MIS environment, which allows for the managerial activity monitor delivery time.
Product
Original Equipment
Replacement
OE Engines
OE Chassis Components
OE Electronic Components
Electronic Components
Engine Parts
Electronic Parts
Automotive Supplies
Industrial Supplies
Services
Production and Storing Facilities
Assembly Line V8 Engine
Assembly Line Chassis Components
Machine V8 - JH7765K
Machine V8 - HJG5RF4
Personnel
Assembly Line V8 Engine Foreman
Machine V8 - JH7765K Foreman
Workplace 1
Workplace 2
Assembly Line Chassis Components Foreman
Machine V8 - HJG5RF4 Foreman
Factory
Factory Alpha
Factory Beta
Factory Alpha Warehouse Foreman
Time by Month
January 2004
February 2004
Legend <dimension identifier>
<non-opened non-leaf dimension object identifier>
<opened non-leaf dimension object identifier>
<leaf dimension object identifier>
Order
Order 0000001
Order 0000002
Logistics Partner
Partners for Engines
Partners for Chassis Components
Customers by CRM Class
Class A Customers
Class B Customers
Factory Alpha Warehouse
Version
Plan
Actual
Figure 7: Set of dimensions for managerial activity monitor delivery time
After the identification of dimensions, their basic structure of dimension objects which have been derived from operational objectives needs to be completed. Other dimension objects that will further be necessary to answer the managers’ questions, need to be added. Basically, this means that all relevant products of all product groups (product group Original Equipment – Engines and all others obtained from the answer to the question derived from the operational objective Rejection Rate Reduction) are added to the product dimension. In this case, the product dimension would be extended by the products, product groups, and business units shown in Figure 7. This procedure needs to be repeated for every identified dimension.
4.2 Constructing Navigation Spaces for Managerial Activities The definition of business objectives first needs to be followed by the managerial activity of undertaking the necessary steps to implement improved business processes. Secondly,
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management must monitor the degree to which the defined business objectives have been achieved. To address the problems of information overflow and information misuse, we need to define dimension scopes for specific managerial monitoring activities.
To monitor the objective Increase Production Efficiency introduced in Section 3.4, we need to define six dimension scopes. As time can be limited to all time dimension objects of the sub-hierarchy 2004, the first dimension scope Time by Month Year 2004 consists of all days and months in 2004 and the year 2004 itself. All other time entities are blanked out. Five more dimension scopes are built similarly. The dimension scope Factory Factory Alpha reduces all factories of the dimension Factory to Factory Alpha, Product Product Group Automotive Supplies – Original Equipment – OE Engines focuses on engines, and Production and Storing Facilities Assembly Line V8 Engine reduces the total set of warehouses and assembly lines to assembly line V8 Engine.
Version is reduced to Plan in one dimension scope (Version Plan) and Actual in another (Version Actual), which allows for comparing the business facts based on these two valuations. The dimension scope combination Production Efficiency joins all six dimension scopes. It creates a navigation space for the required information of the managerial monitoring activity corresponding to the objective Increase Production Efficiency. This navigation space consists of all combined reference objects which are necessary to monitor the objective Increase Production Efficiency itself, and all of its sub-objectives once the respective qualitative and quantitative measures have been assigned to them. The dimension scope combination features two hierarchy levels. To create a combined reference object, one dimension object of each dimension scope of the first hierarchy level needs to be selected. Version is split up into two dimension scopes, which means that one of its dimension scopes needs to be picked for the valuation of business facts. This is necessary, because no information would be aggregated from the versions, Actual and Plan (Holten, Dreiling 2002; Holten, Dreiling, Schmid 2002). Figure 8 contains the dimension scopes and the dimension scope combination for the managerial activity monitor production efficiency.
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Time by Month Year 2004
January 2004
February 2004
2004-01-01
2004-01-02
December 2004
Factory Alpha
Factory Factory Alpha
Assembly Line V8 Engine
Machine V8 - JH7765K
Machine V8 - HJG5RF4
Production and Storing Facilities Assembly Line V8 Engine
OE Engines
Product Product Group AutomotiveSupplies - Original Equipment - OE Engines
Automotive Supplies
Original Equipment
Plan
Version Plan
Actual
Version Actual
Legend
<dimension scope combination identifier>
<dimension scope identifier>
Production Efficiency
Time by Month Year 2004
Product Product Group Automotive Supplies - Original Equipment - Engines
Production and Storing Facilities Assembly Line V8 Engine
Factory Factory Alpha
Version
Version Plan
Version Actual
Figure 8: Set of dimensions scopes and dimension scope combination for managerial activity
monitor production efficiency
The second sub-objective Increase Shipping Efficiency of the main objective Delivery Time Reduction of Business Unit Automotive Supplies requires the construction of a different set of dimension scopes and a different dimension scope combination. Four existing dimension scopes can be used for the managerial activity monitor shipment efficiency, which are Time by Month Year 2004, Factory Factory Alpha, Version Plan, and Version Actual. Additionally, five new dimension scopes are necessary for customers, logistic partners, orders, personnel, and production and storing facilities. Each reduces the total set of its corresponding dimension’s dimension objects to the relevant one for the managerial activity. As for the managerial activity monitor production efficiency, a dimension scope combination joins all of these dimension scopes (Shipping Efficiency). In order to create combined reference objects, again one dimension object from the first hierarchy level of the dimension scope combination, needs to be selected as well as one element of either one the version dimension scopes. Figure 9 contains the dimension scopes and the dimension scope combination for the managerial activity monitor shipment efficiency.
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Time by Month Year 2004
January 2004
February 2004
2004-01-01
2004-01-02
December 2004
Logistics Partner any Logistics Partner
Partners for Engines
Partners for Chassis Components
Factory Alpha
Factory Factory Alpha
Factory Alpha Warehouse Foreman
Personnel Factory Alpha Warehouse Workers
Customers by CRM Class Any Customer
Class A Customers
Class B Customers
Order Any Order
Order 0000001
Order 0000002
Factory Alpha Warehouse
Production and Storing Facilities Factory Alpha Warehouse
Plan
Version PlanActual
Version Actual
Legend
<dimension scope combination identifier>
<dimension scope identifier>
Shipping Efficiency
Time by Month Year 2004
Logistics Partner any Logistics Partner
Factory Factory Alpha
Personnel Factory Alpha Warehouse Workers
Customers by CRM Class Any Customer
Order Any Order
Production and Storing Facilities Factory Alpha Warehouse
Version
Version Plan
Version Actual
Figure 9: Set of dimensions scopes and dimension scope combination for managerial activity
monitor shipment efficiency
The introduced dimension scopes and dimension scope combinations from Figure 8 and Figure 9, correspond to two managerial activities of the managers responsible for production and logistics. Both managerial activities serve the purpose of reducing the delivery time of business unit Automotive Supplies as introduced with the main objective in Section 2.2. The activities of the higher management may just require the information, whether the delivery times have been reduced or not. The determining factors for this reduction are clear to the production and logistics managers, but in order to minimize information overflow, they are not part of upper management’s view on business processes. Also, the hierarchical depth of the dimensions Product and Time by Month have been reduced. In contrast to the horizontal reduction of dimensions, this reduction is made vertically. It is no longer possible to drill down from months and product groups to more detailed dimension objects such as products or days. Figure 10 contains the dimension scopes and the dimension scope combination for the managerial activity monitor delivery time of business unit automotive supplies.
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Product Business Unit Automotive Supplies
Original Equipment
Replacement
Automotive Supplies
OE Engines
OE Chassis Components
OE Electronic Components
Electronic Components
Engine Parts
Electronic Parts
Plan
Version Plan
Actual
Version Actual
January 2004
February 2004
December 2004
Time by Month Year 2004
Legend
<dimension scope combination identifier>
<dimension scope identifier>
Delivery Time of Business Unit Automotive Supplies
Product Business Unit Automotive Supplies
Time by Month Year 2004
Version
Version Plan
Version Actual
Figure 10: Set of dimensions scopes and dimension scope combination for managerial
activity monitor delivery time of business unit automotive supplies
4.3 Constructing Aspect Systems All defined business objectives from Section 3.4 have now been decomposed and used to construct dimensions. Furthermore, navigation spaces have been created to monitor if the business objectives have been accomplished. In the next step, aspect systems will be defined which will be assigned to navigation spaces, allowing for the construction of business facts. The decomposition of facts in Section 3.4, led to measures which have been used to quantify or qualify the references. As shown in Figure 6, these measures will be transformed either into quantitative or qualitative aspects, depending on the nature of their values.
To monitor the objective Increase Production Efficiency introduced in Section 3.4 several aspects are necessary. First, production efficiency is a qualitative aspect. Levels from zero to ten can be used to value production efficiency. The objective Increase Production Efficiency has been broken down into three sub-objectives, which have been transformed into quantitative aspects. The measures of the three sub-objectives are average rejection rate, average defect rate, and average lead time. All three aspects will be organized into an aspect system Production Efficiency Measurement as sub-aspects of the aspect production efficiency. For analytical purposes, the production efficiency is significant and used as a starting point. In case something is wrong, it is possible to drill-down to the influencing aspects average rejection rate, average defect rate, and average lead time.
The construction of the second aspect system for the objective Increase Shipping Efficiency is similar to the construction of the aspect system for the objective Increase Production Efficiency. Shipping efficiency is the most significant aspect. Three sub-aspects are derived from the sub-objectives of the objective Increase Production Efficiency, which are average just-in-time deviation, average packing time, and costs.
The construction of the main objective’s aspect system Delivery Performance Measurement differs from the first two aspect systems. It is constructed from three aspects, which are average delivery time, shipment efficiency, and production efficiency. Shipment efficiency and production efficiency are taken from the first two aspect systems, but in contrast to the
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production and logistics management, there are no drill-down possibilities for the aspects Shipment Efficiency and Production Efficiency. This again is due to avoid information overflow. All aspect systems for the three managerial activities monitor production efficiency, monitor shipment efficiency, and monitor delivery time of business unit automotive supplies are shown in Figure 11.
Average Rejection Rate
Average Defect Rate
Average Lead Time
Production Efficiency Measurement1 34 52967
579
Production Efficiency
Average Just-In-Time Deviation
Average Packing Time
Costs
Shipment Efficiency Measurement1 34 52967
579
Shipment Efficiency
Average Delivery Time
Shipment Efficiency
Production Efficiency
Delivery Performance Measurement1 34 52967
579
Legend <aspect system identifier>1 34 52967
579
<super-aspect (hierarchically)>
<sub-aspect (hierarchically) Figure 11: Aspect Systems for the managerial activities monitor production efficiency,
monitor shipment efficiency, and monitor delivery time of business unit automotive supplies
4.4 Constructing Information Objects As pointed out above, a managerial activity which monitors if a business objective has been accomplished, needs to compare plan scenarios with actual business developments. The nature of such analyses is that the reference objects of compared business facts differ only in a value of dimension Version (Holten, Dreiling 2002; Holten, Dreiling, Schmid 2002). To compare planned facts with actual business facts, fact calculations need to be defined. Any business fact of a dimension scope combination which features the included dimension of the fact calculation, can be calculated according to the calculation expression. Figure 12 shows the fact calculation Plan Variance. It calculates a percentage, which represents the deviation by which planned aspects differ from actual aspects. The fact calculation abstracts from aspects. It can be assigned to each dimension scope combination, where part of it equals to definition of Version in Figure 12.
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Legend <fact calculation identifier>+
-%
<dimension identifier>
<dimension scope identifier>
<calculation expression>
Plan Variance := (Plan/Actual)*100
Plan Variance+
-%
Version
Version Plan
Version Actual
Figure 12: Fact Calculation Expression Plan Variance
Finally, we are able to construct information objects for the three main objectives introduced in Section 3.4. Each information object in our example, consists of a dimension scope combination, an aspect system, and a fact calculation expression. The information object Production Efficiency assigns the aspect system Production Efficiency Measurement to the dimension scope combination Production Efficiency. Furthermore, the deviation analysis of planned and actual aspects is rendered possible by the fact calculation Plan Variance. Both other information objects are structured similarly. Figure 13 contains the information objects Production Efficiency, Shipment Efficiency, and Delivery Performance Measurement.
Legend
<dimension scope identifier>
<information object identifier>
<fact calculation identifier>+
-%
<dimension identifier>
Delivery Performance Measurement
Delivery Time of Business Unit Automotive Supplies
Delivery Performance Measurement1 34 52967
579
Plan Variance+
-%
Shipment Efficiency
Shipment Efficiency
Shipment Efficiency Measurement1 34 52967
579
Plan Variance+
-%
Production Efficiency
Production Efficiency
Production Efficiency Measurement1 34 52967
579
Plan Variance+
-%
Figure 13: Information objects for the managerial activities monitor production efficiency,
monitor shipment efficiency, and monitor delivery time of business unit automotive supplies
The constructed information objects consist of planned and actual business facts. Besides the planned facts that arise from plan scenarios defined by the introduced business objectives, other facts are included within these information objects. Examples are production efficiency of factory alpha, shipment efficiency of logistic partners, or the average delivery times for customer orders within the year 2003. These other facts are part of a dynamic managerial analysis which aims at detailing or generalizing the examined aspect of the business.
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5 Summary and Outlook Information Sharing is the conceptual core of inter-organizational business process integration. Positive effects of information sharing on stock levels and delivery times along supply chains have been shown. Customer relationship management has been introduced to overcome informational uncertainty if information sharing is not possible. Information systems form the backbone of any supply chain integration and complex customer relationship management analysis. Even though the information-technology side of supply chain integration is well understood by researchers and practitioners, the conceptual specification of information systems for business process integration from a management perspective remained an unresolved issue.
In order to overcome the open methodological problem, we have introduced the MetaMIS approach for the specification of managerial views on business processes. In a sample case, we have shown how a system of operational sub-objectives aiming at achieving the objective of delivery time reduction, can be transformed into MetaMIS specifications. These specifications can be used to create data warehouse structures (Holten 2003). With the presented approach, the conceptual management perspective is assisted by first deriving specifications of managerial views from defined business objectives, and second, the development of an information system supporting managerial analysis.
Our future research will focus on validating our approach in various case studies from different business domains. We will further develop a tool for supporting the specification of managerial views on business processes, in order to assist the development of enabling information systems from a conceptual management perspective.
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