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1. 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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2. 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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3. 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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4. 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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5. Base Model
inventory
cost
level
order
total cost warehousing size
costs
average level
ordering level
fixed cost
per order safety stock
per unit X
order size X time
min total cost delivery time
Effects of integrated Material and Inventory Management
inventory
level
average level 2
ordering level 2
safety stock 2
time
delivery time 2
Effects of Reduced Ordering Costs
inventory
cost
level
total costs 1
warehousing
costs
average
total costs 2 level 3
ordering
level 3
cost per order
cost per order
min total cost 2 min total cost 1 order size X time
delivery time 1
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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6. 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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7. 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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8. 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.
Raw Material Basic Material Parts Component End Consumer
Market Market Market Market Market
Raw Material Basic Material Component Product
Part Supplier End Consumer
Supplier Supplier Supplier Assembler
Legend
Supply Chain Information
CRM Demand Information
Market Research
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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9. 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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10. 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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11. 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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12. = 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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13. Reference Object
Structure
(0,m)
(0,m)
Combined Reference
CRO-Coordinates
Object (0,m)
Dimension Object
Hierarchy
(0,m) (0,m)
(0,m)
Operator CE-Ot-As
(0,1)
Dimension Object
(0,m)
(1,1)
(1,m) (1,m)
Calculation Expression DO-DS-AS
(1,m)
(1,m)
(0,m)
Operand CE-On-As
Dimension Scope
(0,m)
u,t Fact
DS-DSC-AS
(0,m) (0,m)
Aspect
(1,m)
A-AS-As u,t Quantitative Aspect
(Ratio) Dimension Scope
Combination
(0,m)
(0,m)
Aspect System
(0,m)
Qualitative Aspect
Information Object
(1,m)
Dimension Grouping
D-DG-AS
(1,1)
(1,m)
Dimension DO-D-AS
(1,m)
(1,m)
D-HL-AS DO-D-HL-AS
(1,1)
Hierarchy Level
Legend Specialization (Types:
Reinterpreted - u unequivocally, e equivocally
<Identifier> Entity Type <Identifier>
Relationship Type - t total, p partial)
Connector (
(min,max)
<Identifier> Relationship Type - 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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14. 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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15. 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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16. Objective
Structure
(0,m)
(0,m)
Objective
Strategy,
Strategic
u,t General Condition, u,t Business Mission u,t Objective
and Guideline
Tactical
Corporate Identity
Objective
Policy and Operative
Practice Objective
Operational
Objective
u,t General Goal
Organizational Unit
Goal
Business Unit
Goal
Marketing-Mix-
Based Goal
Legend Specialization (Types:
Reinterpreted - u unequivocally, e equivocally
<Identifier> Entity Type <Identifier>
Relationship Type - t total, p partial)
Connector (
(min,max)
<Identifier> Relationship Type - 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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17. 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.
Objective
Structure
(0,m)
(0,m)
Objective
Strategy,
u,t General Condition,
and Guideline
Operational (0,m)
OO-OL-AS
Objective
Quantitative Aspect (0,m) Objective (0,m)
(Ratio)
u,t Objective Measure
Level
Qualitative Aspect
(0,m)
Reference Object
Legend
Reinterpreted
<Identifier> Entity Type <Identifier>
Relationship Type
Connector (
(min,max)
<Identifier> Relationship Type - min minimum cardinality,
- max maximum cardinality)
Specialization (Types:
- u unequivocally, e equivocally
- t total, p partial)
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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18. 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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19. 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.
Sub-Objective Sub-Objective Time
Main Objective Reference Measure Level
Level 1 Level 2 Frame
Delivery Time Reduction of Business Unit Automotive business unit automotive supplies, average delivery
next year 24 hours
Supplies any product time
assembly line V8 engine, factory
Increase Production Efficiency next year efficiency 9
alpha
products of product group Original
Rejection Rate average rejection
Equipment - Engines, assembly next year 0.2 percent
Reduction rate
line V8 engine, factory alpha
Machine Defect machines, assembly line V8 average machine 0.3 class A
next year
Rate engine, factory alpha defect rate defects per week
single products of product group
Lead Time
Original equipment, assembly line next year average lead time 240 minutes
Reduction
V8 engine, factory alpha
Increase Shipping Efficiency factory alpha, any logistics partner next year efficiency 9
Decrease Just-In-
factory alpha, any logistics partner, average just-in-
Time Deviation of next year five minutes
product time deviation
Logistics Partners
Decrease Packing factory alpha warehouse workers, average packing
next year one hour
Time customer, order time
Reduce
Warehousing factory alpha warehouse next year total costs 500,000 €
Costs
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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20. 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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21. 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 Production and Storing Facilities
Automotive Supplies Assembly Line V8 Engine
Original Equipment Machine V8 - JH7765K
OE Engines Machine V8 - HJG5RF4
OE Chassis Components
Assembly Line Chassis Components
OE Electronic Components
Factory Alpha Warehouse
Replacement
Electronic Components Personnel
Engine Parts Assembly Line V8 Engine Foreman
Electronic Parts Machine V8 - JH7765K Foreman
Workplace 1
Industrial Supplies
Workplace 2
Services
Machine V8 - HJG5RF4 Foreman
Time by Month
January 2004 Assembly Line Chassis Components Foreman
February 2004 Factory Alpha Warehouse Foreman
Logistics Partner Factory
Partners for Engines Factory Alpha
Partners for Chassis Components Factory Beta
Customers by CRM Class Order
Class A Customers Order 0000001
Class B Customers Order 0000002
Version
Plan
Actual
Legend <dimension identifier>
<non-opened non-leaf dimension object identifier>
<opened non-leaf dimension object identifier>
<leaf dimension object identifier>
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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22. 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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23. Product Product Group Automotive
Time by Month Year 2004
Supplies - Original Equipment - OE Engines
January 2004 Automotive Supplies
2004-01-01 Original Equipment
2004-01-02 OE Engines
Production and Storing Facilities
February 2004
Assembly Line V8 Engine
Assembly Line V8 Engine
December 2004
Machine V8 - JH7765K
Factory Factory Alpha
Machine V8 - HJG5RF4
Factory Alpha
Version Plan Version Actual
Plan Actual
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
Legend <dimension scope identifier>
<dimension scope combination identifier>
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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24. Time by Month Year 2004 Factory Factory Alpha
January 2004 Factory Alpha
2004-01-01
Customers by CRM Class Any Customer
2004-01-02
Class A Customers
Class B Customers
February 2004
December 2004
Order Any Order
Logistics Partner any Logistics Partner Order 0000001
Partners for Engines Order 0000002
Partners for Chassis Components
Personnel Factory Alpha Warehouse Workers
Production and Storing Facilities Factory Alpha Warehouse Foreman
Factory Alpha Warehouse
Version Actual
Factory Alpha Warehouse
Actual
Version Plan
Plan
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
Legend <dimension scope identifier>
<dimension scope combination identifier>
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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25. Product Business Unit Automotive Supplies Time by Month Year 2004
Automotive Supplies January 2004
Original Equipment February 2004
OE Engines
December 2004
OE Chassis Components
Version Actual
OE Electronic Components
Actual
Replacement
Version Plan
Electronic Components
Plan
Engine Parts
Electronic Parts
Delivery Time of Business Unit Automotive Supplies
Product Business Unit Automotive Supplies
Time by Month Year 2004
Version
Version Plan
Version Actual
Legend <dimension scope identifier>
<dimension scope combination identifier>
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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