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These articles made an interesting read and the issues raised were analyzed in relation to
the questions. This presentation is therefore a response to each question with reference to
respective case studies. The first article, “How to get the most from a business intelligence
application during the post implementation phase? Deep structure transformation at a U.K. retail
bank” is about the Clydesdale Bank and Yorkshire Bank (CYB), which is the crust of the case
study. In the second article, “Accounting logics as a challenge for ERP system implementation: A
field study of SAP’, the case study showed the struggle by Wood Plc to implement an ERP and
integrate two accounting systems within the ERP. The third article, “Big data and information
processing in organizational decision processes: A multiple case study” has 12 cases in which
different types of BI&A-supported decision processes are examined. Consequently, adequate
responses to these questions are present as follows:
Question 1: Do the information systems in these case studies primarily support decision-making
or other kinds of business processes? Identify these decisions or other business processes, and
describe how information systems support them. Should this mix of support for decision-making
vs. other kinds of business processes be any different, given the respective organizational
A business process consists of various interrelated and sequential activities performed by a
group of stakeholders to accomplish predetermined goals. In fact, business processes have been
defined as “the unique ways in which organizations coordinate and organize work activities,
information and knowledge to produce a product or service” (Laudon & Laudon 2018).
In this case study Clydesdale Bank and Yorkshire Bank (CYB), we used this definition to identify
the following business processes:
● Customer base expansion and relationships
● Product development, product delivery, customer acquisition/sales, process quality
assurance, and product quality assurance
● Performance measurement, performance management, production process management
● Decision analysis and Resolution
Each of these processes is the natural offshoot of a decision made at the operational and
executive levels. Decisions relating to strategic marketing, customer base expansion campaign,
product development, target performance, profitability, market segmentation and problem
resolution were made at the executive level by the Steering and Executive committees. However,
operational decisions dealing with advertisement and sales, product or service delivery, product or
service quality control, customer acquisition, customer retention and performance monitoring were
made at the operational level by the retail management team.
To align management objective and the organization’s business process towards achieving
the desired goals, the customer profitability BI application was acquired and tailored to meet the
current business needs of CYB. Data input into the profitability BI include customer personal data,
product and service data, product and service costing model, as well as one-off fees and charges.
Expected output include routine reports in addition to analytics on highly valuable customer
groups and any unfavorable changes in their behavior. The actual outputs are:
● Profitability dashboards
● Analytical memorandums on the impact of new product launches on the overall customer
● Business indicators for monitoring and improvement of performance
● Strategic analytics on specific business objective to the Steering & Executive committees.
Most of the output data especially the profitability dashboards and strategic analytics were utilized
by the Steering and Executive committees in their decision-making process. The retail
management unit received the business indicator to facilitate monitoring and improvement of
performance. The marketing team used the profitability dashboard for the various marketing
segments and analytical memorandums on the impact of new product serve as foundation for
decision making by the product development team.
Nevertheless, the information system failed to uncover the analytics on highly valuable
customer groups and any unfavorable changes in their behavior. Consequently, the information
system only partially supports the decision-making and the “quest for promising customer
segments remained unsatisfied”. Furthermore, the mix of support for decision-making and other
business processes could have been different. More so, if the customer profitability BI had been
tailored to include more set of input data from external sources such as UK Office of National
Statistics and the Bank of England which were previously omitted.
This case study is about implementation of the Germanic accounting logic using Enterprise
Resources planning (ERP), by Wood Plc a manufacturing company, to integrate two accounting
systems: accounts for the inside management accounting and the accounts for the outside (financial
accounting). The objective is to facilitate preparation of financial statements for use by external
stakeholders as well as management accounts for internal planning. The financial accounts (FI) are
historical and represented independent application components. The management accounts (CO)
are forward looking and also represented independent application components within the ERP.
According to the article, “These two applications communicate regularly with each other, so all the
data relevant for cost calculation flows automatically from financial accounting to controlling, and
costs and revenues are allocated to different CO account assignment objects (e.g. cost centres or
The following business processes were identified:
● Imputing various financial accounting details and records
● Imputing cost details including depreciation and corporate tax rates
Each process serves as basis for output reports from the ERP. The actual outputs are:
Financial accounting statements, compilation and reports
Detailed contribution margin accounting scheme.
Profitability analysis based on the contribution margin accounting scheme.
Distinguishing between financial and management accounting.
● Transformation from Financial Accounting (FI) to management or Controller Account
The alignment of management objectives, the organization’s business process and this
information system was necessary for achievement of the goals ERP in Wood Plc. Consequently,
the Accounting department ensured the validity of input data into ERP including account details,
invoices, product cost, service cost, selling price, corporate tax rate, etc. Expected output include
periodic financial statements and reports in addition to internal reports such as cost projection,
contribution margin scheme, budgets, profitability analysis, etc. for the controller.
The outputs serve as foundation for decision-making. The contribution margin accounting is the
basis for solving the issue of cost allocation to production process in Wood Plc. The
transformation of FI to CO facilitates monitoring of all subsidiaries by the headquarters. However,
“Profitability analysis based on contribution margin accounting is a major source of errors, resting
upon the struggle to calculate these numbers” (Heinzelmann, 2017.) Consequently, the ERP only
partially supports the decision-making. Additionally, the mix of support for decision-making and
other business processes could have been different. The ERP could use some expert intervention to
solve the error problems related to the profitability analysis.
This article indeed shows that information systems supported decision processes, but first
we must analyze the decisions made. In this particular article, there were twelve different cases
analyzed. Factors behind the decision-making process for each case were the following:
1. Telco: This is a telecommunications firm deciding how to combat competition rivaling
their text-messaging service.
2. Media: The media company was dealing with a downward trend in sales, with managers
deciding to develop a new pricing strategy.
3. Finance: The finance department was losing their competitive advantage and dealing with
low sales. They decided to come up with a new pricing strategy and even develop a new
4. Consumer: Decision process kicked off after profits began to decrease. Alternative
product mixes and prices were then tested in different scenarios for forecasting reasons.
5. Tourism: Intentions were to develop a new destination. The amount to invest into the
undeveloped destination and the location were the two focal points of the decision-making
6. Transport: This sector was dealing with revenue issues pertaining to certain routes.
They provided alternatives to changing frequencies, constitution of the fleet, and capacities.
7. Finance: Profit issues were a problem after they acquired another financial firm, with the
catalyst of the profit issues being a lack of risk and price models. An evaluation of new
models and customer segments were part of the decision process.
8. Pharma: The pharmaceutical sector is going through a more routine decision-making
process that deals with new ingredients coming into the market. Part of that process is
analyzing investments and the long-term effects of those investments.
9. Finance: This department goes through the decision process on a semi-annual basis,
which focuses on the pricing of insurance policies. New pricing strategies are tested,
including revising the existing structure.
10. Consumer: Sales discounts are the topic of the decision-making process on a consistent,
weekly basis. Discount suggestions are brought to the attention of the sector by an analytics
system. Revision is required, and the implementation of the discount depends on overall
11. Engineering: The engineering industry has to make a decision concerning operational
planning and the control of their service capacities. Their analytics system helps combine
vital information to help the sector make better usage of their service and maintenance
12. Transport: Traffic is a concern for this particular transportation case. The decisionmaking process plans to address the capacity of passengers and the flow of traffic. The
BI&A system helps the supervisor, who is responsible for preventing traffic overflow, view
simulations of traffic flow that occurs every five minutes.
As indicated, majority of the cases listed used business intelligence and analytics to be the
catalyst behind the results towards their respective decision contents. Big data analysis allowed each
business decision to be evaluated on three different levels, which were variety, volume, and velocity.
Big data showed that a majority of the first nine cases were rated higher in variety compared to
volume and velocity, indicating that those cases utilized numerous amounts of sources in their
decision-making process. The other three cases had high ratings in all three aspects of the analysis.
The information systems have a positive impact on business processes, but there is variation between
the companies examined. When structured probably and utilized effectively for the respective
company, the information systems do in fact support both decision making and business processes.
Question 2: Have these information systems transformed the business processes they support, or
do they seem to have largely conformed to the processes? Explain your reasoning. Should it have
been any different, given the respective organizational contexts?
Clydesdale and Yorkshire Bank (CYB) set out to increase their customer base value
through the retention and acquisition of customers with high customer value. To achieve this goal
CYB initiated a customer BI profitability application. The BI profitability application was
designed to “provide intelligence allowing the differentiation of CYB retail customers by their
profitability to the bank, the identification of valuable customer groups, and the provision of
customer management leads” (Audzeyeva and Hudson, 2016). During the implementation phase,
the application amalgamated a steady flow of information from databases that produced current
measures of profitability and behavior of customers. The application also generated past
performance indicators for monitoring and making improvements. In addition, the application
provided a precise valuation of the impact of new product initiatives had on customer profitability.
Before BI, access to such information was not feasible. At the operational and strategic
management levels the BI profitability application improved decision making processes for some
users (Audzeyeva and Hudson, 2016). However, for others users it raised additional questions.
Two top level managers at CYB learned that there were some unusual behaviors exhibited
by important customer groups. This observation prompted a deeper investigation known as
Knowledge Transfer Partnership (KTP) into those customer groups. The results of the
investigation showed that the BI profitability application did not uncover ‘new information’ about
customer behavior (Audzeyeva and Hudson, 2016). BI profitability application failed to reveal the
scale and overall contribution that 55 plus customers had on CYB’s profits (Audzeyeva and
Hudson, 2016). It also failed to show a steady decrease in retention for that particular customer
BI profitability application on the surface appeared to transform the business processes
they supported. But when the case is examined more closely the BI profitability application
conforms to the processes as well. For instance, the BI analysis and reporting structures contained
obsolescent developed beliefs about customer behavior which ultimately prevented the application
from alerting managers when the business model was no longer aligned with changes in the
environment (Audzeyeva and Hudson, 2016). A BI based process requires up-to-date information
to deliver accurate decision-making information. In another instance, the BI reporting structures
were statically linked to the business model, organizational structure, and users. This close
alignment hampered the identification of new developments in the environment (Audzeyeva and
Hudson, 2016). A BI based process should incorporate a robustness of business assumptions to
generate problem areas (Audzeyeva and Hudson, 2016).
The outcome would have been different if the IS, management and organizational structure
had been better aligned with each other. Such alignment would have recognized the need for an
external expert to tackle the unresolved issue of failure to identify profitable customer groups and
failure to detect unfavorable changes in customer behavior. As noted in the case study, “their BI
analysts did not possess sufficient data analysis skills to perform the task”. Besides, the analyst
who operated the BI application had other regular duties and was frustrated. In addition, the
customer data was voluminous and complex. This emphasized the fact that achievement of
organizational objectives depends largely on the proper harmony between management,
organization and information technology.
The (SAP) implementation project definitely influenced the Wood Plc’s business processes. If
you compare the situation of the company before and after (SAP) implementation, you will find several
things that have changed. First, the (SAP) implementation project was used by Wood Plc headquarters
to increase the level of control on subsidiaries through several standards. The headquarters used
accounting functions standards according to German accounting logic, which was carried out in
conjunction with the (SAP) R/3 system. This process made the logical Germanic accounting unique, not
only by a strong separation of financial and administrative accounting but also by the use of a number of
technologies which improved the quality of the financial reports.
The (SAP) system exposed the employees to several rules of accounting that they were not
aware of. The new system led to concentration of the tool and technologies of different business units at
the HQ, which enhanced comparability and standardization among plants. German Accounting practices
were incorporated in the standards that were imposed by headquarters and strengthened by (SAP). The
SAP changed the way in which accounting was done in the subsidiaries. They were made to comply
with the standards that reduced their flexibility in terms of scope of judgement involved in accounting.
While (SAP) improves functionality, it limits the scope of work. As one of the interviewees notes: “It
[SAP] gives you so much more functionality, but at the same time it restricts your choice”
(Heinzelmann, 2017). The restriction of functions and powers gave the company management high level
of visibility and control over its business units and subsidiaries.
The system also supported the quality of reports in two ways:
1. All inputs were received in one place (Headquarters): so, there was no distortion in the input data
source that was used in the preparation of reports.
2. The reports became more comprehensive and in-depth in detail: After the introduction of the new
system, the administration received accurate reports about financial information because the (SAP)
system limited the scope of random work and facilitated the flow of towards
Comparable data was made available by the (SAP) system, which allowed Wood Plc. to conduct
benchmarking analysis of different plants to create visibility about the different cost structures at each
plant, based on detailed numbers of key performance indicators (KPIs). Comparability between plants
became an important concern which was supported by detailed cost information through the SAP
systems. The most important KPI used in the benchmarking analysis was costs per bank meter
chipboard, described as “the father of all things’’. (Heinzelmann, 2017). With (SAP) there was
awareness of different cost structures, which helped in better management of overall expenses of the
Researchers examined business intelligence and analytics (BI&A) – supported decision
processes of 12 large organizations from different industries. Researchers looked at organizational
decisions in relation to two aspects of non-routine and uncertainty. As part of the analysis decision
types based on various combinations were placed in four quadrants for all organizations
(Kowalcyk and Buxman, 2014). Quadrant 1 included organizations identified as 1, 2, 3, 4 and 5
and was characterized by organizations who made high non-routine and high uncertain decisions.
Quadrant 2 included organizations identified as 6 and 7 and was characterized by high non-routine
and rather low levels of uncertain decisions. Quadrant 3 included organizations identified as 8 and
9 and was characterized as low non-routine but more routine than Quadrant 1 and high uncertain
decisions. Quadrant 4 included organizations identified as 10, 11, and 12 and was characterized by
low non-routine and low levels of uncertain decisions.
Picture Source: (Kowalczyk, 2014, p.271)
Researchers also studied how big data (variety, volume, velocity) impact decisions
scenarios. For Quadrants 1-3 the decision types revealed that decision processes within this
quadrant were highly variable. In fact, variety is higher than or equal to volume and velocity
(Kowalcyk and Buxman, 2014). This may have suggested that the “decision process is driven with
a priority towards addressing ambiguity and equivocality through an integration of different
viewpoints” (Kowalcyk and Buxman, 2014). For Quadrant 4 the decision types showed that
decision processes wi …
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