Information System Used as Strategy Practices
Answer to Question 1
There is a list of internal and external factors which drove the initiatives at each of the company. Truck arrivals at this given node follow a particular registration procedure at the prior to access of truck. The arrival number of trucks and its time of dispatching can vary to a large extent as a result of different factors (Sahebjamnia, Navid, Ali Torabi, and Afshin Mansouri 16). The arrival is based on some historical business processes for truck firm related to this node. There is some common cause behind the time like peak workload, which is related to truck arrival, an insufficient resource of node and external factors like weather. Due to this waiting time of the truck increases to a large extent. It ultimately results in the complexity of operating planning, where the nodes increase periodically. This will ultimately decrease the overall efficiency. Both truck organization, along with operating nodes face drawbacks. For tackling the issue, a concept has been provided regarding the decision-support system, which is based on predictive analysis.
iLoads system aims to provide support to operational planning and controls both truck companies and even forecast the time for waiting. The system is innovative as completely extends the present approach, which is used in practices. The elaborate system which is used at present provides visual of real-time waiting time by using webcams (Heilig, Leonard, Eduardo Lalla-Ruiz, and Stefan Voß 180). It can even list trivial information from history like waiting time of yesterday. In addition, artificial intelligence can be used for predicting both waiting time and the arrival of trucks. Decision-maker on either side will be benefitted in real-time for high-quality prediction, which highlights their individual need about information. iLoad system is very much generic in nature where it can implement a different kind of logistic node not based on the precise service offered by its customer. The implementation of this system is completely based on artificial intelligence completely motivated by various diverse dependency.
Answer to Question 2
The case study highlights the iLoad concept of system, which aims at twofold of decision support for enhancing the efficiency of the process at both truck companies and a logistics node. Forecasting information is given to these nodes for enhancing the internal planning of resource and controlling the truck companies (Akay, Abdullah Emin, and Hande Egemen Süslü 10). Truckers provide support to vehicle routing and scheduling. The need for requirement of a future information system is present on both sides. The differentiation in its function is done for management and operation even if an individual working for same firm. The main focus of the system was to have a basic truck handling process. It comes up with concepts on a logistic node, which is an integral aspect of the transport network. A logistic node which is handling and storage location. It offered a certain number of logistic services which has transport behaviour like changeover and load carrier. The logistic node at goods was dropped off or even picked up by the trucks.
With respect to companies that focus on iLoad system, there are mainly two types of the user as per the information needs. The operation planner was responsible for operation management on either sides that is logistic node and truck firms. Based on the organization size and organization, an individual can vary with respect to an employee count and task assignment. In addition, executive management will be benefitted from the forecasted load in order to understand the trends in the near future and strategic initiatives (Hill, Alessandro, and Jürgen W. Böse 245). The point should be noted that iLoad system provides some different view on its arrival date and likewise for waiting time of truck. The use of iLoad within various process implies various user needs. It is all about presenting the information in a meaningful way. The interface of the decision support system for different user is completely based on web and its functionality for twofold. All the details of user is completely transferred to the system to forecast the request. No, business value, competitive advantage, risk and organization culture are handled in the correct way.
Answer to Question 3
Yes, organizational information integration needs to have each company to pursue the right technological path. The paper highlights an innovative decision support system which gives forecasting depending on the type of users. On one hand, it analyzes arrivals of truck to logistic nodes like an empty container, packing facility or terminals (Sahebjamnia, Navid, Ali Torabi, and Afshin Mansouri 16). All these facilities can make use for estimating the future workloads in order to enhance the resource planning. The analyzed time for waiting at these nodes is completely available for different truck companies. The output of this optimization result in interplay will ultimately smoothen the workloads at this node. It is due to adaptive routing of truck and reduction of waiting time for trucks. This is possible because of the more accurate deployment of resource at this node.
Another biggest strength in the developed system in the flexibility and implementation of very little effort (Akay, Abdullah Emin, and Hande Egemen Süslü 10). This can even be completely embedded into the present analytics landscape, which requires the involvement of an organization for improving business intelligence.
2. Better Data Brings a Renewal at Bank of England
Answer to Question 1
The case study deals with the Bank of England, which is the oldest central Bank. More specifically, Bank was making use of microeconomic data to form detail picture for UK housing market. It has completely sum up the transactional data at various level within the nation like boroughs in London. As per Land registry data, that includes housing price index and datasets within the transaction (Fitzgerald, Michael). Proper access to datasets has enabled this Bank to go for refining in its model of how housing market behaves under the risk to lenders in financial health. As per instance, bank analysis even highlighted that UK local housing market varied to a large extend. There were concern about another in London and other parts of south UK (Piechocki, Michal). In some of the rest countries, there was not similar kind of price increase.
This was an overview for the Bank which want to accept more specifically to pace of lending the mortgage with much high ratios in loan to income. The overall number of this high loan-to-income mortgage was increasing at a rapid rate. Bank even recommended that lender to limit the required stress to some of the individual borrowers. It highlighted one of beginning triumphs for new way of working altogether at these institutes (Warjiyo, Perry 388). From the perspective of tangible sense where Bank was making changes in the way, it should behave in order to take the better benefits of data that it had access. Data has always key role in this bank work but there is need for understanding the complete potential for accessing new data. Bank made changes in its overall structure, behavior and approach for solving the problem.
Answer to Question 2
Bank of England has make use of analytics. As a result of arrival of Carney and mandates in new supervisory for the pace of its analytics. It was not that typically at this speed associated within the big data analytics. Here, large volume of data which can be gathered at required frequencies of approaching in real-time (Brodsky, Laura, and Liz Oakes). To help in creating datasets for supporting goals of one bank analytics goal, Bank come up first-ever data inventory for the kind of datasets are there in their house. Another important reason behind the inventory was important that it can help datasets for helping with new kind of decision of policy. The inventory took up around a year and turned up around 1,000 datasets. Data inventory tools tab at each of the dataset across of the list of 14 categories. They were searchable and easily available on bank intranet. Inventory clearly highlighted which datasets can be used for which purpose like Bank collect data from the external source. Inventory even capture the main purpose for purpose on which Bank makes use of use of data. Data inventory thus help the Bank to ensure that the bank that there are completely compliant with some legal restriction on the provided data.
Analytics need a balancing act for this Bank, which gives other mission of institutes. Both monetary policy and its regulation of insurance help in making use of huge amount of data. It aims in accomplishing different goals (Chen, Jiakai, Joon Ho Kim, and Ghon Rhee). Various section of the Bank needs to access the same data which aim in accomplishing different goals. Bank has come to the point of make use of it for some specific purposes only. Much of this data does not need restriction and even create much broader access that can highlight the policymaking. It is due to minimized duplication of effort. Good policymaking completely expansion regarding the information value.
Answer to Question 3
All the required recognition and adaptation of new tools are there to enhance new kind of analysis received from Bank. This wades into uncharted into the territory from the Central Bank. Department Insurance supervision have been doing for long-term planning around the change in climate (Akter, Shahriar 529). Work done by them has led to warning among insurers which require to do much more for protecting themselves from any risks related to finance. The effect of change in climate is the one which Bank wants to make use is big-data analytics for addressing the tragedy of horizon. As a result of private sector inability firm took time more than three years. Even the central bank in the UK took out the past decade.
The housing crisis model along with combination of both granular data and macroeconomic model of policy data. It will make the analytics much more powerful (Hill, Alessandro, and Jürgen W. Böse 245). In this way, Bank needs to become the oracle and even bet on analytics which will use the policy game much more opaque. Considering the recommendation for housing, Bank had implemented its limit on the loan-to-income ratio for the year of October 2014.
3. Failure of large Transformation projects from the viewpoint of the complex adaptive system
Answer to Question 1
Most of the large transformation project does have desired outcomes as per the stakeholder. This kind of project is mainly characterized by dynamics that are caused by dynamics both caused and resulted in uncertainties and unexpected behaviour (Burmeister, Fabian, Paul Drews, and Ingrid Schirmer). The complex adaptive system view has been adopted for better understanding of project dynamics. There is a need for identifying management principle in order to deal with them. The project can be easily delayed and become much expensive and even provide much less in terms of functionality. This can led to failure and needs to be completely redone. Different stakeholders come up with own metrics for measuring the status of the project. Transformation project can be understand as its inability for meeting the growing requirements and create a working system (Anthopoulos, Leonidas 167). This can be considered as successful if it exceeds the deadline and budget or delivery of the desired function. Project failure ranks as per the scale which ranges from not providing required function needed for complete failure of the project. In this, most of the effort and funds are completely wasted. In some cases, projects are also evaluated with respect to the delivered functions, budget and time required for completely finishing the project.
CAS is known to be a complex system. CAS lens focus on the complex and growing properties of the system (Nerurkar, Amrutaunshu, and Indrajit Das 38). Theory of CAS is needed to characterize and understanding of organizational behaviour. It was achieved by analyzing at the dynamic interaction among different entities. Organization theory has been treated with complexity like an independent variable. Considering the CAS behavior of a complex system, it is very much tough to predict due to its non-linear nature. Capturing of non-linear outcomes result in many components which are difficult for both social and natural scientists (Ramesh, Nagesh). CAS is all about the study of the system which is built of individuals called agents. They come up with the capability of interacting with one another in an environment to understand individual effect at the system-level response.
Answer to Question 2
As a result of the project complex nature, there is a need for gaining a much deeper insight into factors which influences the project dynamics. All the publicly available documents were analyzed in the beginning. It helped a lot in understanding the project dynamics and management interventions (Nemet, Gregory, Vera Zipperer, and Martina Kraus 157). Business to government information sharing takes place in most of the areas which range in between tax details to social statistics. On the contrary, business-to-government sharing is known to the next frontier in order to reduce the expenditure of the government along with improving performance. Business needs to make a report for all kind of information for the government can track the level to which the organization can comply with the new rules (Hughes, Laurie 1320). International Extensible business reporting language standard and transformation of the process needs to have financial reporting. This project helped in dealing with fragmentation caused by the present scenario. Here various kinds of the document need to completely submitted on paper to the different public firm.
The starting of this project was a bird view of the provided situation that proved to be much more complicated in nature at the time of getting down to the things. The project decision was able to come up with a sound case for business. The logical consequence regarding the project dynamics was leading with ad hoc way emphasizing much on incidents management. There were many factors like scoop creep, change in requirement, bad news, unclear vision, impact of changing and other related factors which lead to a huge effect on the whole project. It merely confirms the whole concept behind CAS and even suggests in guiding the right behavior for individual agents by making use of the simple rule. The overall system behavior will change (Syed, Rehan, and Wasana Bandara). An individual can continue to accepting and adapting to the changes in the present environment. They obey certain all principles that are presented.
Answer to Question 3
Large transformation project comes up with a high chance of failure due to its dynamics which make it difficult for managing this kind of project. Project failure can be studied in an ICT project. Yes, the organization aim to adopt a complex adaptive system lens for looking into the project dynamics. There is a list of factors that provided in a dynamic and related to project failure (Zhu, Yu-Qian, and Asdani Kindarto 630). All these aspect resulted to the complex dynamics like transformation project. It will result in uncertainty for outcomes, scoop creep, requirement for managing incident and changes on overall impact. It tends to have a devising impact on overall progress and adaptation by the user in the whole project. Analysis of the case study even highlighted that there is a huge number of factors which were overcome by using simple principles. The growth of some adaptive behavior resulted in failure of project, which can be enhanced indirectly as a result of individual agent and alteration in the relationship in the network.
Information system projects tend to have a huge number of problems. Failure of a project is generally based on the categorizing the failure (Alami, Adam 67). Complexity, superior level nature and growing behaviour result in conceptualization and provide a much simplistic overview on failure of project. CAS lens provides a much deeper analysis of project dynamics where the influence of project success comes into the picture.
References
Akay, Abdullah Emin, and Hande Egemen Süslü. “Developing GIS based decision support system for planning transportation of forest products.” Journal of Innovative Science and Engineering (JISE) 1.1 (2017): 6-16.
Akter, Shahriar, et al. “How talent capability can shape service analytics capability in the big data environment?.” Journal of Strategic Marketing 27.6 (2019): 521-539.
Alami, Adam. “Why do information technology projects fail.” Procedia Computer Science 100.2016 (2016): 62-71.
Anthopoulos, Leonidas, et al. “Why e-government projects fail? An analysis of the Healthcare. gov website.” Government Information Quarterly 33.1 (2016): 161-173.
Brodsky, Laura, and Liz Oakes. “Data sharing and open banking.” McKinsey on Payments July (2017).
Burmeister, Fabian, Paul Drews, and Ingrid Schirmer. “Leveraging Architectural Thinking for Large-Scale E-Government Projects.” (2019).
Chen, Jiakai, Joon Ho Kim, and S. Ghon Rhee. “Matches Made in Banks: Do Common Bank Relationships Facilitate Better Mergers?.” (2019).
Fitzgerald, Michael. “Better data brings a renewal at the bank of England.” MIT Sloan Management Review 57.4 (2016).
Heilig, Leonard, Eduardo Lalla-Ruiz, and Stefan Voß. “Multi-objective inter-terminal truck routing.” Transportation Research Part E: Logistics and Transportation Review 106 (2017): 178-202.
Hill, Alessandro, and Jürgen W. Böse. “A decision support system for improved resource planning and truck routing at logistic nodes.” Information Technology and Management 18.3 (2017): 241-251.
Hughes, D. Laurie, et al. “Information systems project failure–analysis of causal links using interpretive structural modelling.” Production Planning & Control 27.16 (2016): 1313-1333.
Janssen, Marijn, Haiko Van Der Voort, and Anne Fleur van Veenstra. “Failure of large transformation projects from the viewpoint of complex adaptive systems: Management principles for dealing with project dynamics.” Information Systems Frontiers 17.1 (2015): 15-29.
Nemet, Gregory F., Vera Zipperer, and Martina Kraus. “The valley of death, the technology pork barrel, and public support for large demonstration projects.” Energy Policy 119 (2018): 154-167.
Nerurkar, Amrutaunshu, and Indrajit Das. “Analysis of DILRMP Project: Identifying the Applicability of Agile Project Management for Digital Transformation Projects in Government and Public Sector.” Proceedings of the Special Collection on eGovernment Innovations in India. 2017. 34-38.
Piechocki, Michal. “Overview of international experiences with data standards and identifiers applicable for big data analysis.” IFC-Bank Indonesia Satellite Seminar on “Big Data” at the ISI Regional Statistics Conference. 2017.
Ramesh, Nagesh. Digital Transformation: How to Beat the High Failure Rate. Diss. 2019.
Sahebjamnia, Navid, S. Ali Torabi, and S. Afshin Mansouri. “A hybrid decision support system for managing humanitarian relief chains.” Decision Support Systems 95 (2017): 12-26.
Syed, Rehan, and Wasana Bandara. “Public sector digital transformation failure: Insights from a revelatory case study in a developing country.” (2019).
Warjiyo, Perry. “Central bank policy mix: key concepts and Indonesia’s experience.” Buletin Ekonomi Moneter dan Perbankan 18.4 (2016): 379-408.
Zhu, Yu-Qian, and Asdani Kindarto. “A garbage can model of government IT project failures in developing countries: The effects of leadership, decision structure and team competence.” Government Information Quarterly 33.4 (2016): 629-637.
