Business Finance
INSS 605 UB Information Systems Single IS Category Paper

INSS 605

University of Baltimore


Question Description

I’m studying for my Business class and don’t understand how to answer this. Can you help me study?


Note: You are strongly advised to submit your work for this second individual project by the recommended due date highlighted below. Although work submitted after this due date will NOT be subject to a late penalty, any submissions made after 11:55 pm on Thursday, May 14 will simply not be graded, and automatically earn a grade of 'F.' NO EXCEPTIONS!

Recommended Due Date: 3/29/2020

Submission Information: Compile all of your answers into a single Microsoft Word document, and submit that document here as one attachment. Submissions in other formats (including pdf) are not acceptable.

Grading Information: If accepted, your submission for this project will be graded per the Rubric for Individual Projects.

Answer the questions below in the context of each of the following five cases (all pdf files) of real-world business information systems.


  1. The information systems described in these articles don't really fall neatly into a single IS category such as TPS, MIS, DSS, ESS, ERP, SCM, CRM, KMS, collaboration environments, GIS, GDSS, etc. Rather, most seem to possess functionalities from more than one category. Identify and discuss the multiplicity of these categories for each system.
    (As a hypothetical example, one particular article may describe a system that primarily appears to be a DSS for mid-to-upper-level managers working in finance and accounting, with other functionalities that resemble an MIS designed for lower-to-mid-level managers in sales and marketing. Your answer will need more elaboration and discussion, of course.)
  2. Each system assists its respective users with decision-making in their work environments. In what stage(s) of their decision-making (Figure 12-2 in the textbook) does it provide them with assistance -- intelligence stage, design stage, choice stage, and/or implementation stage? Discuss and justify your answer.
    (Address how each completed, implemented system is proving useful, not the process by which it was conceived and acquired/built.)
  3. Each system is probably interconnected/linked to other information systems in its organization. Although the articles themselves do not address this aspect, from your understanding of organizations, business processes, and systems, describe some possible/likely examples of such interconnections for each system. Explain your reasoning, while explicitly stating any assumptions.

Also i attached to you the cases that you would need to answer these questions.

Unformatted Attachment Preview

Inf Technol Manag (2017) 18:241–251 DOI 10.1007/s10799-016-0267-3 A decision support system for improved resource planning and truck routing at logistic nodes Alessandro Hill1 • Jürgen W. Böse1 Published online: 3 October 2016  Springer Science+Business Media New York 2016 Abstract In this paper, we present an innovative decision support system that simultaneously provides predictive analytics to logistic nodes as well as to collaborating truck companies. Logistic nodes, such as container terminals, container depots or container loading facilities, face heavy workloads through a large number of truck arrivals during peak times. At the same time, truck companies suffer from augmented waiting times. The proposed system provides forecasted truck arrival rates to the nodes and predicted truck gate waiting times at the nodes to the truck companies based on historical data, economic and environmental impact factors. Based on the expected workloads, the node personnel and machinery can be planned more efficiently. Truck companies can adjust their route planning in order to minimize waiting times. Consequently, both sides benefit from reduced truck waiting times while reducing traffic congestion and air pollution. We suggest a flexible cloud based service that incorporates an advanced forecasting engine based on artificial intelligence capable of providing individual predictions for users on all planning levels. In a case study we report forecasting results obtained for the truck waiting times at an empty container depot using artificial neural networks. Keywords Decision support systems  Forecasting  Predictive analytics  Truck routing  Resource planning & Alessandro Hill Jürgen W. Böse 1 Institute of Maritime Logistics, Hamburg University of Technology, Am Schwarzenberg-Campus 4 (D), 21073 Hamburg, Germany 1 Motivation Recent numbers on cargo in industrialized countries show that road based transport dominates the market. More importantly, it will have a significant stake in the future since its market share grows faster than for alternative modes of transport such as for example railroad. Truck freight exceeded rail freight by a factor of four with a total of about 1700 billion ton kilometers in 2012 in the European Union [9]. An increase of 50 % leading to about 600 billion ton kilometers in 2030 is estimated only for Germany [3]. Accordingly, truck deliveries and pick-ups at logistic nodes [22] such as warehouses, container terminals, freight stations, empty container depots and logistics centers will further increase. Truck arrivals at these nodes are typically followed by a registration procedure at the gate before the subsequent assignment of the truck to a loading area. Both, the number of arrivals and the dispatching time can significantly vary due to various impact factors. The arrivals depend on highly stochastic business processes of the truck companies that are associated with the node. Common causes for the rise of the dispatching time are peak workloads related to the truck arrivals, insufficient node resources, node-internal process issues or external factors such as weather. As a consequence of such delays, the truck waiting times (e.g., at the gate) notably increase. This results in a major drop of the service level provided by the node. At the same time, the complexity of operations planning at the node increases in these periods of strongly fluctuating workload which is likely to decrease efficiency [5, 19]. Thus, truck companies as well as node operating companies both experience notable disadvantages. In order to mitigate the mentioned issues, this paper describes a concept for a decision support system that is 123 242 based on predictive analytics [8]. The presented iLoads1 system concept essentially aims at supporting operational planning and control at the truck companies as well as the nodes by providing forecasted waiting times and truck arrivals, respectively. This system is innovative since it extends existing approaches that are currently used in practice. The most elaborate systems that are used today, provide either visual real-time gate waiting time information through corresponding web cams or simply list trivial historical information such as yesterday’s waiting time. In contrast, we incorporate a forecasting engine based on artificial intelligence to predict waiting times and truck arrivals. Decision-makers on both sides benefit from realtime high quality predictions that are tailored to their individual information needs. The iLoads system is generic in the sense that it can be implemented at various types of logistic nodes independent from the precise service it offers to its customers. The implementation of a system based on artificial intelligence is motivated by the numerous diverse dependencies of the highly volatile waiting times in the described environment. The consideration of additional external and internal impact factors at the logistic nodes is crucial for the quality of the forecasts. The contribution of this paper is twofold. On the one hand side, we suggest the general concept of providing forecast information to the actors in this logistic environment. We propose the application of a standard artificial neural networks approach to incorporate relevant features. On the other hand side, we describe the implementation of such a system based on a real world application and provide corresponding results. The remainder of the paper is organized as follows. In Sect. 2 we describe the application domain and identify main user types and associated business processes. The resulting system requirements are presented in Sect. 3. In Sect. 4 we give a description of the system architecture and its interfaces, model components and data components before concluding this work in Sect. 6. 2 Processes and decision support The presented iLoads system concept aims at twofold decision support to simultaneously increase process efficiency at logistic nodes and truck companies. Therefore, forecasting information is provided to the nodes for improving internal resource planning and control as well as to the truck companies and truckers to support their vehicle routing and scheduling. The requirements regarding future information are certainly different on both sides. Furthermore, we differentiate between system users according to 1 Intelligent logistic order arrival decision support. 123 Inf Technol Manag (2017) 18:241–251 their function, such as management and operations, even if working in the same company. In Sect. 2.1, we identify the main user types addressed by the iLoads system and highlight their information needs for effective decision making. Subsequently, we explain the relevant business processes at logistic nodes in Sect. 2.2. 2.1 Basic truck handling process The focus of the iLoads system concept is particularly on logistic nodes as integral part of cargo transport networks. Logistic nodes are handling and storage locations, as defined in [10]. The offered logistic services include transport system changeover, load carrier changeover, repackaging, short and long term storage. In this context, we consider logistic nodes at which goods are dropped off or picked up by trucks. Optionally, the nodes interface with other transportation means such as ship and railroad. Figure 1 shows the base process that can be identified at these nodes. Its two main parts are the administrative truck handling and the physical truck handling. The administrative handling consists of an eventual waiting time that occurs before the processing of the documents at the document center, also called gate waiting time. This administrative task might require the driver to register at a desk but may also be done using an electronic terminal which typically reduces the time needed. The physical truck handling process can be divided into intermediate waiting times and loading or unloading operations. Since multiple container loading or unloading operations are possible, several intermediate waiting times might occur. We define the truck waiting time as the sum of the administrative waiting time plus the aggregated intermediate waiting times before loading and unloading operations during the physical handling as illustrated in the flow chart in Fig. 1. We note that periods without physical activity regarding truck, driver or cargo (e.g., document processing) are frequently experienced as waiting time for the truck company. Nevertheless, we follow the waiting time definition in accordance with the legal situation in Germany as follows. We consider waiting times as periods in which the node is unproductive with respect to the corresponding truck. That is, neither the truck is unloaded or loaded nor is the corresponding order involved in any administrative process. This matches the formal definition of waiting times in major countries of the European Union (see e.g., [6]). The overall dwell time is the sum of the truck waiting times and the truck handling time which corresponds to the total time that the truck spends from queuing at the gate until its departure from the node’s site. Inf Technol Manag (2017) 18:241–251 243 Fig. 1 Base process at logistic nodes 2.2 User types and business processes Regarding the companies which are in the focus of the iLoads system we can basically distinguish two types of users according to their information needs. Namely, the operations planners in charge of the operations management [25] on both sides, the logistic nodes and the truck companies, respectively. We note that depending on the company size and its organization the responsible personnel can vary in terms of the number of employees and the task assignment. Furthermore, executive management can benefit from forecasted workloads to understand future trends and trigger strategic initiatives. The predicted data can also be utilized to feed further analytic models [20]. 2.2.1 Node operations planners On the node side, the presented decision support system is most useful to operations planners who benefit from truck arrival forecasts by increased equipment and personnel planning accuracy. More detailed, this includes assignment of employees to shifts, deployment of machinery and usage of policies on a tactical level. The ability to foresee operational events allows a more adaptive planning in general. These planning tasks are typically done for a horizon from 1 day to 1 week. Additionally, future truck arrival information generates substantial value for better operations control. Ad hoc decision support is achieved by responsive short term forecasts which take into account events (e.g., traffic congestions) or notable changes of influencing factors (e.g., weather) which were not present during operational planning. In daily business of logistic nodes, the truck arrival rate, expressed by the number of trucks that arrive during a specific time period (e.g., 1 hour), is frequently used as an indicator for the workload. More accurate resource planning leads to reduced truck waiting times. Additionally, order specific arrival rates restricted to the truck type (e.g., light, heavy), the load carrier (e.g., container, pallet) or the customer are of interest since they yield a more detailed estimation of the corresponding handling effort. Forecasted truck arrival information is usually not utilized at the nodes. However, corresponding historical data is frequently considered by the planners. 2.2.2 Truck company operations planners The second main user type of the iLoads system is operations planning personnel in truck companies which is responsible for planning and control of the vehicle fleet. The forecasted waiting times can be used for truck routing, truck scheduling and related tasks [4, 11]. Furthermore, related inter-terminal traffic coordination [13] can benefit from the information provided by the system. So-called dispatchers can use forecasted truck waiting times to improve the operational fleet management. This includes the daily or weekly order assignment to trucks and drivers followed by the route planning. In practice, it is of major importance to plan the tours in accordance with existing time window restrictions. Therefore, the periods in which a truck has to visit a logistic node can depend on scheduled appointment times of preceding and subsequent jobs. For instance, the pick-up of an empty container has to happen an appropriate time before the packing date agreed with the customer, or, a truck might be unavailable during certain periods due to previously planned trips. 123 244 As at logistic nodes, the operational planning at truck companies is done from 1 day to 1 weak in advance. This includes the assignment of the orders to the available trucks, followed by the determination of the individual tours. Ultimately, this induces the number of resources (i.e., trucks and drivers) required to manage the workload. Besides efficient resource planning, tour delays due to truck waiting times can be reduced which increases punctuality. Furthermore, the logistic nodes benefit from smoothed peak workloads since the tour planner will try to schedule truck arrivals within periods of low waiting time (see Fig. 2). Currently, only a few logistic nodes provide information about truck waiting times to their customers [1]. One reason might be the lack of digital information on actual truck waiting times. In some cases, nodes offer a webcam service to show the current situation at the gate [12]. Such visual information can be used by dispatchers to get a rough idea about current waiting time. Against this backdrop, it is not surprising that most dispatchers do not anticipate truck waiting times at all. However, today’s economic and ecologic damage caused by truck waiting times is considerable. Several approaches were undertaken to clarify the general waiting time situation in major ports [15, 17, 21]). A survey among more than 550 German logistics services providers in 2012 revealed that in 50 % of the cases the waiting times at warehouses exceed 1 h (Bundesministerium für Verkehr und digitale Infrastruktur [7]). In contrast to truck arrival rates, the calculation of waiting times requires basic statistical compilation. Typically this is achieved by considering average hourly waiting times which have to be derived from the individual waiting times. In addition, auxiliary measures such as the maximal, or minimal, hourly waiting time could be useful in practice. We note that the information needs of independent truckers basically correspond to those of dispatchers. Nevertheless, differences between both user groups exist on the soft- and hardware level since the former truckers Fig. 2 Benefits of forecast information on truck waiting times and arrival rates at logistic nodes 123 Inf Technol Manag (2017) 18:241–251 are permanently on the road in contrast to dispatchers being located in an office on site at the company. Independent truckers, organized as one-man companies, basically use the system as the truck companies. Since they are continuously on the road, they particularly benefit from short waiting times. 3 System requirements In this section we describe the iLoads system requirements in detail. Based on the embedding of the system into the relevant business processes in Sect. 2.2 we provide the essential functional requirements and define the necessary data sources. 3.1 User requirements The iLoads system has to efficiently provide different views on the truck arrival data and likewise for the truck waiting time. In the following we assume that historical information as well as predicted information is provided by the system. 3.1.1 Display and forecast horizon As mentioned in Sect. 2, the use of the iLoads within the different processes implies individual user needs. To present the corresponding information in a meaningful way, we suggest the inclusion of minimal and maximal truck waiting times in addition to the average waiting times. The estimation of relevant key performance indicators is left to the user but could represent a practical extension. Another main feature that has to be addressed is the option of customizing the forecast horizons as well as the overall timespan displayed which includes historical data. In this regard, the user has to be able to clearly distinguish between past and future data and adjust the corresponding horizons individually. Naturally, this implies real-time Inf Technol Manag (2017) 18:241–251 reporting functionality. The forecasting horizon should range from 30 min for operations control up to multiple months for resource planning. Furthermore, the operations manager has to be able to adjust the forecast granularity. That is, constant time periods, or buckets, in which the underlying arrivals are summarized and the waiting times are averaged, respectively. In practice, these buckets should comprise from 10 min to 1 month subject to the chosen forecast horizon. 245 3.1.6 Forecast accuracy Regarding helpfulness, reliability and user acceptance of the system, we require a certain minimum forecast quality. Depending on the business, an accuracy within 25 % can be acceptable from a practitioner’s point of view. We note that one idea of the presented system is to outperform straightforward approaches such as simple hourly or daily performance averages through the incorporation of sophisticated forecasting methods. 3.1.2 Specific data display 3.2 Data and sources Besides showing the general arrival data for all trucks more specific views on the arrival rates and waiting times are required for effective order and customer oriented decision support. The mentioned information has to be given for various predefined order types which are commonly categorized by the truck type, cargo type and the customer. 3.1.3 Threshold visualization A meaningful visual presentation of the relevant information should include clear signals to indicate action required by the user. This can be accomplished by the use of temperature schemes and traffic light systems which qualify the recent or future situation. Corresponding thresholds have to be defined by the planning personnel based on their experience. Moreover, the forecast information can be translated directly into best practices such as required resource quantities or operational plans and strategies. In both cases such an interpretation should be parameterizable by the user and, therewith, flexible with respect to operational or strategic changes. 3.1.4 Accessibility The different user types described in Sect. 2.2 access the system for different purposes and in particular from their individual workplaces. Certainly, they are all equipped with a devices that have access to the information system. Thus, the user interface has to be accessible to multiple device types such as desktop computers, tablets and smart phones using their corresponding operation systems. 3.1.5 Response time Users with operative duties expect information in real time within their commonly fast paced environment. In other words the forecast has to be presented within seconds to support ad hoc decision making. Even on a tactical level, the information has to be provided continuously, whereas the management has rather low requirement regarding the systems response time. The presented system relies on sufficient input data to produce satisfactory forecasts. Corresponding base information is given by historical data which is typically hosted by the logistic node. The suggested intelligent forecasting methodology will furthermore utilize external information to increase the forecasting quality. In both cases the system has to adapt to the interfaces provided by the host systems for the sources described below. 3.2.1 Historical data The historical data which is relevant for the forecas ...
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Final Answer

As promised, here is your paper. Thanks for reminding me do send it over as I knew you need it earlier to be completed but forgot to actually sned it earlier too (different deadlines can sometimes be confusing). Anyway here you have the file.As you can see, the paper is written in APA format and contains The file contains a total of 13 pages (1 cover page + 1 reference page + 11 content page). That's a total of 3396 words. As you will be able to see based on the paper, question 1 was the most complex one and also had the longest answers (1474 words). I also included all your case studies which were provided in order to deliver the paper.Nevertheless,I understand the importance of originality in the academic field. The entire paper was written by me and you will see that it is fully original.Hope you enjoy the paper.

This file is intended for personal use. It does not represent the file that should be sent to the professor. Its only role
is to highlight general information about the paper or add any additional ones which were unsuitable for the essay
but might help the student have a better understanding of the topic.

1.General Information About the Paper
File Name: Business Final Task
Format: APA
Type : Essay
Number of Words: 3396 words
Pages: 14 pages (1 cover page + 1 reference page + 12 content pag)
Subject: Business
References: No
In text citation: No

*The guideline given by your professor was correctly followed
*All the questions requested were answered and were included in the paper with
the suitable references.



Essay Assignment
Student’s Name
Institution Affiliation


Essay Assignment


The information systems described in these articles don't fall neatly

into a single IS category such as TPS, MIS, DSS, ESS, ERP, SCM, CRM, KMS,
collaboration environments, GIS, GDSS, etc. Rather, most seem to possess
functionalities from more than one category. Identify and discuss the multiplicity of
these categories for each system.
Companies in different sectors of the economy, starting from government institutions,
sports, logistics firms, among others, strive to incorporate technology to improve their business
processes. Since the model of operations within these institutions are different, different
Information Systems are implemented to either improve their performances or enable an entirely
new process. The business process involves the flow of materials or information within the
institution. It can be generalized as a collection of business operations, including manufacturing
and production, finance, human resource, among others. The articles described in the study cover
health, business, human resource, and sports sectors. In the study, information systems described
cannot be categorized singly into a specific category, such as the decision support system (DSS).
Information systems can be categorized simultaneously due to their functionalities within the
The use of technology in sports has exhibited a tremendous amount of changes in the past
years. Previously, sporting activities were all about fun and play. The use of technology was
never incorporated into sporting activities until productivity and efficiency came into play. A
research conducted by Robert E. Becker, a researcher in sports management, together with his
counterpart, Ted Kwartler, proved the revolution of technology in sports. The study conducted
between two National Football League (NFL) teams, used logistics regression software – a



decision support system- to classify their upcoming play types (Baker & Kwartler, 2015).. Even
though the software can be categorized as a decision support system, it cannot fall neatly into the
category. The software exhibits several characteristics possessed by other Information Systems
(IS). As a decision support system (DSS), the open-source logistics regression software is datadriven. In the study, data collected 26.310 data points within 13 recent seasons of the two teams.
The data collected would aid the management of the team to make critical decisions such as
change of play type based on data collected from the opponent's analysis. The software showed a
capability as an in-game aid to determine the opponent’s behavior. The accuracy of data
collected in the study showed the software was 66.4% correct on Cleveland’s offensive play
types, and 66.9% on Pittsburgh’s. Further precision of the software used could output improved
results in change controls of coordinators, head coaches, players, and other factors such as
weather that influences play types. As an executive support system (ESS), the regression
software supports senior management and addresses non-routine decisions that require judgment
and evaluation.
Sanjeev Arora, a liver specialist, was facing a rough time handling patients in New
Mexico. The number of patients seeking his services was overwhelming, not neglecti...

Robertmariasi (5608)
Rice University

Top quality work from this tutor! I’ll be back!

It’s my second time using SP and the work has been great back to back :) The one and only resource on the Interwebs for the work that needs to be done!

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