Module 7 Flashcards

1
Q

What are the systems of different management groups? Describe them.

A
  1. Transaction processing system (TPS)
    *Systems that record data on fundamental operations occurring within the company
    *Tracks flow of transactions
    *Questions: What happened with this customer’s order? How many items do we still have in stock?
    (Scheme)
  2. Business Intelligence Systems (BIT)
    *Support management decision making for different levels
    * represent input side; business intelligence tools, as well as more sophisticated analytics systems, represent the output side
    *Responsible for producing information and insights from the data
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2
Q

Which systems support decision making? Name them and their differences.

A
  1. (MIS)
    *Serve middle management.
    *Provide reports on firm’s current performance, based on data from TPS.
    *Answers routine questions
    *Little analytical management
    (see MIS Report on slides for example)
  2. (DSS)
    *Serves middle management.
    *Supports non-routine decision making.
    *May use external info as stock prices etc.
    Has analytical capability. Forecasting, stating impact etc.
    KPI’s key performance indicators.
  3. (ESS)
    *Supports senior management.
    *non-routine, requires judgement, evaluation, insight.
    *Incorporated data about external events (new tax laws, competition etc.)
    * Summarised MIS & DSS
    Example: digital dashboard w/real-time view of financial performance.
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3
Q

How does business deal with managing all the decision making systems?

A

<Describe>

Enterprise applications- Systems that span functional areas. Execute business processes across the firm, including all levels of management.

Four major enterprise applications:
– Enterprise systems
– Supply chain management systems
– Customer relationship management systems
– Knowledge management systems
</Describe>

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4
Q

Name 4 “Enterprise applications”.

A

Four major enterprise applications:
– Enterprise systems
– Supply chain management systems
– Customer relationship management systems
– Knowledge management systems

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5
Q

What are Enterprise Systems?

A

ERP- Enterprise resource planning system. Aim at consolidating data collected and processed in various departments or branches of the company.
*Integrates core functions of the company (such as accounting, HR, procurement, sales)
*Speed communication of information throughout the firm.
*Enable greater flexibility in responding to customer requests, greater accuracy in order fulfilment.
EXAMPLE: SAP&ORACLE

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6
Q

What is the value of Enterprise Systems?

A
  1. Increase operational efficiency.
  2. Provide firm-wide information to help managers make better decisions.
    3.Help firms respond rapidly to customer requests for information or products.
  3. Analytical tools are available, informing corporate headquarters in real-time.
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7
Q

What do Supply Chain Management Systems do?

A

Provides a holistic overview of value chain, including flow of raw materials, deliveries, inventories.
*Manage relationships with suppliers, purchasing firms, distributors, and logistics companies
*Manage shared information about orders, production, inventory levels, and so on.
*Goal- move correct amount of product from source to point of consumption as quickly as possible and at lowest cost.
*Automating info flow.

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8
Q

What is Bullwhip effect and to which system it belongs to?

A

SCM- Supply chain management system.
Bullwhip effect- info about product demand gets distorted as it passes from one entity to next in supply chain.
“Pampers” example in lecture.

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9
Q

What is included in upstream in supply chain?

A

Tier 1 supplier (contract suppliers to the company)
Tier 2
Tier 3

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10
Q

What is included in down-stream in supply chain?

A

Distributor <—>Retailer <—>Customer

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11
Q

Explain push-pull relationship in supply chain .

A
  • Push-based model (build-to-stock): based on forecasts or best guesses of demand for products and then pushed to customers
  • Pull-based model (demand-driven or build-to-order): customer purchases trigger events in the supply chain
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12
Q

What is CRM System? Name it’s tools.

A

CRM- Customer Relationship Management
Integrates customer data to be used by various departments (sales, marketing, support).
Tools-
*Sales force automation (sales prospect and contact information,
sales quote generation capabilities)
*Customer service (assigning and managing customer service
requests, web-based self-service capabilities)
* Marketing (capturing prospect and customer data, scheduling
and tracking direct-marketing mailings or e-mail, cross-selling)

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13
Q

What is a touch point in CRM System?

A

A touch point (also known as a contact point) - method of interaction with the customer, such
as telephone, email, customer service desk, Facebook etc.)

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14
Q

What is KMS?

A

KMS- Knowledge Management Systems.
* Manage processes for capturing and applying knowledge and expertise
* Collect relevant knowledge and make it available wherever needed in the enterprise to improve business processes and management decisions.
* Link firm to external sources of knowledge

What are examples of firm knowledge? What does it take to run a fast food restaurant or operate a website?

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15
Q

Where are all the data stored?

A
  1. Data warehousing
    * Data warehouses- data consolidated and standardised, cannot be altered.
    *Data marts- subsets of data in data warehouses.
  2. Online Analytical Processing (OLAP)
    Transaction-level data stored in relational databases is aggregated and summarised.
    Results of analysis are stored in special databases- data cubes.
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16
Q

What are data cubes? Describe them.

A

Results of analysis are stored in special databases- data cubes.
Data cubes structure results across multiple (i.e. not limited to three) dimensions, such as:
* Space (store locations, countries)
* Products
* Time period (by week, month, year)
Running queries on data cubes enables substantially quicker response times than running them on the original database

17
Q

What refers to the use of specific algorithms to identify hidden patterns in and to fit models to large data sets?

A

Data mining.
P.S. data mining is more discovery driven.

18
Q

What are the patterns of data mining?

A

– Associations (different product combinations that customers buy together)
– Clusters (a group of customers that share certain characteristics and behave in a similar way)
– Sequential (time series) relationships (dependences)

19
Q

What is association rule mining? Name two concepts.

A
  • Association rule mining seeks to identify the most frequent affinities amongst items
  • Two central concepts
    – Support s(X) is the fraction of transactions that contain a certain set of items X
    – Confidence c(X Y) is the fraction of transactions that contain Y among those
    transactions that contain X. E.g. what is the likelihood that someone who buys milk and
    bread also buys cereal – that is confidence
20
Q

Name 4 V’s of Big Data.

A

– Volume: the simple size of a dataset that needs to be processed
– Velocity: the speed with which new data is generated and needs to be processed
– Variety: the different formats and features of data that need to be processed (relational databases, documents, photos, videos, spatial and temporal aspects)
– Veracity: the reliability of the data (user-generated content, such as restaurant reviews, but also imprecisions in measurements, such as GPS positions)

21
Q

What applies methods from both classical statistics and AI to derive actionable insights from big data?

A

Analytics.

22
Q

Describe example of neural network.

A

Neural networks replicate the basic functionality of the human brain to support decision-making by predicting future outcomes.

  • Neural networks are usually trained using very large historical datasets on the outcome of interest and other variables (this may take a long time)
  • After training, once a new set of input variables is fed into the network, it can quickly predict the outcome variable
  • Neural networks are usually black box methods, i.e. it is extremely hard (and not intended) to quantify the impact of a particular input variable on the outcome