IS final

structured process

formally defined, standardized processes involving day-to-day operations, i.e, a salesclerk accepting a return.

Dynamic Processes

flexible, informal, and adaptive processes involving strategic and less structured managerial decisions and activities, i.e, deciding whether to open a new store location. Usually require human judgement

Business Process

a network of activities that generates value by transforming inputs into outputs

Workgroup Process

fulfills the purposes and goals of a particular group or department

workgroup IS

aka functional information system, exists to support 1+ processes within the workgroup, i.e, operations department implements IS system to support ops process

workgroup IS

typically supports 10-100 users, procedures for use often formalized in documentation, workgroups can duplicate data; problem solutions within group; somewhat difficult to change

Enterprise IS

supports 1+ enterprise processes, 100-1000+ users, procedures formalized, problem solutions affect enterprise; eliminate workgroup data duplication; difficult to change; users must undergo formal training procedures

Inter-enterprise IS

support 1+ inter-enterprise processes, 1000+ users; systems procedures formalized; problem solutions affect multiple organizations; can resolve problems of duplicated enterprise data; very difficult to change

Enterprise Processes

span an organization, support activities in multiple departments

Enterprise IS

data duplication either fully eliminated or consistent across duplicates

Enterprise IS

changes to system are difficult due to amount of users and must be carefully planned

Enterprise IS

CRM, ERP, EAI

Inter-enterprise processes

span 2+ independent organizations, i.e, buyinf healthcare insurance via healthcare exchange

Inter-enterprise IS

problems resolved by meeting, contract or litigation

Inter-enterprise IS

data often duplicated between organizations– must be eliminated or carefully managed

supply chain management

inter-enterprise IS

Process quality

one of the most important determinants of organizational success

Process Quality Dimensions

Efficiency and Effectiveness

Process efficiency

measure of the ratio of process outputs to inputs, i.e, if an alternative produces the same order approvals/rejections at a lower cost it’s more efficient

Process Effectiveness

measure of how well a process achieves organizational strategy

Improving processes

change process structure, change process resources, change both

IS improves process quality

performs activity, augments a human who is performing an activity, controls data quality and process flow

information silo

condition that exists when data are isolated in separated information systems

information silo issue

data duplication and integrity

information silo issue

data reconciliation made difficult, inefficient when decisions are made in isolation, increased organizational cost

Solving info silos

integrate data into single database, revise apps to use it

business process reengineering

* **activity of altering existing and designing new business processes to take advantage of new IS; difficult, slow, expensive; requires high level skills and considerable time**

Enterprise application solutions

software solutions used to conduct business operations and processes. systems built in-house are expensive, initial development cost, cost of continuous adaptation, becomes infeasible as apps become more complex

inherent processes

* **predesigned procedures for using software products**
* **almost never a perfect fit**

CRM

**customer relationship management**

CRM system

**a suite of applications, database and inherent processes for managing all customer interactions, every interaction, contact and transaction w customer recorded here**

four phases of CLC (customer life cycle)

marketing, customer acquisition, relationship management, loss/churn

enterprise resource planning (ERP)

* suite of applications (**modules),** a database, and set of inherent processes for consolidating business ops into single, consistent, computing platform

ERP system

* **IS based on erp tech, include functions of CRM systems but also incorporate accounting, manufacturing, inventory and human resource applications** 

ERP system

primary purpose is integration, allows real time updates globally

enteprise application integration

* When ERP isn’t appropriate, use this to solve information silo related issues

enteprise application integration

* suite of software applications that integrates existing systems by providing layers of software that connect applications together

enteprise application integration

* connects system islands via new layer of software/system
* enables existing applications to communicate and share data
* provides integrated information
* leverages existing systems, leaving functional applications as is but providing integration layer over top
* Enables gradual move to ERP

EAI Software

can be configured to automatically carry out data conversions required to make data compatible among different systems

EAI

no centralized database, but software keeps files of metadata that describes data location and format

ERP system elements

* must include applications that integrate:
* supply chain
* manufacturing 
* CRM
* Accounting
* Human resources

PaaS

**replace orgs existing hardware infrastructure w hardware in the cloud, install ERP software and databases on that cloud hardware,** 

PaaS

larger installations more likely to move to this

PaaS

on-demand access to a complete, ready-to-use, cloud-hosted platform for developing, running, maintaining and managing applications

SaaS

on-demand access to ready-to-use, cloud-hosted application software

SaaS

smaller installations or those with new ERP systems likely to use this

SaaS

* **acquire cloud-based ERP solution, i.e, SAP, oracle, microsoft, vendor manages erp software and offers to customers as a service**

ERP database program code

trigger, stored procedure

ERP db

* includes database design and initial configuration data

trigger

* **comp program stored within database that runs to keep database consistent when certain conditions arise**

stored procedure

* **comp program stored in DB used to enforce business rules, i.e, never sell this item at a discount**

process blueprints

inherent processes defined in erp solution

industry specific solutions

* starter kits for specific industries, contain program and db configurations files as well as process blueprints that apply erp implementations to specific industries 

challenges of industry specific solutions

* **Collaborative management: no clear boss, must collaborate between departments and customers, needs to develop some sort of collaborative management for resolving process issues, slow and expensive, develop committees and steering groups**
* **Requirements Gaps: licensed products never a perfect fit for company, usually gap exists between orgs requirements and apps capabilities**
* transition problems
* employee resistance: in order to combat this, communicate why change is necessary and reiterate throughout transition proess, change threatens self efficacy, need to be trained and coached on successful use of new system
* **New Tech: emerging new technology affects enterprise systems because of importance and value, hybrid models may need to be devised, i.e, work with cloud**

Solving requirement gaps

* identify gaps, what does it need and what does new product do, features of complex product like CRM or ERP not easy to identify, major task
* Decide what to do with gaps once identified, either org needs to change how they do things to adapt to new app or app must be altered to match what org does

Transition problems

**have to change from isolated systems while continuing to run business**

benefits of erp

felt mostly by acc, finance and senior management **departments, so many employees asked to change will not receive direct benefit. extra incentive may be needed** 

Dimensions of knowledge

Data, knowledge, wisdome

data

**the flow of events or transactions captured by orgs systems that are useful for transacting– to turn into info, org must expend resources to organize data into categories of understanding**

knowledge

* **info is transformed into knowledge by discovering patterns, rules and contexts where knowledge works**

Tacit knowledge

*  **knowledge that employees know that hasn’t been documented**

Explicit Knowledge

*  **knowledge that has been documented** 

wisdom

* **collective and individual experience of applying knowledge to solution of problems, where, when and how do you apply knowledge**

knowledge management value chain

* **et of business processes developed in an organization to create, store, transfer and apply knowledge**

knowledge management value chain

increases ability of org to learn from its environment and incorporate knowledge into business processes

knowledge acquisition

* utilizing and building repositories of documents, reports, presentations and best practives, develop online expert networks so employees can find the people in orgs who are personally knowledgeable 
* discovering patterns in data via machine learning

knowledge application

* **if it cannot be shared and applied, it is not useful to an org, must provide ROI and become a systemic part of management decision making**
* **must be built into firms business processes and key application systems, including ERPs** 

Communities of practice (COPs)

* **informal social networks of professionals and employees within and outside firm who have similar work related activities and interests, can make it easier for people to reuse knowledge by pointing community members to useful docs, creating doc repositories and filtering info for newcomers**

**Types of knowledge management systems**

**Enterprise wide knowledge management systems, Knowledge work systems**

**Enterprise wide knowledge management systems**

* **general purpose firm-wide efforts to collect, store, distribute and apply digital content and knowledge**
* **can search for info, store structured and unstructured data, locate employee expertise, supporting technologies** 

**Knowledge work systems**

* **specialized systems built for engineers, scientists and knowledge workers, who create new knowledge for companies**
* **ex: CAD systems, VR systems for simulation and modeling**

CAD

* **automates the creation and revision of designs using comps and sophisticated graphics software**

AI

* **intelligent techniques, involves attempt to build computer systems that think and act like humans**
* **narrower definition– programs take data input from the environment, process the data and produce outputs**

drivers of AI evolution

development of big data: db generated by internet, e-commerce, internet of things, social media

* secondary drivers, reduction in costs of computer processing and growth in processing power
* last, refinement of algorithms and significant investment

expert systems

first large-scale apps of AI in orgs, 20% of all AI today, capture the knowledge of individual experts in orgs through in-depth interviews and represent the knowledge as a set of rules, converted into computer code, often used to develop apps that walk users through process of decision making

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* **Benefits: improved decisions, reduced errors, reduced costs, reduced training time, better quality and service**
* **Rules: rules are interconnected, number of outcomes known in advance and limited, model human knowledge as a set of rules that’s collectively called the knowledge base, searching through collections of rules and formulating conclusions is called an inference engine**
* **Limitation: we don’t understand how they make decisions, knowledge base can become chaotic as rules grow in amount, rules have to be changed and added continuously** 
* **expert systems aren’t useful for unstructured problems, don’t use real time data to aid in decisions**
* **don’t scale well, expensive to build**

machine learning

* **software that can identify patterns in large db without explicit programming– significant human training**
* **more than 75%, main focus is finding patterns in data and classifying into known and unknown outputs**
* **begins with large data set and finds patterns, making statistical inferences, i,e, social media ads**

machine learning

* **software that can identify patterns in large db without explicit programming– significant human training**
* **more than 75%, main focus is finding patterns in data and classifying into known and unknown outputs**
* **begins with large data set and finds patterns, making statistical inferences, i,e, social media ads**

neural networks and deep learning

* **algorithms can be trained to classify objects into known categories based on data inputs, deep learning uses multiple layers of neural networks to reveal underlying patterns in data**
* **composed of interconnected units (neurons)**
* **neurons are  software programs and mathematical models that can take and transfer data from other neurons**
* **strength of connections can be controlled using learning rule, algorithm that systematically alters strength of connections among neurons to produce final desired output** 
* **find relationships in large amounts of data, too complicated and difficult for human beings to analyze with computational models or machine learning pattern detection programs**
* **using learning rule, successful paths are identified and these connections are strengthened between neurons, learning rule identifies best or optimal pathways through the data**
* **process stops when an acceptable level of pattern recognition is reached, i.e, identifying cancer tumors as well as or better than humans**
* **ex: computer vision, speech recognition, diagnostics, language translation, targeted online ads**

**Deep learning neural networks**

* collections of neurons = nodes/layers
* still young software, used for pattern detection or unlabeled data where system isn’t told what tol ook for specifically, just discovers patterns, expected to be self-taught


* **Limitations: require very large data sets to identify patters,often patterns are non-sensical in large data, takes humans to choose whicho ones are not, many patterns are ephemeral, don’t know how system arrives at particular solutions**

genetic algorithms

* **used to generate high-quality solutions to optimization and search problems**
* **form of machine learning, finds optimal solution by examining large number of alternative solutions**
* **based on ideas inspired by inheritance, mutation, selection and crossover**
* **used to solve dynamic and complex problems involving thousands of variable and formulas**

natural language processing

* **understand and analyze human language**
* **makes it possible to analyze natural language, i.e, slang, regional dialects, social context**
* **language used by humans, not specially formatted to be understood by computers**
* **based on machine learning, deep learning**

computer vision systems

* can view and extract info from real world images
* how computers can emulate human visual system to view and extract info from real-world images
* image-processing, pattern recogniation, image understanding
* ex: facebook deepface facial recognition 

robotics

* **use of machines that can substitute for human movement, and computer systems for control and processing**
* **robots programmed to perform specific series of actions automatically, often used in dangerous environments, manufacturing, military ops and medical procedures**
* **most widespread use in mfg**

intelligent agents

* **software agents use built-in or learned knowledge to perform specific tasks or services for individual users, business processes, or software apps**
* **no direct human intervention**
* **uses limited built-in or learned knowledge base to accomplish tasks or make decisions on users behalf, ex: deleting junk mail, scheduling appointments**

chatbots

intellignet agents, **designed to simulate conversation w one+ human users via text or phone**

supervised learning

* system is trained by providing specific examples of desired inputs and outputs identified by humans in advance, large database is developed and split into two, development database and test database
* humans select a target, i.e photos with a car, and feed it to the machine that eventually learns photos w a car, then tested using test db to ensure algorithms can achieve same results w different photos
* technique used to develop autonomous vehicles

unsupervised learning

* same procedures followed, but humans don’t feed system examples, instead system asked to process development database and report patterns
* neural network learning
* principle is that machines can teach themselves about the world without human intervention, but far from perfected technique