Developments in the global economy have changed the traditional balance between customer and supplier

Keywords: business models, business strategy, innovation, customer-centric, David J. Teece

Business Models, Business Strategy and Innovation

By David J. Teece

Whenever a business enterprise is established, it either explicitly or implicitly employs a particular business model that describes the design or architecture of the value creation, delivery, and capture mechanisms it employs. The essence of a business model is in defining the manner by which the enterprise delivers value to customers, entices customers to pay for value, and converts those payments to profit. It thus reflects management’s hypothesis about what customers want, how they want it, and how the enterprise can organize to best meet those needs, get paid for doing so, and make a profit. The purpose of this article is to understand the significance of business models and explore their connections with business strategy, innovation management, and economic theory.

Developments in the global economy have changed the traditional balance between customer and supplier. New communications and computing technology, and the establishment of reasonably open global trading regimes, mean that customers have more choices, variegated customer needs can find expression, and supply alternatives are more transparent. Businesses therefore need to be more customer-centric, especially since technology has evolved to allow the lower cost provision of information and customer solutions. These developments in turn require businesses to re-evaluate the value propositions they present to customers in many sectors, the supply side driven logic of the industrial era has become no longer viable.

This new environment has also amplified the need to consider not only how to address customer needs more astutely, but also how to capture value from providing new products and services. Without a well-developed business model, innovators will fail to either deliver  or to capture value from their innovations. This is particularly true of Internet companies, where the creation of revenue streams is often most perplexing because of customer expectations that basic services should be free.

A business model articulates the logic and provides data and other evidence that demonstrates how a business creates and delivers value to customers. It also outlines the architecture of revenues, costs, and profits associated with the business enterprise delivering that value. The different elements that need to be determined in business model design are listed in Figure 1. The issues related to good business model design are all interrelated, and lie at the core of the fundamental question asked by business strategists e how does one build a ... Read more

Business Term of The day: "Academy Company"

Keywords: academy company

Definition of academy company

Academy company is a term used for an organization that is well known as a place to start a professional career and provides leaders to other companies. Often academy companies hire the majority of their staff from recent college and university graduates, and provide extensive training. Academy companies are frequently targeted by executive search firms as sources of talent. Often academy companies have "up or out" policies that facilitate organizational growth and development.

Examples of academy companies:

• PepsiCo
• Procter & Gamble
• General Mills
• Kraft Foods
• Goldman Sachs
• JPMorgan Chase
• General Electric
• McKinsey & Company
• Bain & Company
• Boston Consulting Group
• Hewlett-Packard
• Unilever

5S Methodology

Keywords: 5S methodology

Source Wikipedia

5S is the name of a workplace organization method that uses a list of five Japanese words: seiri, seiton, seiso, seiketsu, and shitsuke. Transliterated into Roman script, they all start with the letter "S". The list describes how to organize a work space for efficiency and effectiveness by identifying and storing the items used, maintaining the area and items, and sustaining the new order. The decision-making process usually comes from a dialogue about standardization, which builds understanding among employees of how they should do the work.

In some quarters, 5S has become 6S, the sixth element being safety.

Other than a specific stand-alone methodology, 5S is frequently viewed as an element of a broader construct known as visual control, visual workplace, or visual factory. Under those (and similar) terminologies, Western companies were applying underlying concepts of 5S before publication, in English, of the formal 5S methodology. For example, a workplace-organization photo from Tennant Company (a Minneapolis-based manufacturer) quite similar to the one accompanying this article appeared in a manufacturing-management book in 1986.

The 5 S

There are five 5S phases: They can be translated from the Japanese as "sort", "set in order", "shine", "standardize", and "sustain". Other translations are possible.

Sort (Seiri)

• Make work easier by eliminating obstacles.
• Reduce chances of being disturbed with unnecessary items.
• Prevent accumulation of unnecessary items.
• Evaluate necessary items with regard to cost or other factors.
• Remove all parts or tools that are not in use.
• Segregate unwanted material from the workplace.
• Define Red-Tag area to place unnecessary items that cannot immediately be disposed of. Dispose of these items when possible.
• Need fully skilled supervisor for checking on a regular basis.
• Waste removal.
• Make clear all working floor except using material.

Set In Order (Seiton)

• Arrange all necessary items so that they can be easily selected for use.
• Prevent loss and waste of time by arranging work station in such a way that all tooling / equipment is in close proximity.
• Make it easy to find and pick up necessary items.
• Ensure first-in-first-out FIFO basis.
• Make workflow smooth and easy.
• All of the above work should be done on a regular basis.
• Maintain safety.
• Place components according to their uses, with the frequently used components being nearest to the work place.

Shine (Seiso)

• Clean your workplace on daily basis completely or set cleaning frequency
• Use cleaning as inspection.
• Prevent machinery and equipment deterioration.
• Keep workplace safe and easy to work.
• Keep workplace clean and pleasing to work in.
• When in place, anyone not familiar to the environment must be able to detect any problems within 50 feet (15 meter) in 5 secs.

Standardize (Seiketsu)

• Standardize the best practices in the work area.
• Maintain high standards in workplace organization at all times.
• Maintain orderliness. Maintain everything in order and according to its standard.
• Everything in its right place.
• Every process has a standard.

Sustain (Shitsuke)

• Not harmful to anyone.
• Also translates as "do without being told".
• Perform regular audits.
• Training and discipline.
• Training is goal-oriented process. Its resulting feedback is necessary monthly.
• Self discipline
• To maintain proper order

The origin of 5S

5S was developed in Japan and was identified as one of the techniques that enabled Just in Time manufacturing.

Two major frameworks for understanding and applying 5S to business environments have arisen, one proposed by Osada, the other by Hirano. Hirano provided a structure to improve programs with a series of identifiable steps, each building on its predecessor. As noted by John Bicheno, Toyota's adoption of the Hirano approach was '4S', with Seiton and Seiso combined

.

In case of development of the Japanese system of management were used Alexey Gastev's development and the Central Institute of Labour (CIT) in Moscow.

Variety of 5S Applications

5S methodology has expanded from manufacturing and is now being applied to a wide variety of industries including health care, education, and government. Visual management and 5S can be particularly beneficial in health care because a frantic search for supplies to treat an in-trouble patient (a chronic problem in health care) can have dire consequences. Although the origins of the 5S methodology are in manufacturing, it can also be applied to knowledge economy work, with information, software, or media in the place of physical product.

5S in Lean Product & Process Development

The output of engineering and design in a lean enterprise is information, the theory behind using 5S here is "Dirty, cluttered, or damaged surfaces attract the eye, which spends a fraction of a second trying to pull useful information from them every time we glance past. Old equipment hides the new equipment from the eye and forces people to ask which to use"

Terms related to 5S:

Just-in-time manufacturing

Kaikaku

Kaizen

Kanban

Knolling

Lean manufacturing

Muda

Alibaba's Jack Ma is one of the richest men

Keywords: Alibaba, Jack Ma, Chinese e-commerce

How Alibaba’s Jack Ma Is Building a Truly Global Retail Empire

By Adam Lashinsky - Fortune

March 31, 2017

Jack Ma is one of China’s richest men, with a fortune valued at nearly $30 billion. As executive chairman of Alibaba Group, he leads the dominant force in Chinese e-commerce, a company with a market value of $264 billion and some 450 million customers. A global ambassador for Chinese business, he spent 800 hours aloft last year-visiting princes, Presidents, and Prime Ministers and lots of mere businesspeople too. “A professional pilot cannot travel that much, or so I’m told,” he boasts.

Even so, the rich and powerful people who meet with Ma tend to come away from the experience with a fresh nugget of information, either about him or about the still poorly understood digital conglomerate he started with a bunch of friends 18 years ago in the provincial coastal city of Hangzhou. Jim Kim, a physician who is the president of the World Bank, met Ma four years ago over a dinner lasting more than three hours and was startled to find the billionaire wearing sandals, holding Buddhist prayer beads, and sitting cross-legged on his chair. Kim was so taken with Ma’s passion for facilitating global trade by focusing on small-business people that he’s rethinking his international development organization’s approach.

Others are moved by Ma’s humanity. Jean Liu, president of Chinese ride-hailing startup Didi Chuxing, has known Ma for years and considers him a mentor. (Alibaba is a Didi shareholder.) She recently learned, through family connections rather than from Ma, about how he repeatedly visited a seamstress he had met after learning she was ill. Says Liu: “He genuinely cares about the people around him.”

Then there’s the President of the United States, who met Ma for the first time a few weeks before his Inauguration. “Trump didn’t know that much about Alibaba,” reports company president Michael Evans, a former Goldman Sachs banker and Asia hand who helped set up the powwow. “He was fascinated to hear that Chinese consumers are interested in buying from U.S. small businesses. I don’t think that had occurred to him.” Ma used the sit-down to make a bold promise-that Alibaba would help create 1 million jobs in the U.S. over five years. The pronouncement was music to the President-elect’s ears. “It was a great meeting,” he declared before the cameras in the lobby of Trump Tower, a beaming Ma beside him. “Jack and I are going to do some great things.”

President Trump isn’t the only one who could stand to learn more about Alibaba. Despite its heft in China and the blockbuster 2014 public offering that raised $25 billion on the New York Stock Exchange and introduced Alibaba to Western investors, Ma’s company remains a mystery to most non-Chinese. There’s a simple reason for that: Few outside the world’s second-largest economy are Alibaba customers. Ma is aware of this knowledge gap. It’s part of what drives him to keep logging frequent-flier miles to educate people about his company and his plans.

To realize his vision-which relies on technology to buy, sell, finance, and deliver goods on Alibaba’s digital platforms around the world-Ma has been busily recasting himself of late as a global leader. Already he is the first Chinese business executive who can claim to have transcended his homeland for the world stage. In his travels, Ma promotes the lowering of trade barriers, touts his own brand of philanthropy, and supports causes such as primary-school education. His version of globalization is carefully calibrated and expansive enough to be consistent with the goals of his own President, Xi Jinping, as well as with Trump’s America-first positioning. Read more on Yahoo! >>>

Federal Judge Approved Trump University Lawsuit Settlement

Trump University Suit Settlement Approved by Judge

By STEVE EDER and JENNIFER MEDINA

MARCH 31, 2017

Keywords: Trump university lawsuit, settlement, Judge Gonzalo P. Curiel, Sherri Simpson

A federal judge on Friday gave final approval to a $25 million agreement to settle fraud claims arising from Donald J. Trump’s for-profit education venture, Trump University, rejecting a last-minute objection to the deal.

The judge, Gonzalo P. Curiel, in San Diego, issued his order after considering a challenge from Sherri Simpson, a former Trump University student from Fort Lauderdale, Fla., whose lawyers say she should have had a chance to opt out of the class-action settlement and individually sue President Trump, perhaps forcing a trial.

The civil settlement was not enough for Ms. Simpson, who wanted to see Mr. Trump tried on criminal racketeering charges. She also wanted an apology.

But Judge Curiel, in his ruling, sided with the class-action plaintiffs’ lawyers, who had urged him to approve the agreement, saying it was the best possible outcome for roughly 3,730 students. They could recoup more than 90 cents on the dollar of what they spent at Trump University.

“The court finds that the amount offered in settlement is fair, adequate, and reasonable, and accordingly concludes that this factor weighs in favor of final approval,” wrote Judge Curiel, who approved the agreement and dismissed the objection in a 31-page order. It is subject to appeal.

The approval of the settlement, assuming it stands, brings to a close a case that garnered outsize national attention during Mr. Trump’s presidential campaign. He faced two suits in California and one in New York brought by Eric T. Schneiderman, the state attorney general.

The suits contended that Trump University students had been cheated out of thousands of dollars in tuition through high-pressure sales techniques and false claims about what they would learn. Mr. Trump and his ... Read more on New York Times

Demand for predictive intelligence is fundamentally changing the way information is consumed and used in business

Keywords: business intelligence, prescriptive intelligence, business analytics, data mining, predictive analytics

Traditionally, business intelligence (BI) has looked backward at what has happened. In today’s marketplace, enterprises need to look ahead. From predictive to prescriptive intelligence, we look at what businesses need most with David Clement, product marketing manager for IBM Business Analytics. Customers are looking at descriptive intelligence (what is happening now) and predictive intelligence (what is going to happen next). Ultimately, the goal is to get to prescriptive intelligence—what should I do and what will that do to my business. Forward-looking business intelligence is the way to go beyond descriptive intelligence—which has long been a staple—to predictive intelligence, which, in turn, paves the way to prescriptive intelligence.

How does forward-looking business intelligence help decision makers and lower risk?

Combining past, present, and future views of one’s data side by side allows analysts, department managers, and executives to make better, more informed decisions. By aligning with company goals and business plans, business users become more dependent and responsive to the data being used to validate their decisions. Although gut feel and experience are always going to play a role in how data is used to improve business choices and direction, validating those choices has become more important and liability has gone up. Backed by organizationwide data systems that articulate the position and predictive insights of the business prerogatives, using business intelligence and predictive analytics adds value and competitive advantage and lowers the risk of bad decisions.

What is the difference between data mining and predictive analytics?

Data mining provides the methodology for getting predictive intelligence out of your data from a technical perspective. Predictive analytics is a type of analytics and data mining is what a business user, business analyst, or data scientist actually does. Data mining at its essence is about finding the natural patterns, relationships, and outcomes within your data. Predictive analytics is more than just using algorithms and understanding models. For organizations, it’s about being able to use the results of data mining to effect positive business outcomes.

What are businesses looking to get help with?

Revenue generation is critical to a business. However, understanding how best to drive revenue growth can be arduous. Lines of business are looking to have precise awareness of their operations, and technology factors play a larger role than ever in maximizing this responsiveness to growing the business. Today’s small-to-large businesses have much more in common in terms of their needs to identify market trends, understand customer behavior, tackle inefficiencies sooner, make sense of the explosion of data to stay ahead of the competitive curve, and make impactful and smarter decisions that align with company goals. Read more...

Business Analytics: Evolution, Applications, and Emerging Research

Business Intelligence And Analytics: From Big Data To Big Impact

• Hsinchun Chen, Eller College of Management, University of Arizona,
• Roger H. L. Chiang, Carl H. Lindner College of Business, University of Cincinnati,
• J. Mack Robinson College of Business, Georgia State University

Keywords: Business intelligence and analytics, big data analytics

Business intelligence and analytics (BI&A) and the related field of big data analytics have become increasingly important in both the academic and the business communities over the past two decades. Industry studies have highlighted this significant development. For example, based on a survey of over 4,000 information technology (IT) professionals from 93 countries and 25 industries, the IBM Tech Trends Report (2011) identified business analytics as one of the four major technology trends in the 2010s. In a survey of the state of business analytics by Bloomberg Businessweek (2011), 97 percent of companies with revenues exceeding $100 million were found to use some form of business analytics. A report by the McKinsey Global Institute (Manyika et al. 2011) predicted that by 2018, the United States alone will face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as a shortfall of 1.5 million data-savvy managers with the know-how to analyze big data to make effective decisions.

Hal Varian, Chief Economist at Google and emeritus professor at the University of California, Berkeley, commented on the emerging opportunities for IT professionals and students in data analysis as follows:

So what’s getting ubiquitous and cheap? Data. And what is complementary to data? Analysis. So my recommendation is to take lots of courses about how to manipulate and analyze data: databases, machine learning, econometrics, statistics, visualization, and so on.

The opportunities associated with data and analysis in different organizations have helped generate significant interest in BI&A, which is often referred to as the techniques, technologies, systems, practices, methodologies, and applications that analyze critical business data to help an enterprise better understand its business and market and make timely business decisions. In addition to the underlying data processing and analytical technologies, BI&A includes business-centric practices and methodologies that can be applied to various high-impact applications such as e-commerce, market intelligence, e-government, healthcare, and security.

Data management and warehousing is considered the foundation of BI&A 1.0. Design of data marts and tools for extraction, transformation, and load (ETL) are essential for converting and integrating enterprise-specific data. Database query, online analytical processing (OLAP), and reporting tools based on intuitive, but simple, graphics are used to explore important data characteristics. Business performance management (BPM) using scorecards and dashboards help analyze and visualize a variety of performance metrics. In addition to these well-established business reporting functions, statistical analysis and data mining techniques are adopted for association analysis, data segmentation and clustering, classification and regression analysis, anomaly detection, and predictive modeling in various business applications. Most of these data processing and analytical technologies have already been incorporated into the leading commercial BI platforms offered by major IT vendors including Microsoft, IBM, Oracle, and SAP (Sallam et al. 2011).

Many marketing researchers believe that social media analytics presents a unique opportunity for businesses to treat the market as a “conversation” between businesses and customers instead of the traditional business-to-customer, one-way “marketing” (Lusch et al. 2010). Unlike BI&A 1.0 technologies that are already integrated into commercial enterprise IT systems, future BI&A 2.0 systems will require the integration of mature and scalable techniques in text mining (e.g., information extraction, topic identification, opinion mining, question-answering), web mining, social network analysis, and spatial-temporal analysis with existing DBMS-based BI&A 1.0 systems.

Except for basic query and search capabilities, no advanced text analytics for unstructured content are currently considered in the 13 capabilities of the Gartner BI platforms. Several, however, are listed in the Gartner BI Hype Cycle, including information semantic services, natural language question answering, and content/text analytics (Bitterer 2011). New IS and CS courses in text mining and web mining have emerged to address needed technical training. Download the article (24 pages)