June 15, 2020

Budget, Budgeting and Budgetary Control

Online MBA Management Theory Handbook 


Budget

A budget is a formal quantitative expression of management plans.

Budgets can be made by managers at any level including a single person managing a machine or operating a machine. In the context of business, budget may have revenue, expenses and profits, all in a single statement. But one can think of a budget for revenues alone, budget for expenses alone.

Master Budget

Master budget for a big organization summarizes the goals of all subunits of an organization - either business divisions if the company is organized along divisional lines or managerial functions if the company is organized along functional lines.
The master budget consists of expected or projected income statement, balance sheet, and a cash flow statement, along with supporting schedules.

Benefits of Budgeting or Imperative for Budgeting

The advocates of budgeting state that the process of preparing budget forces executives to become better managers. Budgeting schedule of a company puts planning where it belongs - in the forefront of every manager's mind. It also forces him to review his performance in the last period and identify good practices that enhanced performance and issues that contributed negatively to performance.

The formal budgeting system has the following major benefits.

1. Budgeting due to its formal time table or schedule compels managers to think ahead apart from taking care of their current activities.
2. Budgeting, due to its approval and authorization  by the superiors, provides definite expectations that are the best framework for judging subsequent performance.
3. Budgeting helps in coordinating the various departments of the organization. The budget harmonizes the goals (objectives) of the individual departments into the organization wide goals (objectives).

Budgetary control at department level is encouraging department level personnel to plan their operations for the forth coming period. Both outputs and inputs are to be planned. If possible outputs and inputs are converted into revenues and costs.

The accounting system of the company will prepare the actual revenues and costs generated at the end of the period as well as during the period. The department managers have to responsibility to carry out the day to day activities to achieve the best possible results with their plan/budget as the guiding document.

Budgets can be made flexible so that cost estimates are in relation to the output produced.

Variance analysis can be done to pin point the variables that changed during the period and their effect on actual results.

Budgetary control system facilitates participation of department managers as well as senior level managers in explicitly planning for the future. The plan can be optimized with various optimization techniques.

These techniques include linear programming (for product mix problems), transportation (for planning transport of finished goods) and assignment (assigning machines for jobs or operators for jobs) and other operations research techniques. A formal budgeting system can question the department managers on whether they have applied the optimization techniques or not and where necessary advise them to use those techniques and provide specialist support in cases where necessary.

References

Horngren, Charles, T., Gary L. Sundem, and William O. Stratton, Introduction to Management Accounting, 13th Ed., Prentice Hall, 1999.



Videos

_________________________

_____________





_____________ _____________ ______________


For Further Study or More Information


http://www.alliancetac.com/index.html?PAGE_ID=2464


https://www.slideshare.net/Jaynegamgee/budgeting-thesis

Zero-based Mindset: Getting ahead by cutting back
OCTOBER 25, 2019
Most companies today base their cost management approaches on exactly that—setting budgets based on what happened last year. Against a backdrop of increasing volatility, it’s time to reimagine cost structures based on what’s needed in this new, disruptive environment. Basing resource demand on what’s needed now rather than on last year’s performance frees up capital that can then be used in ways that will have the most impact on building innovation and fueling sustainable growth.
https://www.accenture.com/be-en/insights/strategy/getting-ahead-cutting-back

Related Knols



Cost Accounting - More articles
Financial Accounting - More articles
Management Accounting - More articles

_______________________________________________________





 Knol no. 62


Updated on 15 Jun 2020, 16 Feb 2012

Demand Forecasting in a Supply Chain - Review Notes

Online MBA Management Theory Handbook 



Based on Chapter of Chopra and Meindl's book, Supply Chain Management: Strategy, Planning, and Operation,  

Importance of Forecasts



Forecasts of future demand are essential for supply chain management decisions.

Demand forecasts are used in supply chain design, planning as well as in operations.

Demand forecasts are used in various subcomponents of supply chain.

Production: for aggregate planning, inventory control and scheduling,
Marketing: for new product introductions, promotions, and sales-force allocation
Finance: Plant and equipment investment decisions, operating budgeting
Personnel: Workforce planning and resulting hiring and layoff.

Characteristics of Forecasts



1. Forecasts may always go wrong. Therefore a rigorous presentation of forecast should include both the expected value and a measure of forecast error.

2. Long-term forecasts are usually less accurate in comparison to short-term forecasts.

3. Aggregate forecasts are usually more accurate in comparison to disaggregate forecasts. For example, forecast of the food consumed by a group of students in a college canteen can be forecasted more accurately than the food consumed by each and every student.

Forecasting Methods



Forecasting methods fall into four categories

1. Qualitative: The forecasts are based on the human judgement and opinion. Market research falls in this category.

2. Time Series: These methods use historical demand data of an item.

3. Causal: Causal forecasting uses data of multiple variable to forecast demand of an item.

4. Simulation: Simulation methods use what if questions and come out with forecasts. The underlying models for whatif analysis are time series or causal models. Even a hybrid model can be used for simulation.

When quantitative methods are used for forecast, the effort is to isolate systematic component and random component using the available data. The systematic component gives the expected value and the variation around the expected value happens in the future periods due to the random component.

Static and Adaptive Methods of Forecasting

In a static method, a single forecasting model is applied to the currently available data to derive forecasts for all the future periods for which forecasts are to be generated. In adaptive methods, as new data arrives, the new data is incorporated into the forecasting model to derive forecasts for future periods from then on.

Basic Approach to Demand Forecasting



1. Understand the objective of forecasting: Determine the decisions which are taken based on the forecast.

2. Integrate planning and forecasting in the entire supply chain: Different units in the supply chain should not forecast separately. All the required forecasts have to be generated from uniform premises and tools.

3. Identify major factors that influence the demand: This identification helps in choosing the forecasting technique.

4. Understand and identify customer segments for which you want forecast of demand.

5. Determine the appropriate forecasting technique

6. Establish performance and error measures for forecast.



Time series methods



In static methods, estimates of level, trend, and seasonal factor are derived using the past data. These three factors give the forecast of the systematic component for future periods.

Adaptive methods:



Moving average is an adaptive method. Exponential smoothing is also an adaptive method. Holt model is trend-corrected exponential smoothing model. Winter's model is a trend- and seasonality corrected exponential smoothing model.

Measures of Forecast Errors



An estimate of the forecast error is to be given along with the forecast of an expected value. As actual values are realized, a forecast error can be calculated and managers perform error analysis to satisfy themselves that the current forecasting method is accurately predicting the systematic component of demand. Contingency plans have to be put in place to account for the predicted forecast error.


Some popular measures for forecast error are:

Mean square error
Mean absolute deviation
Mean absolute percentage error
Tracking signal


Supply Chain Demand Forecasting Using Machine Learning and Predictive Analytics


https://www.altexsoft.com/blog/demand-forecasting-methods-using-machine-learning/

https://towardsdatascience.com/machine-learning-for-supply-chain-forecast-66ef297f58f2

An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain
Complexity / 2019 / Article
Volume 2019 |Article ID 9067367 | https://doi.org/10.1155/2019/9067367
https://www.hindawi.com/journals/complexity/2019/9067367/

https://www.tcs.com/blogs/how-ml-can-take-demand-forecasting-to-next-level-in-supply-chain


References


Sunil Chopra and Peter Meindl, Supply Chain Management: Strategy, Planning and Operations, Prentice Hall, 2001.

Chamber, YJ.C., K.M. Satinder, and D.D. Smith, "How to Choose the Right Forecasting Technique," Harvard Business Review, July-August 1971, pp. 45-74.
Georgoff, David M., and Robert G. Murdick, "Manager's Guide to Forecasting," Harvard Business Review, January-February 1986, pp. 2-9.


Key to S&OP Success: Move from Forecasting to Integrated Demand Management
April 07, 2017
The Problem: Forecast Accuracy Remains a Challenge
http://www.supplychain247.com/paper/key_to_sop_success


New Addition

Top 10 Demand Planning Strategies
Learn to trust the numbers and manage by exception.
Dave Blanchard
OCT 08, 2008
https://www.industryweek.com/leadership/companies-executives/article/21942412/top-10-demand-planning-strategies


Concepts of Demand Management
The concept of demand management includes demand sensing, demand shaping, demand translation, and demand orchestration throughout the value network.
The New World of Demand Management: Demand Sensing, Shaping, and Translation.
April 4, 2013 · By Lora Cecere, founder of Supply Chain Insights
https://www.supplychain247.com/article/concepts_of_demand_management

A Practitioner’s Guide to Demand Planning
Effective demand planning doesn’t just happen, it requires work. To move forward, companies have to admit the mistakes of the past, implement continuous improvement programs to drive discipline, and carefully re-implement demand planning technologies.
July 2014 · By Lora Cecere
https://www.supplychain247.com/article/a_practitioners_guide_to_demand_planning


How Demand Planning Can Improve the Supply Chain  (Good description)
April 2, 2020
https://www.michiganstateuniversityonline.com/resources/supply-chain/how-demand-planning-improves-supply-chain/

DEMAND SENSING
Demand Forecasting is still stuck in 90s for many enterprise customers due to inherent limitations in prevalent SCM solutions. Basic time series based trend/seasonality smoothing models do not cut it any more in the world of fickle end-consumer demand and pressures to maintain service levels while reducing inventory and relieving working capital.
https://www.gitacloud.com/demand-sensing-overview


Top 100 Management Theory Articles of the blog

Article originally posted in

http://knol.google.com/k/narayana-rao/demand-forecasting-for-supply-chain/ 2utb2lsm2k7a/ 1356

Updated  15 June 2020,  8 April 2017,  20.1.2012


May 24, 2020

Component Areas of IE: Human Effort engineering and System Efficiency Engineering




New:  Industrial Engineering ONLINE Course

"Industrial Engineering is human effort engineering and system efficiency engineering."

This statement appeared in IIE magazine "Industrial Engineer" in March 2010 issue.


In the year 2015-16, Institute of Industrial Engineers changed its name to Institute of Industrial and Systems Engineers. It was explained by some of the proponents of the name change is required to focus the attention of industrial engineers on the big picture of the enterprise. Otherwise industrial engineers are more focused on the time study of single operator and improvement of single work stations. But in my thinking, system focus was always present. All the pioneers of industrial engineering Taylor, Gilbreth and Emerson mentioned systems and carried out enterprise level changes. All of them worked with machines, materials, men and other resources. A major classification of industrial engineering areas as system efficiency engineering and human effort engineering clearly brings out the system focus of industrial engineering and also its special focus among engineering branches on human resource in the engineering systems and processes.


The following subjects or techniques form part of industrial engineering tool kit and can be categorized under human effort engineering and system efficiency engineering. More detailed articles are written for each topic

Human Effort Engineering

1. Principles of Motion Economy and Motion Study.
Therbligs, SIMO chart, Chronocycle graph

2. Work Measurement
Stop watch time study, worksampling, PMTS - MTM, MOST

3. Ergonomics

4. Safe Work Practice Design
Personal protective devices

5. Wage Incentives and Job Evaluation

System Efficiency Engineering

1. Method Study and Methods Efficiency Design
Process analysis, operation analysis, work station design

2. Value Engineering

3. Statistics Based Techniques: Statistical Quality Control (SQC), Statistical Process Control (SPC), and Six Sigma Projects etc.

4. Mathematical Optimization, Operations Research and Quantitative Techniques
Linear programming models, Integer programming, Non-linear programming

5. Plant Layout Studies for reduction of material movement, operator movement and movement of salesmen etc.

6. Engineering Economics
Engineering Economic Appraisals of projects submitted by Engineering Departments

7. Specialised Functional IE Solutions: SMED. Lean Manufacturing, BPR


______________________________________________________________________




Related Papers and Articles


IE Tool Kit in Hospitable Food Service Sites.
IE magazine article, 2010
http://www.iienet.org/uploadedFiles/IIE/Applied_Ergonomics/martinez.pdf
Operation Research Methodologies in Industrial Engineering: A Survey
Authors: Robert E. Shannona; S. Scott Longb; Billy P. Bucklesa
IIE Transactions, Volume 12, Issue 4 December 1980 , pages 364 - 367

Industrial Engineering Job Description - Boeing
http://www.iienet2.org/uploadedFiles/IIE/About_IIE/IE%20Functions%20SS%20version.pdf


Source:
http://knol.google.com/k/narayana-rao/industrial-engineering-tool-kit/2utb2lsm2k7a/2352



June First Week - IE Knowledge Revision

http://nraoiekc.blogspot.com/2016/05/june-first-week-ie-knowledge-revision.html

Industrial Engineering Knowledge Revision Plan - One Year Plan


January - February - March - April - May - June



July - August - September - October - November - December




Updated 24 May 2020, 1 June 2016, 16 Dec 2011

April 24, 2020

Data Science - A New Subject - Introduction and Courses Information


Data science includes  topics such as plotting, statistics and probability, Monte Carlo simulations, data modeling, clustering, and plotting. Data scientists are in high demand due to development of Big Data technologies and the adoption of these technologies by companies in both manufacturing and marketing as well as in surveillance and risk management.

https://en.wikipedia.org/wiki/Data_science

https://datascience.berkeley.edu/about/what-is-data-science/

1 Hour Data Science Course
______________

https://www.youtube.com/watch?v=-ETQ97mXXF0
______________


19 April 2015

https://www.coursebuffet.com/tag/Data%20Science


Data Science with R: Intro to Data Analysis
Date: Sept. 28th; Oct. 5th, 12th, 19th, 26th; 2014
http://nycdatascience.com/courses/r-programming-intensive-beginner/

Data Sciences - Moocs
http://www.coursetalk.com/courses/data-science



Updated 25 April 2020,   19 April 2015
1 August 2014







April 23, 2020

Big Use of Big Data in Market Research

Browse  Online MBA Management Theory Handbook


In Market Research or Marketing Research, the producers and marketers have to figure out what market wants currently and in future.  This requires data and interpretation of data. Interpretation is 60% and data collection is 40% of marketing research according to a marketing research expert.  The emergence of social media is making data available in real time about brands and their shortcoming. It also has desires of customers for future products. So emergence of Big Data that has the capability to analyze social media conversations and posts has made data available to data interpreters and thereby reduced the need for data collectors in marketing research activity.


Whereas earlier marketing research was based on small samples, Big Data based marketing research is able to take in millions of potential customers and buid insights on their conversations. The advantage is that marketing communications can be targeted to them and a better response rate can be achieved for products and services created according to their stated needs.




Big Data Use 2015

Analyzing customer behavior  65%
Bringing together different data sources  63%
Improving customer perception 59%
Enhancing responsiveness to market dynamics  51%
Generating reports faster  41%
Enhancing customer relationshipss  37%
Developing new products and services  33%
Identifying cost reduction opportunities  14%.


Article in Corporate Dossier on 15 May 2015
Interview with Richard Ingleton, CEO, TNS, Subsidiary of WPP Group and one of the largest market research firms in the world.



Updated on 25 April 2020
14 May 2015

Management of Research and Development - Jain, Triandis and Weick - Book Information and Summary


Published by John Wiley and Sons 2010

Ravi K Jain
Harry C. Triandis
Cunthia Wagner Weick

Table of Contents

Preface xiii

1 R&D Organizations and Research Categories

1.1 How Information can be Used,
1.2 A Perspective on R&D Management
1.3 What is Research and Development?
1.4 Research Categories,
1.5 What to Research,
1.6 Emphasis on Basic Versus Applied Research,
1.7 What is Unique About Managing R&D Organizations?
1.8 Summary,
1.9 Questions for Class Discussion,

2 Elements Needed for an R&D Organization 20

2.1 People, 20
2.2 Specialization, 22
2.3 Staffing, 23
2.4 Ideas, 24
2.5 Defects in Human Information Processing, 28
2.6 Fads in Science, 30
2.7 Communication Networks, 31
2.8 The Innovation Process, 34
2.9 Funds, 34
2.10 A Culture for R&D Organizations, 36
2.11 Not-Invented-Here Syndrome, 38
2.12 Fit of Person and Job, 40
2.13 Creative Tensions: Managing Antithesis and Ambiguity, 41
2.14 Develop a Climate of Participation, 44
2.15 Summary, 45
2.16 Questions for Class Discussion, 46

3 Creating a Productive and Effective R&D Organization 47

3.1 Organization Effectiveness, 47
3.2 Who are the Inventors and Innovators?, 52
3.3 Odd Characteristics of Inventors and Innovators, 58
3.4 Researcher’s Relationship with Management and Peers, 59
3.5 Formation of Teams, 60
3.6 Generating New Ideas, 64
3.7 Emphases on Aspects of Organizational Culture, 68
3.8 Ethos of A Scientific Community, 69
3.9 Summary, 71
3.10 Questions for Class Discussion, 71

4 Job Design and Organizational Effectiveness 72

4.1 Job Attributes, 73
4.2 Physical Location and Communication, 74
4.3 Career Paths, 76
4.4 Dual and Triple Hierarchies, 78
4.5 Centralization and Decentralization, 80
4.6 Keeping the Researcher at the Innovation Stage, 81
4.7 Job Design and Conflict, 83
4.8 Summary, 86
4.9 Questions for Class Discussion, 87

5 Influencing People 88

5.1 Attitude, Attitude Change, 89
5.2 Findings from Attitude Research, 90
5.3 Behavioral Science Division Case, 92
5.4 Case Analysis, 94
5.5 Communication Alternatives and Outcomes, 95
5.6 Summary, 101
5.7 Questions for Class Discussion, 102

6 Motivation in R&D Organizations 103

6.1 A Model of Human Behavior, 104
6.2 Changing the Reward System to Support Technical
Careers, 112
6.3 Structuring the Organization for Optimal
Communication, 113
6.4 Rewards and Motivation, 114
6.5 Reward System Discussion, 116
6.6 Sense of Control and Community, 119
6.7 A Federal R&D Laboratory Case, 121
6.8 Summary, 122
6.9 Questions for Class Discussion, 122

7 Dealing with Diversity in R&D Organizations 123

7.1 Assimilation and Multiculturalism, 124
7.2 Understanding Culture, 126
7.3 Cultural Differences, 128
7.4 What Happens When People from Different Cultures Work Together?, 129
7.5 Cultural Distance, 130
7.6 Cultural Intelligence and Related Concepts, 130
7.7 A Model for Diversity in Groups, 132
7.8 The Status of Minorities in Work Groups, 135
7.9 Dealing with People from Different Disciplines,
Organizational Levels, and Functions, 136
7.10 Intercultural Training, 136
7.11 Summary, 139
7.12 Questions for Class Discussion, 139

8 Leadership in R&D Organizations 140

8.1 Identifying Your Leadership Style, 142
8.2 Theories of Leadership and Leadership Styles, 151
8.3 Leadership in R&D Organizations, 154
8.4 R&D Leadership: A Process of Mutual Influence, 157
8.5 A Leadership-Style Case, 158
8.6 Leadership in a Creative Research Environment, 160
8.7 Summary, 161
8.8 Questions for Class Discussion, 163

9 Managing Conflict in R&D Organizations 164

9.1 Conflict Within Individuals, 164
9.2 Conflict Between Individuals, 169
9.3 Conflict Between Groups, 171
9.4 Intercultural Conflict, 177
9.5 Personal Styles of Conflict Resolution, 179
9.6 Unique Issues of Conflict in R&D Organizations, 181
9.7 Ethics, 183
9.8 Summary, 183
9.9 Questions for Class Discussion, 184

10 Performance Appraisal—Employee Contribution—In R&D Organizations 185

10.1 Some Negative Connotations of Performance Appraisal, 185
10.2 Difficulties with Employee Appraisal, 187
10.3 Performance Appraisal and the Management System, 189
10.4 Performance Appraisal and Organizational Stages, 190
10.5 Performance Appraisal and Organization Productivity, 190
10.6 Goals of Engineers Versus Scientists, 191
10.7 Performance Appraisal and Monetary Rewards, 192
10.8 Performance Appraisal in Practice, 194
10.9 A University Department Case, 195
10.10 Implementation Strategy with Emphasis on Employee Contribution, 196
10.11 Summary, 203
10.12 Questions for Class Discussion, 203
10.13 Appendix: Argonne National Laboratory Performance
Review Information, 20

11 Technology Transfer 213

11.1 Technology Transfer Hypotheses, 214
11.2 Stages of Technology Transfer, 214
11.3 Approaches and Factors Affecting Technology Transfer, 216
11.4 Role of the User, 218
11.5 Characteristics of Innovation and its Diffusion, 220
11.6 Role of People, 222
11.7 Boundary Spanning, 223
11.8 Organizational Issues in Technology Transfer, 226
11.9 The Agricultural Extension Model, 227
11.10 NASA Technology Transfer Programs, 228
11.11 IBM Technology Transfer Cases, 229
11.12 Technology Transfer Strategy, 231
11.13 Summary, 236
11.14 Questions for Class Discussion, 237

12 Models for Implementing Incremental and Radical Innovation 238

12.1 Defining Innovation, 239
12.2 Strategic Choices in Technological Innovation, 242
12.3 Making Technological Innovation Operational, 244
12.4 The Market, Marketers, and Market Research in
Technological Innovation, 249
12.5 Leading Innovative Organizations, 253
12.6 Summary, 254
12.7 Questions for Class Discussion, 256

13 Organizational Change in R&D Settings 257

13.1 Why Organizational Change?, 258
13.2 Steps in Organizational Change, 259
13.3 Problems and Action Steps, 259
13.4 Individual Change, 262
13.5 Group Change: Team Building, 264
13.6 Organizational Change, 267
13.7 Evaluating Organizational Change, 268
13.8 Case Study in Organizational Change, 270
13.9 Summary, 273
13.10 Questions for Class Discussion, 273

14 Managing the Network of Technological Innovation 274

14.1 Overall Trends Within and Between Sectors, 274
14.2 Trends in Research, Development, And Innovation in the Commercial Realm, 276
14.3 Trends in Research, Development and Innovation in the Federal Government, 279
14.4 Trends in Research, Development, and Innovation in Universities, 286
14.5 Open Innovation, Regional Economic Development, and the Global Innovation Network, 290
14.6 Summary, 294
14.7 Questions For Class Discussion, 295

15 Universities and Basic Research 296

15.1 Basis for University Research Activities, 297
15.2 Federal Support of University Research: An Entitlement or a Means to Achieve National Goals?, 298
15.3 Basic Research: Who Needs It?, 301
15.4 University–Industry Linkage, 309
15.5 Rethinking Investment in Basic Research, 311
15.6 Summary and Concluding Comments, 312
15.7 Questions for Class Discussion, 313

16 R&D Organizations and Strategy 315

16.1 What is Strategy?, 316
16.2 Strategy Levels and Perspectives, 319
16.3 Strategy Formulation and Implementation, 319
16.4 Strategy Evaluation, 321
16.5 Strategy and Innovation, 322
16.6 Technology and Strategy, 324
16.7 Applying a Strategy Process, 325
16.8 Summary, 330
16.9 Questions for Class Discussion, 330

17 Research, Development, and Science Policy 331

17.1 Relationship Between Science and Technology, 334
17.2 Technical Innovation and Economic Development, 336
17.3 Analysis of Investment in Basic Research, 339
17.4 R&D Expenditure, 340
17.5 R&D Productivity, 347
17.6 Global Perspectives on Innovation, 352
17.7 R&D Expenditure and Science Policy, 357
17.8 Summary, 362
17.9 Questions for Class Discussion, 363


Ways to improve the productivity of R&D organizations and foster excellence in R&D organizations.

Of the approximately 595,000 doctoral scientists and engineers employed in the United States as of 2003, approximately 372,000 work in R&D. Of the 372,000, it is estimated that about 60,000 work in management of R&D. In the  remaining doctoral scientists and engineers (223,000), substantial number teach (184,000). Some are involved in professional services and consulting. Consulting engineers and scientists undertake creative activities that are, in many ways, responsible for closing the loop between research and development and application.


Studies have clearly shown that where supervisors were rated highest in technical skills the research groups were most innovative. And where supervisors did not possess excellent technical skills (but had high-level administrative skills), the research groups were least innovative (Farris, 1982, p. 340). These findings point to a fundamental need for a supervisor in an R&D organization who possesses
excellent technical skills. Consequently, scientist have to be provided inputs in managing R&D organizations and they are to be made managers.

The National Science Foundation (NSF) classifies and defines research as follows
:
Basic Research. Basic research has as its objective “a more complete knowledge or understanding of the subject under study, without specific  applications in mind.” To take into account industrial goals, NSF modifies this definition for the industry sector to indicate that basic research advances scientific knowledge “but does not have specific immediate commercial objectives, although it may be in fields of present or potential commercial interest.”

Applied Research. Applied research is directed toward gaining “knowledge or understanding to determine the means by which a specific, recognized need may be met.” In industry, applied research includes investigations directed “to discovering new scientific knowledge that has specific commercial objectives with respect to products, processes, or services.”

Development. Development is the “systematic use of the knowledge or understanding gained from research, directed toward the production of useful materials, devices, systems or methods, including design and development of prototypes and processes.”

A two-tier model for identifying “what to research
The model includes an economic index model and a portfolio model.

Economic Index Model
Research needs is to improve the operation or manufacturing efficiency of the organization or the enterprise. The emphasis is on building a “better products and processes” to reduce the cost of doing
things. Inputs for such needs come from looking at competitive products and operation

Portfolio Model
Normative needs are those of the user (a user being the primary or follow-on beneficiary of the research product). Comparative needs relate to research needs derived from reviewing comparable organizations, competitive product lines, and related enterprises. Forecasted research needs focus on trend analysis in terms of consumer or organization needs derived from new requirements, changed consumer behavior, new technological developments, new regulations (e.g., environmental, health, and safety regulations), and new operational requirements.


Updated on 25 April 2020
First posted on 11 March 2015



April 18, 2020

Digital Internal and Supply Chains Operations - Digital Product Offerings



Both are different. Require different skills at the present moment. May require two different digital information officers.

Digital products require much more new knowledge and understanding and therefore involves new science, engineering and management.

https://mitsloan.mit.edu/ideas-made-to-matter/how-to-master-two-different-digital-transformations
3 March 2020