Tuesday, August 2, 2016

Principles of Supply Chain Management



Supply Chain Management objectives:

Add value by
ß reducing working capital,
ß taking assets off the balance sheet,
ß accelerating cash-to-cash cycles,
ß increasing inventory turns,
decreasing cost of production in various firms in supply chain
improving quality in various firms in supply chain
introducing new management techniques in various firms in supply chain



The seven principles as articulated by Andersen Consulting are as follows:

1. Segment customers based on service needs.
2. Customise the Supply Chain Management network.
3. Listen to signals of market demand and plan accordingly.
4. Differentiate product closer to the customer.
5. Strategically manage the sources of supply.
6. Develop a supply-chain-wide technology strategy.
7. Adopt channel-spanning performance measures.

The original statements of Andersen Consulting Persons

Principle 1: Segment customers based on the service
needs of distinct groups and adapt the supply
chain to serve these segments profi tably.

Principle 2: Customize the logistics network to the
service requirements and profi tability of customer
segments.

Principle 3: Listen to market signals and align
demand planning accordingly across the supply
chain, ensuring consistent forecasts and optimal
resource allocation.

Principle 4: Differentiate product closer to the
customer and speed conversion across the supply
chain.

Principle 5: Manage sources of supply strategically
to reduce the total cost of owning materials and
services.

Principle 6: Develop a supply chain-wide technology
strategy that supports multiple levels of decision
making and gives a clear view of the fl ow of
products, services, and information.

Principle 7: Adopt channel-spanning performance
measures to gauge collective success in reaching
the end-user effectively and effi ciently.

http://www.scmr.com/images/01.SevenPrinciples_.pdf


The Seven Principles of Effective Supply Chain Planning

Systematic management of “master data,” including key data fields for items, customers, manufacturing resources, and suppliers
Synchronized long-term, tactical, and execution planning processes, planning horizons, and intervals for data refresh
Mature collaborative processes for both key customers and suppliers reconciling forecast, orders, and usage or sell through
Data-oriented understanding of the inputs to the forecast including forecast error, cumulative bias, lift, new products, and year-end volume variation
Intense focus on “point-of-sale” or “sell-through” data (as opposed to sales orders and “sell in”)
Disciplined product lifecycle management process bridging the gap between product development and supply chain
A continuous improvement approach to understanding consumer or user behavior.
http://www.apics.org/sites/apics-blog/think-supply-chain-landing-page/thinking-supply-chain/2015/05/20/the-seven-principles-of-effective-supply-chain-planning



Understanding the Concept of Elasticity in Supply Chain Relationships:  An Agency Theory Perspective Maryam Zomorrodi  and  Sajad Fayezi 

ASIAN JOURNAL OF MANAGEMENT RESEARCH
2010
P.1: Relational governance mechanisms based  on  trust, commitment  and information sharing  significantly influence the relationship elasticity of cooperating parties.
P.2: Contractual  governance mechanisms based  on  risk/reward  sharing  and  relationship­ specific investment significantly influence the relationship elasticity of cooperating parties.
http://www.ipublishing.co.in/ajmrvol1no1/EIJMRS1035.pdf

Innovation Generation in Supply Chain Relationships: A Conceptual Model and Research Propositions

Journal of the Academy of Marketing Science.
Volume 32, No. 1, 2004,  pages 61-79.

Proposition 1a:The greater the extent of buyer-seller interaction, the greater the generation of incremental innovations in supply chain relationships
Proposition 1b:The greater the extent of buyer-seller interaction, the greater the generation of radical innovations in supply chain relationships.

Proposition 2a:The greater the IT adoption and integration between the buyer and seller, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships.
Proposition 2b:The greater the IT adoption and integration between the buyer and seller, the lesser the impact of interaction on the generation of radical innovations in supply chain relationships.

Proposition 3a:The greater the asymmetry in input commitment between the buyer and the seller, the lesser the impact of interaction on the generation of incremental innovations in supply chain relationships.

Proposition 3b:The greater the asymmetry in input commitment between the buyer and the seller, the
greater the impact of interaction on the generation of radical innovations in supply chain relationships.

Proposition 4a: The lesser the asymmetry in attitudinal commitment between the buyer and the seller, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships.
Proposition 4b: The lesser the asymmetry in attitudinal commitment between the buyer and the seller, the greater the impact of interaction on the generation of radical innovations in supply chain relationships.

Proposition 5a: The greater the competence trust between the buyer and the seller, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships.
Proposition 5b: The greater the competence trust between the buyer and seller, the greater the impact of interaction on the generation of radical innovations in supply chain relationships.

Proposition 6a: The greater the goodwill trust between the buyer and seller, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships
Proposition 6b: The greater the goodwill trust between the buyer and the seller, the greater the impact of interaction on the generation of radical innovations in supply chain relationships.

Proposition 7a: The greater the tacitness of technology associated with an innovation, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships
Proposition 7b: The greater the tacitness of technology associated with an innovation, the greater the impact of interaction on the generation of radical innovations in supply chain relationships

Proposition 8a:The greater the stability of the final consumer demand, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships.

Proposition 8b:The lesser the stability of final consumer demand, the greater the impact of interaction on the generation of radical innovations in supply chain relationships.

Proposition 9a: The greater the network connections of the buyer and seller within an industry group, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships.
Proposition 9b: The greater the network connections of the buyer and the seller across industry groups, the greater the impact of interaction on the generation of radical innovations in supply chain relationships.

Sunday, July 10, 2016

Statistical Optimization in High Dimensions - A Research Paper



Huan Xu, Constantine Caramanis, and Shie Mannor

Received: January 2014
Accepted: March 2016
Published Online: July 5, 2016

The paper deals with optimization problems whose parameters are known only approximately, based on noisy samples. In large-scale applications, the number of samples one can collect is typically of the same order of (or even less than) the dimensionality of the problem.

Three algorithms are proposed to address this setting, combining ideas from statistics, machine learning, and robust optimization.

The key ingredients of to the algorithms are dimensionality reduction techniques from machine learning, robust optimization, and concentration of measure tools from statistics.

http://pubsonline.informs.org/doi/abs/10.1287/opre.2016.1504

Sunday, June 19, 2016

Why Some Bosses Bully?



https://hbr.org/2016/06/why-some-bosses-bully-their-best-employees


Social Dominance Orientation (SDO)

Social Dominance Theory postulates that some people have more of a tendency toward “social dominance orientation” (SDO) than others.


Predictors of abusive supervision : Supervisor perceptions of deep-level dissimilarity, relationship conflict, and subordinate performance. / Tepper, Bennett J.; Moss, Sherry E.; Duffy, Michelle K.
In: Academy of Management Journal, Vol. 54, No. 2, 01.04.2011, p. 279-294.
https://experts.umn.edu/en/publications/predictors-of-abusive-supervision(7ebef711-d96b-4b97-b323-e9dd8a0a2d06).html

Saturday, April 30, 2016

Rough Set Theory and Applications

Revise Basics of Set Theory

Ch. 1. Sets - Concept Review

Ch.2. Cartesian Product of Sets and Relations - Part 2



Rough Sets Using R
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lalasriza upload

Published on 25 Jun 2014
Presented by Lala Septem Riza for the Orange County R User Group. Organized and recorded by Ray DiGiacomo, Jr. (President, OC-RUG, rayd@liondatasystems.com). RoughSets is an R package that implements algorithms based on Rough Set Theory and Fuzzy Rough Set Theory. It contains some features: missing value completion, discretization, feature selection, instance selection, rule induction, and fuzzy rough nearest neighbor classifiers.

For further information on the package, visit the following URLs:
https://cran.r-project.org/web/packages/RoughSets/index.html