Phanish Puranam, Professor of Strategy

MBA 18D
Period 3 | May-June 2018|
Singapore
Seats: 30
Credit: 1

Course Description

In this elective, we will bring you to the cutting edge of how organizations are (re)-designed. The suite of new ideas that characterize “Org2.0” represent a major departure from the “copy best practice” approaches to designing the “boxes and arrows” of organization charts, or the obsession with incentive compensation and reporting as the key organizational/HR decisions. The new approaches are made possible by a combination of theoretical developments and access to vast computational power and data arising from digitalization.

The tools of Org 2.0 are diverse and cover Perception (i.e. what is happening in the organization now), Prediction (i.e. what is likely to happen in the future, based on sophisticated extrapolation of past data) and Prototyping (i.e. what is likely to work based on pilot tests). We will rely extensively on group exercises. An experienced practitioner, Dr. Andreas Raharso will lead a team of guest speakers to help bridge techniques and their application in practice.

In 7 double sessions, the specific skills you will learn about include:

  • How to think about complex organizations by breaking them down into a few basic building blocks or “micro-structures”
  • Using graph theory to map networks of interaction within organizations
  • The use of machine learning to answer the fundamental “people” questions in organizations: whom to hire, develop and retain?
  • Prototyping organizational changes in silico using agent based computational models
  • Using A/B testing (a.k.a. Randomized Controlled Trials) to know rather than guess which organizational designs will be effective

Who should take this course?
If you are likely to be involved in strategy execution, post-merger integration, re-organizations, or HR in either a direct or advisory role, this course will put you at the cutting edge of thinking and methodology in these areas. Please note we do NOT allow auditing of this class.

Pre-requisites

You DO NOT need to know statistics (beyond that covered in core courses) or computer programming to take and benefit from this course. We will provide technical support and will design exercises in a way that you can focus your energies on mastering the concepts and thinking of solving business problems. To learn more about how the course design accomplishes this, please see: https://knowledge.insead.edu/blog/insead-blog/organisationdesign-based-on-science-not-superstition-6531 

You DO need to be intellectually curious, and have the aptitude to think analytically about organizations and “people issues”. Please note that class size is restricted to allow the pedagogical design to work effectively. You can attend the first double session even if you are on the waitlist.

Course Materials

All necessary items are available in the course pack or on the course website. Also note that slides will be posted after each class on the course website. My experience is that this works better for my teaching style.

Outline of Class Sessions

Session 1 &2- Soft stuff, hard problems!
SGP:
FBL:
Pre-class readings:
(BEFORE you begin reading the case, please make a list of 5-10 attributes you think an effective manager must have)

  1. Case: Project Oxygen at Google (HBS case)

Pre-class Interest and Expertise Poll:
Submit your response to the pre-class “Interests and Expertise” survey by 11:59 pm the day before class. This is not a graded exercise, but it is compulsory as it will help me understand your requirements better.
In–class group exercise: Introduction to HR analytics
We learn about how to analyse data in order to understand which employees are at risk of departing, and what we might want to do about it. This is a basic application in organizational
analytics.
Follow up reading:

  1. People before strategy: a new role for the CHRO (HBR)

Session 3&4- Letting the Data speak: Prediction
SGP:
FBL:
Pre-class readings:

  1. What Google learned from its quest to build the perfect team (C. Duhigg, NY Times)
  2. Machine Learning for Humans- read Parts 1 and 2.1, skim 2.2-2.3

Online poll question:
In your view, what was the most surprising thing that Google discovered about effective teams?
Why? (maximum 250 words)
Submit your response to the poll by 11:59pm the day before class.

In–class group exercise: Predictive analytics in HR

We will introduce ideas about machine learning and its uses in Org2.0. These techniques enable us to go from “perception”- understanding what is going on using data description and
hypothesis testing- to “prediction”- using past data to forecast future behaviour. We will begin in class, and complete and submit as a group a short write up of the analysis (3 page memo) later.
Follow up reading:

  1. They’re watching you at work (The Atlantic Debate)

Session 5&6- Just cause: A/B testing
SGP:
FBL:
Pre-class readings:

  1. Online Case: Changes at Mayo Clinic: http://vol10.cases.som.yale.edu/designmayo/introduction/mayo-clinic

For the Mayo clinic case, please read and view materials under the various links, but with a particular focus on two projects

  1. Exam Room Redesign and
  2. Dermatology Practice Redesign.

This is an innovative “raw case” format that requires you to make your assumptions where data are not available, and do your own synthesis.

Online poll question:
Based on the materials and the two projects you read about, would you evaluate CFI’s approach to improving health care delivery as effective or ineffective? (maximum 250 words)”
Submit your response to the poll by 11:59pm the day before class.

Follow up reading:

  1. A refresher on randomized control trials (HBR)
  2. Machine learning for humans: Skim part 3 & 4

Session 7&8- Proto-typing: Clinical trials for organization designs
SGP:
FBL:
Pre-class readings:

  1. Experiment with organizational change before going all in (HBR)
  2. Cisco Reorg (A) and Analyst reactions

Online poll question:
Do you think Cisco’s proposed reorganization is sensible? Why or why not?

Submit your response to the poll by 11:59pm the day before class.

In-class group exercise: Will this work?
How to design and analyze experiments to see if a new management process/practice is better than the one currently used in a company. We will begin in class, and complete and submit (as a
group) a short write up of the analysis (3 page memo) later

Follow up reading:
The discipline of business experimentation (HBR)

Session 9&10- Breaking down complexity: Thinking in micro-structures
SGP:
FBL:
Pre-class readings:

  1. Valve’s Way
  2. Valve’s New Employee Handbook

Pre-class Online poll question:
Do you think “Valve’s way” can work more broadly? Why or why not? (maximum 250 words)

Submit your response to the poll by 11:59pm the day before class.

In–class group exercise: How does it work? Your groups will select one “new” model of organizing out of two that you will be given brief descriptions of, and will explain how it works,
and under what conditions it can be effective. We will begin in class, and complete and submit (as a group) a short write up of the analysis (3 page memo) later

Follow up readings:
These are a series of very short blog posts.
https://knowledge.insead.edu/blog/insead-blog/the-shape-of-hierarchy-and-why-it-matters-7351
https://knowledge.insead.edu/blog/insead-blog/how-to-make-corporate-hierarchy-more-likable-7761

Session 11 & 12- Matrix redux: graph theory and organization design
SGP:
FBL:
Pre-class readings:

  1. Case: Building a networked organization: Restructuring the IT department at MWH (A)
  2. The value of social network analysis (HBR)

Pre-class Online poll question:
What are the insights from the network analysis of iNet organization for Gulas that you think could not have been obtained in any other way? Pick your top two and explain
why (maximum 250 words)

Submit your response to the poll by 11:59pm the day before class.

In–class group exercise: Find the “hot-spots”
How to analyse data on different kind of networks on the same set of individuals (i.e. multiplex data) to be able to identify potential “hot-spots” or problem areas in the organization structure.
Where are coordination issues likely to arise? Which managers will become decision bottlenecks?

Follow up reading:

  1. Informal networks: the company behind the chart (HBR)

Session 13 & 14- Organizations in Silico: Agent Based Models
SGP:
FBL:
Pre-class readings:

  1. The new science of organization design (P. Puranam & J. Clement)
  2. Predicting the unpredictable (HBR)

In–class group exercise: Will it spread?
You are probably familiar with the idea that whether a change initiative will be adopted cannot be mandated but must be voluntarily adopted. Further, people are more likely to adopt what
others they know and trust are adopting. But can you forecast the probability of adoption? What intervention could you undertake to improve the chances of success? Agent based models can
help us understand the dynamics of this complex process.

Follow up reading:

  1. The network secrets of great change agents (HBR)