Monday, November 25, 2013

Optimizing Shubh Laabh: Harmonious Profitability

Sustainable machine-generated, data-driven decisions
The growing popularity of 'Big Data' coupled with 'machine learning' techniques coincides with an increasing use of automated, machine-computed solutions for a a variety of business problems that were once solved and optimized based (predominantly) on human inputs. Machine-generated solutions have been shown to be superior to these previous methods on the measured performance metrics in many instances, and companies all over the globe have deployed advanced analytics and business optimization models (e.g. built using Operations Research) to achieve incremental profitability, cost reductions, or improved system efficiency. However, all is not well. Some solutions are sustainable, and work well over time, while others begin to run into a seemingly endless stream of human or environmental issues, and fall by the wayside. 

What differentiates sustainable machine-generated optimizations from the unsustainable ones? The answer is not straightforward, and this post explores one aspect. For an example of what kinds of issues can crop up, see this BBC news article: "Amazon workers face increased risk of mental illness", as well as this older article on 'unhappy truckers'A portion of the BBC article highlighted below is color-coded to show where sustainable decision optimization could be potentially applied to improve upon the status-quo):

 "..... Amazon said the safety of its workers was its "number one priority."

Undercover reporter Adam Littler, 23, got an agency job at Amazon's Swansea warehouse. He took a hidden camera inside for BBC Panorama to record what happened on his shifts.
He was employed as a "picker", collecting orders from 800,000 sq ft of storage.
A handset told him what to collect and put on his trolley. It allotted him a set number of seconds to find each product and counted down. If he made a mistake the scanner beeped.
"We are machines, we are robots, we plug our scanner in, we're holding it, but we might as well be plugging it into ourselves", he said.
"We don't think for ourselves, maybe they don't trust us to think for ourselves as human beings, I don't know.
..... Prof Marmot, one of Britain's leading experts on stress at work, said the working conditions at the warehouse are "all the bad stuff at once".
He said: "The characteristics of this type of job, the evidence shows increased risk of mental illness and physical illness."
"There are always going to be menial jobs, but we can make them better or worse. And it seems to me the demands of efficiency at the cost of individual's health and wellbeing - it's got to be balanced."
I spent the early-mid 2000s redesigning, and improving airline crew scheduling optimization systems. This period also happened to be the industry's most tumultuous: 9-11, out-of-control fuel and labor costs exacerbated by the invasion of Iraq, repeated strikes by various worker unions followed by contentious negotiations that lead to multiple CBAs (collective bargaining agreements) being ripped up and rewritten, and companies lining up to file for Chapter-11 bankruptcy protection, etc. Endless problems. The office atmosphere got quite intense when the R&D team somehow managed to find itself in the middle of these events, and facing heat from all sides (management, unions, soaring passenger complaints) on the kinds of solutions that were generated by our decision support systems (The US airline industry pioneered the use of such techniques). The analytical lessons empirically learnt from such episodes are hard to replicate in classrooms. One such lesson was "pay a lot of attention to the impact of your model on the people and environment". The application of this lesson has been explored in this space in a variety of contexts earlier: here (Gandhi's methods), here (Smart-Grid), here (Airline Crew scheduling), and here (Conflict resolution). The issue is revisited here by borrowing an idea from traditional Indian business philosophy to see if new insight can be generated toward answering our question on sustainable business optimization.

(pic link source:

(Updated: November 30, 2013 Finally found the link to article that inspired this post)
It is interesting to note that for centuries, traditional business communities in India had adopted the policy of Shubh Laabh (written in Hindi in the picture), which roughly translates into 'auspicious/harmonious profit' (Aravindan Neelakandan, co-author of 'Breaking India' in the linked article notes: "Lakshmi symbolizes the wealth that is holistic: it is wealth that puts welfare (Shub) before profit (Laabh)." The pursuit of wealth and profitability was never frowned upon in Hindu society, while unconstrained profit maximization was recognized as a socially destabilizing and ecologically unsustainable objective.  'Shubh Laabh' recognizes and respects the presence of long-lasting and latent side-effects that arise from business decisions (that can bring you 'bad luck') and attempts to balance them equitably with the more immediate goal of profitability (Laabh). These traditional businesses employed some operational form of Ahimsa (the principle of minimal harm) to optimize Shubh Laabh:
Rule a) limit harm (hard-constraint version)
Rule b) minimize harm (soft-constraint version)
Let us see how this idea can be incorporated within modern decision optimization systems. Amazon appears to have satisfied all legal requirements via (a) by making safety a top priority. It has probably ensured that the statistical rate of accidents is below some stringent threshold. In the airline world, (a) is achieved by ensuring total compliance with respect to all FAA- and CBA-mandated safety rules. However, this represents a necessary condition that tolerates a certain level of error as 'legally acceptable collateral damage'. The resultant formulation is: maximize profitability subject to safety regulations. However, this in itself is an insufficient specification if we want our algorithms to minimize harmful side-effects. An Ahimsa-based model would additionally consider (b) and eschew profit achieved at the cost of a reduced employee quality-of-work-life (QWL) or environmental degradation, as unsustainable and counterproductive in the long run. 

For large-scale systems such as a retail supply-chain or airline crew schedules, a reasonably skilled analytics professional should be able to incorporate requirements (a)-(b) within their decision support algorithm which, among alternative near-optimal solutions (and there are often many of these), selects one that also maximizes worker QWL, and/or minimizes harm (e.g. reduces carbon footprint). This requires the human-and-environment-variables in the system be treated positively as an active and equal partner based on mutual respect, by explicitly including their requirements as part of the primary goal (objective function), going beyond a legalistic/adversarial approach of treating these variables as a 'loss-making noise that has to be managed' by specifying a minimum tolerance constraint.

To summarize
It is possible to achieve sustainable profitable solutions via automated decision support systems that are also harmonious and sustainable, by paying due respect to all the stakeholders (including Ms. Nature), right from the design phase.

An old blog discussed Rajiv Malhotra's use of 'mutual respect' (as opposed to mere tolerance) as a simple but powerful basis for two heterogeneous groups of people, or people subscribing to conflicting thought systems, to achieve a fair and sustainable equilibrium in their interactions. It appears that such a mutual respect:

a) is implicitly present in the idea of Shubh Laabh, which in turn

b) can be employed as a key guiding principle of 'sustainable design' when building decision support algorithms for managing complex business problems, where multiple, and potentially conflicting, goals have to be delicately balanced.

The opinions expressed in this article are personal.


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