‘Nudge’ Your Business to Grow Phenomenally

Richard Thaler, father of “Nudge Theory” won the 2017 Nobel Prize in Economics. His achievement was to articulate and formalize, what successful leaders have always known, albeit often intuitively – That as a leader, one can influence and nudge one’s team to do better than “the expected/average outcome”.

The question is not whether its possible to nudge your team to increase revenue and reduce wastage (Obviously it is, and you are aware of it). Instead, what I want to explore is how do you do it and more importantly can it be done in a formal, measurable, and sustainable way?

Take target setting in sales as an example. While this should be a data driven exercise, there are organizations which take the conventional route of accepting organizational “aspiration” as the target for the next fiscal/quarter while ignoring ground realities. Often these companies end up burning cash in search of an elusive target & unsurprisingly create discontentment amongst work force. In addition, there might be product categories and geographies which remain untapped and sub optimally monetized.

Slightly more mature organizations rely on statistical forecasts, enforced top-down, which trickle down after multiple modifications at various levels. Although better, it is still problematic because by the time the targets reach the bottom of the pyramid they have been changed and influenced by multiple biases and subjective buffers.

To chart a marine analogy, such processes and decision making in the organization is like the movement of a convoy of ships, following the ship in front, with the lead ship tracing the north star using a 19th century sextant while the competing fleet is using GPS and Radars. Oh, and if the competitor decides to bring a submarine to do some reconnaissance (read ‘Data backed real time insights’), its just too bad.

Let me paint an image of how mature organizations work instead.

Mature organizations are data first enterprises, which have separate “Top Down” and “Bottom Up” forecast. While the “Top Down” forecast is glued in to regulatory changes and draws inspiration from macro phenomenon, the “Bottom Up” forecast relies on localized event and draws inspiration from ground level business inputs. Imagine the efficacy of the inputs available to stakeholders during consensus planning and S & Op meetings to come up with a harmonized strategy. Further, near real time weekly forecasts are made available to the marketing team to fine tune approach and step in as necessary.

SCOPT Analytics’ cloud-based Demand Forecasting platform (DEM-SENSE) enables organizations to scale the maturity ladder by doing all the above and more. Not only do we generate separate Top Down, Bottom Up and Middle Out forecasts, all these are available for the next Quarter, Month and down to the week level to facilitate need based intervention and strategy tweaking in real time based on tangible evidence. The platform provides a rich set of dashboards and a friendly UI as additional aids.

Further, the platform places significant emphasis on business inputs and local ground realities which it can capture & integrate into the forecasting engine.

If the competing fleet is armed with GPS and Radars its only fitting that you show up to the party armed with Machine Learning and Artificial Intelligence backed technology.

Game Changer for Organizations: Dealing with ambiguity in Supply Chains

Amidst the glitz and glamour of self-driving cars and humanoid robotics, if there is one application of advanced analytics that has unfortunately not got its due recognition, it is supply chain optimization.

The objective of this blog is to capture the recent shift in the motivation to harness data for supply chain improvements and to explore decision making under uncertainty.

There is wide variance in the maturity of organizations, their recognition and acknowledgement of the role of advanced analytics in supply chain, institutionalizing streams of data flow, commissioning statistical / mathematical models and finally, actual deployment of business-ready automated decision support systems.

While the case for efficiency gains and undercutting competition by relying on predictive & prescriptive analytics is well documented, surprisingly, the latest push for supply chain innovation is less because of perceived or actual threats by competitors Instead, it is the end consumers of products and services, who are pushing organizations to be more dynamic and innovative with their supply chain.

The guiding principles for supply chains have always been getting the right SKU to the right location at the right time in the most cost-effective way. However, increasingly decision makers at various nodes are grappling with defining “what is right”?

The end consumer’s expectation of a “Wow” and highly customized experience along with zero tolerance for quality-issues have manifested in consumer demand patterns that are significantly more volatile & complex, for legacy software and tools to predict. Further, a diktat from senior management on “what is right” is counter productive as it does not allow distribution centers and hubs to proactively react to ground realities without first justifying and getting the buy in from senior management.

As an anecdotal example, there is an organization in the manufacturing sector which still has same inventory targets for all distribution centers across the country irrespective of the patterns and volatility of consumer demand. Thus, even though from an org perspective, they are doing the “right thing” and meeting all targets it is leaving customers highly dissatisfied, draining millions in lost sales and piling up extra inventory at various levels.

Many organizations, including the one referred above are now experimenting with splintered supply chains with a more modular structure where the focus is not only on doing everything right in the first instance but also on having a game plan for reactive management which taps into shorter horizon, localized events and gives more flexibility to distribution centers and hubs to take independent decisions within the ambits of a pre-approved strategy. This pre-approved strategy and flexibility bands at various levels can be made possible by relying on advanced Bayesian Statistics amongst other predictive analysis models and an exhaustive scenario analysis.

Another challenge to transforming legacy monolithic supply chains to more dynamic & resilient components of business strategy would be ushering in the “change management” component that comes with the adoption of any new business process or workflow. Well intended projects have failed because of lack of insight consumption and more importantly consensus planning between various functions.

Supply chain is the silent articulation of corporate strategy and is increasingly visible in the way end consumers interact with the brand. In an era where consumer behavior is defined by a penchant for customization and lack of brand loyalty, where SKU management itself has become a specialized science, the organizations that will survive in the long run are the ones that leverage data and advanced analytics in supply chain not only with an eye on the bottom line but also as a lever for influencing the top line and direct revenues.