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.