Procyon Mukherjee, January 2020
Demand signals remained always that mysterious piece till the technologies got better off the ‘hunches’ and ‘gut’ effects. When the chips are down as in a downturn, sales is over-estimated (largely due to top line pressures on the overall system), while when the tide lifts all boats, sales is under-estimated and lost forever. From distribution and warehousing to manufacturing the line extends to the suppliers and finally to the supplier’s supplier. Some parts of the chain do better to sense the demand but the worse part remains the supplier’s part of the chain; the communication of “sensed” demand is never smooth and it has to ride on the barriers that functional silos pose.

If you take the easiest of examples, think of the large change in inventory and supply due to much smaller changes in demand, but that is a Bull Whip effect, we all know, that is created by sequential forecasting and planning with several of the supply chain participants disconnected to the actual data on the ground or to their inter-connectivity. It is about variability of supply responding to variability of demand signals as captured by firm participants and this flows through the chain creating havoc at every part of the chain. But why is the customer end much better than the supplier end, well that is just because the signals do not pass through the chain seamlessly and the customer just does not accept anything that it would not want. There is of course the problem of visibility in long chains, especially where several parts must get in ship-sets to make the final piece but then you also need a technological solution to it. In today’s world we have fortunately both the visibility and a technological solution to the problem, we need to re-inforce a few other things.

I recently ran a Bull Whip game with a group of senior people from a leading automotive company; I allowed a bit of negotiation and collaboration to happen in the game. It turned out that the teams that collaborated to trade information and share parts of the burden when there was a disruption, did far better than the others. Those that simply followed the rules and were too tunnel-visioned to venture out to experiment with new conversation with other participants in the chain did much worse. Moderating influence theory in Supply Chains tell us that not every solution works in the same way in different organizations or parts of the chain, but surely changing the way influences the results for an individual supply chain.

This is an important question, the rules in the supply chain could be anything but when you have a disruption, you need to quickly allow new conversations and it could well be that some of the ideas could be out of the ordinary. For example when a plant stopped and did not need any materials, the supplier cooperated and when the supplier had a strike the plant cooperated by finding a temporary solution by on-boarding a new supplier but only temporarily. Creating an atmosphere of sharing each other’s troubles is a simple step but it does wonders. But what about some use of technologies that were not there before?

So those who want to continue on the path of playing this by the gut are happy in their own way, but data shows very differently what this means in terms of actual cash flows and profitability. Research has shown that a company bullwhips if it purchases from suppliers more variably than it sells to customers. We investigate the bullwhip effect in a sample of 4,689 public U.S. companies over 1974- 2008. Overall, it is found about two thirds of firms bullwhip. Decomposing the bullwhip by information transmission lead time, it is found that demand signals firms observe with more than three quarters’ notice drive 30% of the bullwhip, and those firms observe with less than one quarter’s notice drive 51%.

Research has shown that there are six dimensions at play in the demand sensing area: (1) whether firms consider their supplier’s inventory costs, (2) the convexity of production cost functions, (3) the replenishment rate of depleted inventories, (4) the drivers of the bullwhip effect, (5) the degree to which supply chain alignment mitigates the bullwhip effect, and (6) whether demand forecast accuracy constitutes a supply chain externality.

I was attracted to the supply chain externality issue which is also called Spillover Effects in Supply Chains. During Covid-19 disruptions there were two dominant themes, one was the signaling effects seen in firms when every firm responded to demand signals in its own way and sometimes the industry clusters responded in concert to the same signals and at times some were at variance to the theme. The spillover effects were also glaringly visible.

Think of the positive impact of supply sources shifting to Taiwan, Vietnam, Indonesia and finally to India when the Chinese supply chains had major disruptions. The localization of some of the components became a dominant theme and this has came from spillover effects alone. Let me now move over to the peer effect in supply chains. This was seen much in abundance in the area of shipping lines who used every information in the physical system through networks of peers.

Think of it, most supply chains are extremely centralized, such that it loses enormous amount of local information as they are thought to be redundant or are cocooned by very small firms who are part of the physical network but not of the informational one. Current supply chain information systems are transaction-based and suffer from lack of real-time transparency as well. Think of the shipping industry and much of the information is lost in the peer network of stevedores, handling agents or simply between two shipping lines who have a range of network participants who handle paper work.

A solution to this problem has been developed in hybrid peer-to-peer supply chain physical distribution framework (HP3D) that addresses these increasingly critical gaps in a global market. HP3D leverages the advantages of hybrid networks through flexible peers and a lightweight index server in order to share supply chain physical distribution information in pseudo real-time among stakeholders. The architecture of HP3D consists of a hierarchy of dynamic sub-networks that evolve based on market demands and digitize the transfer of goods between suppliers and customers. These sub-networks are created on demand, emulate the end-to-end movement of the shipment and terminate when the delivery of goods is completed. A variation of block-chain technology is also used in order to increase the security level of the proposed framework. This is being now made a major driver of change as shared networks can now talk to each other, including banks, port personnel, shipping lines and all agents who handle cargo between consignor and consignee.

The future will be the following, every participant in the physical network will be part of the web of real time informational network as well, through use of technology and that will completely eliminate the pangs of signaling, bull-whip, et al.