Challenges in Demand Management in Recessionary Times
The recent macroeconomic changes and the speed at which they impacted end-consumer demand have significantly affected organizations. Some of the immediate effects on businesses include:
During such times of economic uncertainty, most organizations face challenges in forecasting demand. While companies do not want to lose sales opportunities, they also do not want to hold excess inventory, particularly when liquidity is low and credit is hard to obtain. Economists are predicting even more turbulent times in 2009; macroeconomic trends indicate growing prospects of a global slowdown.
Bullwhip effect is an important aspect of demand forecasting that could play a critical role in managing demand during the current depressed economic situation. Bullwhip effect in a supply chain refers to the phenomenon where a change in forecasted end-customer demand gets amplified as one moves up the supply chain.
Given the current economic environment what strategies should organizations adopt to manage demand and reduce the bullwhip effect on their supply chains?
Organizations have traditionally used one of the following methods for demand forecasting –Time Series (using historical data to extrapolate future sales) or Life Cycle (using sales curve of similar products for forecasting purposes)
Though these methods are popular they do not take into account the entire distribution channel. Let’s consider the example of an OEM component manufacturer. Even though the forecast for the component demand is dependent on the forecast of the reseller whose final product uses the component, there are others in the supply chain who also affect the forecast of the component demand. For example, in a typical supply chain, these include distributors and retailers. Each of these channel members will have their own forecasts for the same component demand. Each of these channel members creates their own forecast to reflect their view of adequate safety stock which in turn is a reflection of their view of the forecast errors. Moving up the value chain, this amplification of forecast demand has a multiplying effect when used by the OEM. To reduce this amplification of forecast error, it is imperative for manufacturers to have visibility into the demand at each stage of the supply chain.
This is where multi-tiered forecasting or collaborative forecasting techniques can be extremely useful. Both of these forecasting techniques refer to the concept of multi-tier inventory visibility and multi-tier demand visibility so that each channel partner can forecast demand optimally. Techniques such as Vendor Managed Inventory (VMI), use of Point-of-Sales (POS) or consumption data by upstream channel partners, sharing of promotion information etc. are some of examples of collaborative or multi-tiered forecasting approaches that can be used by entities within the supply chain to reduce the bullwhip effect.
I am curious to hear from readers of this blog their experiences/inputs on collaborative forecasting. Some of the key areas I am hoping to hear about include:
a. What are the challenges in adopting collaborative forecasting?
b. Do suppliers trust their customers well enough to share their internal forecasting data?
c. Does an ERP system such as Oracle help in collaborative forecasting?
I will try discussing my thoughts on these questions in my upcoming
