An unmanaged supply chain is not inherently stable. Demand variability increases as one moves up the supply chain away from the retail customer, and small changes in consumer demand can result in large variations in orders placed upstream. Eventually, the network can oscillate in very large swings as each organization in the supply chain seeks to solve the problem from its own perspective. This phenomenon is known as the bullwhip effect and has been observed across most industries, resulting in increased cost and poorer service.
Causes of the Bullwhip Effect
Sources of variability can be demand variability, quality problems, strikes, plant fires, etc. Variability coupled with time delays in the transmission of information up the supply chain and time delays in manufacturing and shipping goods down the supply chain create the bullwhip effect. The following all can contribute to the bullwhip effect:
Overreaction to backlogs
Neglecting to order in an attempt to reduce inventory
No communication up and down the supply chain
No coordination up and down the supply chain
Delay times for information and material flow
Order batching - larger orders result in more variance. Order batching occurs in an effort to reduce ordering costs, to take advantage of transportation economics such as full truck load economies, and to benefit from sales incentives. Promotions often result in forward buying to benefit more from the lower prices.
Shortage gaming: customers order more than they need during a period of short supply, hoping that the partial shipments they receive will be sufficient.
Demand forecast inaccuracies: everybody in the chain adds a certain percentage to the demand estimates. The result is no visibility of true customer demand.
Free return policies
Countermeasures to the Bullwhip Effect
While the bullwhip effect is a common problem, many leading companies have been able to apply countermeasures to overcome it. Here are some of these solutions:
Countermeasures to order batching - High order cost is countered with Electronic Data Interchange (EDI) and computer aided ordering (CAO). Full truck load economics are countered with third-party logistics and assorted truckloads. Random or correlated ordering is countered with regular delivery appointments. More frequent ordering results in smaller orders and smaller variance. However, when an entity orders more often, it will not see a reduction in its own demand variance - the reduction is seen by the upstream entities. Also, when an entity orders more frequently, its required safety stock may increase or decrease; see the standard loss function in the Inventory Management section.
Countermeasures to shortage gaming - Proportional rationing schemes are countered by allocating units based on past sales. Ignorance of supply chain conditions can be addressed by sharing capacity and supply information. Unrestricted ordering capability can be addressed by reducing the order size flexibility and implementing capacity reservations. For example, one can reserve a fixed quantity for a given year and specify the quantity of each order shortly before it is needed, as long as the sum of the order quantities equals to the reserved quantity.
Countermeasures to fluctuating prices - High-low pricing can be replaced with every day low prices (EDLP). Special purchase contracts can be implemented in order to specify ordering at regular intervals to better synchronize delivery and purchase.
Countermeasures to demand forecast inaccuracies - Lack of demand visibility can be addressed by providing access to point of sale (POS) data. Single control of replenishment or Vendor Managed Inventory (VMI) can overcome exaggerated demand forecasts. Long lead times should be reduced where economically advantageous.
Free return policies are not addressed easily. Often, such policies simply must be prohibited or limited.
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