Was the Pandemic Supply Chain Crisis Caused by Bad Data?
Was the Pandemic Supply Chain Crisis Caused by Bad Data?
The main driver of supply chain planning is understanding expected demand through forecasting. However, Information about the end point of the supply chain – where the customer selects a product and likes it enough to purchase it again – can sometimes be elusive. It also depends on many factors including weather, holidays, promotions and many more. Furthermore, companies have easier access to order data and often use that as a proxy for demand.
During the pandemic, we witnessed how huge changes in demand affected the ability of suppliers to respond. This ultimately led to excess inventory when the suppliers inevitably over reacted to the demand changes. This would seem to be an extreme situation and in normal times most companies have systems in place to manage inventory and deal with the regular ebb and flow of demand. But even during normal times, there is a natural tendency to react to increases or decreases in demand, especially large ones by either over or under ordering.
This over reaction is called the bullwhip effect, which suggests that demand variability increases as one moves up a supply chain. The two major factors that cause the bullwhip effect are demand forecasting and order lead times. Research has shown that having centralized customer demand information can reduce, but not eliminate the bullwhip effect.
One of the underlying reasons driving the bullwhip effect is that data analysis has proven that variability in customer demand is significantly lower than variability in retail orders (i.e. orders from retailers to their suppliers). This implies that predicting consumption should be easier than predicting retail orders, and indeed, the accuracy of a typical consumer packaged goods manufacturer’s forecast for market demand is quite high. At any moment the demand forecasts at the SKU, week, and retailer level for five to eight weeks out have proved to be 85% accurate.
What if retailers could create less volatile orders and communicate more effectively with their suppliers? Will this improve the entire supply chain?
Using modern technology such as Supplyve, which provides easy to use mobile phone access to the smallest stores, users have a better handle on their ordering and inventories and suppliers will have store level data visibility, which translates to more consistent, timely and accurate orders - mitigating the bullwhip effect.
Was the Pandemic Supply Chain Crisis Caused by Bad Data?
The main driver of supply chain planning is understanding expected demand through forecasting. However, Information about the end point of the supply chain – where the customer selects a product and likes it enough to purchase it again – can sometimes be elusive. It also depends on many factors including weather, holidays, promotions and many more. Furthermore, companies have easier access to order data and often use that as a proxy for demand.
During the pandemic, we witnessed how huge changes in demand affected the ability of suppliers to respond. This ultimately led to excess inventory when the suppliers inevitably over reacted to the demand changes. This would seem to be an extreme situation and in normal times most companies have systems in place to manage inventory and deal with the regular ebb and flow of demand. But even during normal times, there is a natural tendency to react to increases or decreases in demand, especially large ones by either over or under ordering.
This over reaction is called the bullwhip effect, which suggests that demand variability increases as one moves up a supply chain. The two major factors that cause the bullwhip effect are demand forecasting and order lead times. Research has shown that having centralized customer demand information can reduce, but not eliminate the bullwhip effect.
One of the underlying reasons driving the bullwhip effect is that data analysis has proven that variability in customer demand is significantly lower than variability in retail orders (i.e. orders from retailers to their suppliers). This implies that predicting consumption should be easier than predicting retail orders, and indeed, the accuracy of a typical consumer packaged goods manufacturer’s forecast for market demand is quite high. At any moment the demand forecasts at the SKU, week, and retailer level for five to eight weeks out have proved to be 85% accurate.
What if retailers could create less volatile orders and communicate more effectively with their suppliers? Will this improve the entire supply chain?
Using modern technology such as Supplyve, which provides easy to use mobile phone access to the smallest stores, users have a better handle on their ordering and inventories and suppliers will have store level data visibility, which translates to more consistent, timely and accurate orders - mitigating the bullwhip effect.
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