Uncertainty implied demand

Implied Demand Uncertainty. At first glance, it may appear that each of the customer need categories should be viewed differently, but in a fundamental sense, each customer need can be translated into the metric of implied demand uncertainty, which is demand uncertainty imposed on the supply chain because of the customer needs it seeks to satisfy. 

We make a distinction between demand uncertainty and implied demand uncertainty. 

Demand uncertainty reflects the uncertainty of customer demand for a product. Implied demand uncertainty, in contrast, is the resulting uncertainty for only the portion of the demand that the supply chain plans to satisfy based on the attributes the customer desires. For example, a firm supplying only emergency orders for a product will face a higher implied demand uncertainty than a firm that supplies the same product with a long lead time, as the second firm has an opportunity to fulfill the orders evenly over the long lead time. 

Both the product demand uncertainty and various customer needs that the supply chain tries to fill affect implied demand uncertainty. Table 2-1 illustrates how various customer needs affect implied demand uncertainty. 

As each individual customer need contributes to the implied demand uncertainty, we can use implied demand uncertainty as a common metric with which to distinguish different types of demand. 

Impact of Customer Needs on Implied Demand Uncertainty Customer Need Causes Implied Demand Uncertainty to. .. 

Fisher (1997) pointed out that implied demand uncertainty is often correlated with other characteristics of demand, as shown in Table 2-2. An explanation follows ... 

Increased implied demand uncertainty leads to increased difficulty in matching supply with demand. For a given product, this dynamic can lead to either a stockout or an oversupply situation. Increased implied demand uncertainty thus leads to both higher oversupply... 

Markdowns are high for products with greater implied demand uncertainty because oversupply often results. 

First, let us take an example of a product with low implied demand uncertainty—such as table salt. Salt has a low margin, accurate demand forecasts, low stockout rates, and virtually no markdowns. These characteristics match well with Fisher s chart of characteristics for products with highly certain demand. 

On the other end of the spectrum, a new cell phone has high implied demand uncertainty. It will likely have a high margin, inaccurate demand forecasts, high stockout rates (if it is successful), and large markdowns (if it is a failure). This, too, matches well with Table 2-2. 

Correlation Between Implied Demand Uncertainty and Other Attributes... 

Now, consider a pasta manufacturer such as Barilla. Pasta is a product with relatively stable customer demand, giving it a low implied demand uncertainty. Supply is also quite predictable. Barilla could design a highly responsive supply chain in which pasta is custom made in small batches in response to customer orders and shipped via a rapid transportation mode such as FedEx. This choice would obviously make the pasta prohibitively expensive, resulting in a loss of customers. BariUa, therefore, is in a much better position if it designs a more efficient supply chain with a focus on cost reduction. 

Where would you place the demand faced by Nordstrom on the implied demand uncertainty spectrum Why ... 

Especially in an industrial environment conditional demand is an important concept. This is because orders usually allow for some delivery time. This delivery time is in many cases long enough to be taken into account in production scheduling. As the deadline for orders for a certain date of delivery comes closer one can compare the original forecast with the orders that were already received. It is intuitively clear that if many orders were already received this implies a somewhat increasing forecast with less uncertainty. Sometimes orders that were already received can be used to automate the forecast to a certain extent because one knows that usually 25% are ordered four weeks in advance, 50% are ordered two weeks in advance and the like. We assume here that there is a valid latest forecast at the point in time where a decision on the next production volume is necessary and the orders that were already received are taken into account at that fixed point in time. 

ATP is again a bit more reliable as it is based on inventory, which is a function of the schedule or commitment to production. To handle the discretion applied to most demand, ATP relies foremost on what the system says is in inventory or stock buffers within the limits of the master schedule and not the flexible capacity of CTP. The problems with ATP are associated with the commitments or process difficulties. Externally, this implies that there is a potential problem with understanding the commitment of the buyer and matching it with the commitment to produce. The greater the uncertainty between the two factors, the more inventory or production is required for the producer. Internally, the ability of a company to promise from a schedule is quantified in classical measures inventory, bills of material, routing and master schedule accuracy. Again, it is inventory that is required to compensate for the lack of ability in these matters in order to keep promises. To measure the level of commitment or the ability the firm has for keeping its scheduled promises, you need only to look at the quality of its processes from which those promises are made. 

We can create a spectrum of uncertainty by combining the demand and supply uncertainty. This implied uncertainty spectrum is shown in Figure 2-2. 

STEP 2 UNDERSTANDING THE SUPPLY CHAIN CAPABILITIES After understanding the nncertainty that the company faces, the next question is How does the firm best meet demand in that uncertain enviromnent Creating strategic fit is all about designing a supply chain whose responsiveness aligns with the implied uncertainty it faces. 

FIGURE 2-2 The Implied Uncertainty (Demand and Supply) Spectrum... 

These abilities are similar to many of the characteristics of demand and supply that led to high implied uncertainty. The more of these abilities a supply chain has, the more responsive it is. 

In our discussion so far, we have assumed that each stage of the supply chain has a well-defined demand and supply distribution that it uses to set its safety inventory levels. In practice, this is not true for multiechelon supply chains. Consider a simple multiechelon supply chain with a supplier feeding a retailer that sells to the final customer. The retailer needs to know demand as well as supply uncertainty to set safety inventory levels. Supply uncertainty, however, is influenced by the level of safety inventory the supplier chooses to carry. If a retailer order arrives when the supplier has enough inventory, the supply lead time is short. In contrast, if the retailer order arrives when the supplier is out of stock, the replenishment lead time for the retailer increases. Thus, if the supplier increases its level of safety inventory, the retailer can reduce the safety inventory it holds. This implies that the level of safety inventory at aU stages in a multiechelon supply chain should be related. 

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