|
Module SCM105: Performance Measures for Supply Chain Management
|
|
|
| List Price: |
Duration: |
Word Count: |
Rating: |
Datasheet: |
| $60 | Approx.
1 - 2 hours | Approx.
11,000 words |  
(4.2 / 5) |  Download |
| Certificate: |
Counts toward Fundamentals of Supply Chain Management |
|
| Promotions: | Save $60 on "Fundamentals of Supply Chain Management" bundle; buy modules SCM102, SCM103, SCM104, SCM105, and SCM106 together and save $60 |
|
|
|
|
|
The table below shows some traditional metrics, or performance measures, for three functional areas of a typical organization:
|
Table 1. Traditional Functional Performance Measures
| Manufacturing | Sales & Marketing | Engineering / R&D |
Unit cost
Labor cost
Labor productivity
Quality, scrap rate
Plant utilization
Plan vs. actual production |
Market share
Revenue
Sales growth
New "hot" products
Customer satisfaction |
Functions/features
Labor & material cost
Time-to-market
Award-winning designs
Design for manufacturability, assembly, etc. |
|
|
Looking at the examples in Table 1, approximately what percent of these traditional functional metrics are common across more than one functional area?
You wouldn't give your sales team tool belts and send them down to the assembly line any more than you would ask your engineers to do market research. While there are obvious benefits to the traditional functional organization (e.g., the ability to focus attention on particular skills needed for various parts of the business), it turns out that the standard performance measures for those functional areas can often hinder supply chain improvements. In this module we'll show examples of how that can happen and describe ways to reduce or avoid the problem...
...it is essential to have a thorough understanding of a particular business unit's business strategy and value proposition before selecting appropriate metrics. The value proposition answers the question:
"Why do customers buy from us?"
The business strategy answers the question:
"How can we ensure that customers will continue to buy from us?"
Different firms and different supply chains have different business strategies and value propositions, and answering those questions is often harder than one might imagine. To illustrate, let's look at some examples of metrics that are mis-aligned: cases in which a company discovered that they weren't measuring the things that really mattered to their customers.
Dell initially thought that their personal computer customers were most interested in buying the fastest processor available. But after getting to know their corporate customers better through ongoing face-to-face interactions, they discovered that the Information Technology (IT) departments in corporations really wanted a consistent and common platform across all their users. If the speed was 5% below the maximum currently available, it didn't matter - the consistency was what counted, since that would make technical support a great deal simpler.1 Dell's LatitudeTM notebook computers are an example resulting from these customer interactions; component stability, consistency, and backward-compatibility are features of that particular product line. Dell's InspironTM line pushes "bleeding edge" features like the fastest available processor speeds and high-end graphics components that corporate IT departments may not wish to support in wide-scale rollouts due to potential incompatibilities...
...suppose we set inventory levels so that on average we maintain a 95% Line Item Fill Rate,3 and suppose there are 14 line items on a typical order. Then what is the probability that a typical order will be filled completely, without delay? Looking at our average 95% line item fill rates, if we had only two lines on an order, then the probability the first item is in stock is 95%, and the probability the second item is in stock is also 95%. To fill the total order we need to multiply these probabilities:
Probability of complete order fill for 2-line order = .95 * .95 = 0.9025 or 90.25%.
With 14 items we multiply the probabilities for all 14 items:
Probability of complete order fill for 14-line order = (0.95)14 = 0.4877 or 48.77%.
The probability of complete order fill for a 14-line order is below 50%! The figure below shows the order fill rate corresponding to a 95% line item fill rate, based on the number of items in the order:
|
Figure 2: Order Fill Rate
(Given 95% Line Item Fill Rate)
|
This figure should convince you that if there are a large number of items on a single customer order, then the chances are good that it won't be filled completely...
(Footnotes are numbered as they are in the module)
1 "The Power of Virtual Integration: an Interview with Dell Computer's Michael Dell" by Joan Magretta, Harvard Business Review, March-April 1998. (back)
3 To implement this you could follow the procedure in Chapter 8 of Bonini, Hausman and Bierman, "Quantitative Analysis for Management", McGraw-Hill, 1997, to determine the corresponding safety stock levels. (back)
|
|
|
