decision variables

Types of Supply Chains

Daniel Dumke — Mon, 28 Nov 2011 15:59:00 +0000

As a good book with current research on supply chain management I often recommend Supply Chain Management and Advanced Planning: Concepts, Models, Software, and Case Studies, edited by Stadtler and Kilger. In this review I refer to chapter 3 of that book, which goes into the details of different types of supply chains.

The understanding of the specific types of supply chains is especially useful from a viewpoint where multiple supply chains are analyzed. This could be within a larger company with several different supply chains or a consultant or researcher who wants to tailer strategies to reduce risks for a specific type of supply chain network.

Structure

The chapter is conceptual in nature and prescribes supply chain attributes which may be functional or structural in nature. As the last part a consumer goods and a computer assembly supply chain from a previous case study are characterized according to that scheme.

Functional attributes

The functional attributes fall into four categories: procurement type, production type, distribution type and sales type (figure 1).

Figure 1: Supply Chain Typology: Functional Attributes (Meyr, Stadtler, 2008)

Structural attributes

The structural attributes define the topography of the supply chain and the integration and coordination. They are summarized in figure 2.

Figure 2: Supply Chain Typology: Structural Attributes (Meyr, Stadtler, 2008)

Case: consumer goods

Using the data from a case study the authors then select the parameters of a typical supply chain in the consumer goods industry. Figure 3 shows the supply chain’s distribution network, starting with the factories, the central and regional warehouses and the trans-shipment points (CW, RW, TP) and ending with the retailers.

Figure 3: Case Study: Consumer Goods (Meyr, Stadtler, 2008)

From the case study the authors select the following functional and structural attributes of a typical consumer goods supply chain (figure 4).

Figure 4: Case Study: Consumer Goods Supply Chain (Meyr, Stadtler, 2008)

Case: computer assembly

Another case study unravels the typical properties of a supply chain for computer assembly (figure 5).

Figure 5: Case Study: Computer Assembly (Meyr, Stadtler, 2008)

Conclusion

Analyzing the strategic properties of supply chains is also quite important to my own research as well, and I find the selection of the different attributes in this chapter quite intriguing, even though some of them might be too far on the operational level.

The application of such a framework on the other hand requires a lot of care, since the parameters have to be analyzed and applied equally for each supply chain. Especially in the case of a generalization, where a “typical” case should be described, it is, in my point of view, necessary that the foundation of these common denominators are clearly described, a feature which is missing in the chapter completely.

I also doubt that it is possible to deduce the properties of an average supply chain from a singular case study at all. So one has to ask if the right method has been chosen for this chapter.

Nonetheless, overall I do think the categories which have been suggested can be very useful to analyze and differentiate supply chain types.

Reference:

Meyr, H., & Stadtler, H (2008). Types of Supply Chains Supply Chain Management and Advanced Planning, 65-80

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Finding the Right Supply Chain for your Product!

Daniel Dumke — Wed, 12 Oct 2011 14:08:00 +0000

This again is an old classic in supply chain risk literature. In 1997 Marshall L. Fisher published this article in the Harvard Business Review targeting a simple question: “What is the Right Supply Chain for Your Product?”

It is noteworthy that this appears to be one of the most often cited papers in supply chain management. So I overlook the fact that it is quite weak on the methodological foundations.

The full text of this paper can be found here.

Product types

One of the major highlights of Fisher’s work is its simplicity. The author defines two basic products: functional and innovative products (figure 1). The major differentiating factor is the uncertainty of demand: Functional products (e.g. a classic Coke) show a rather predictable demand pattern and have long product cycles. Innovative products on the other hand show an unpredictable demand, and the life cycle can be as short as a few month.

Figure 1: Product Properties (Fisher 1997)

Supply chain types

Staying in the realm of simplicity Fisher also sees two basic types of supply chains or supply chain strategies a company can select (figure 2).

First there is the Physically Efficient Process, which can supply a specified amount of products at the lowest cost possible.

Second there is the Market-Responsive Process, which focusses on quick adaptability towards changing market needs.

Figure 2: Supply Chain Categorization (Fisher 1997)

Strategic Implications

The strategic implications become obvious quite quickly. One does not want to match a efficient supply chain with a fast changing market, since this would probably leave the company either with a huge excess inventory or, if too little is produced, with many unsatisfied customers.

On the opposite side one should not match a responsive supply chain with a steady market demand. Cost for maintaining this flexibility are potentially to high to maintain a positive product margin.

Figure 3 shows the matches in a matrix.

Figure 3: Matrix Matching Products with Supply Chain (Fisher 1997)

Conclusion

The implications of this concept are intriguing, just define the type of your products and adapt the supply chain accordingly. Fisher validates his points by describing two case studies (Campbell Soups and Sport Obermeyer) where his concept was successfully employed.

On the other hand, the concept is really simplistic. There are overlaps in the defining factors of the process and product categories making them harder to separate than it seems: What really is a predictive demand and when is it unpredictable?

And the critique continues. In 2011 Perez-Franco, Singh and Sheffi documented some of it in their article (which I reviewed here), which indicates that the concept is not viable in reality.

Of course this does not mean that I underrate Fisher’s contributions: with this article he still contributed to the discussion of supply chain strategy and its influencing factors and it serves as a basis for many more works, as I mentioned above.

The conclusion I draw:

Consistency: Of course there should be a connection between the product of a company and it’s supply chain strategy. But there are many other factors which have to influence an effective and efficient supply chain design as well.

Methodological: Never rely solely on a handful of sample case studies as the foundation of a concept. Sometimes you will be right, but very often you will be wrong.

You can continue reading on Discovering the Right Planning Approach for your Supply Chain.

Reference:

Fisher, M.L. (1997). What is the Right Supply Chain for Your Product? Harvard Business Review, March-April, 105-116

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Customer Service or Cost? Optimization of the Supply Chain Design

Daniel Dumke — Mon, 28 Feb 2011 14:09:00 +0000

In several articles I talked about competing objectives to the classical cost reduction goals (eg. with responsiveness or with reducing uncertainty).

Shen and Daskin (2005) explore the “Trade-offs Between Customer Service and Cost in Integrated Supply Chain Design”. The goal is to find ways to simultaneously improve customer service and reduce operating cost.

Modell

The authors model a three tier supply chain, with a supplier, distribution center, and retailer.
The following cost factors are considered: Facility Location, Inventory, divided into working inventory and safety stock, and distribution cost.
Since the goal is to find design decisions which lead to reduced cost and improved service the customer service is measured by the fraction of all demands that are within a specific distance to the distribution center. To make the cost and service requirements comparable Shen and Daskin have to introduce a weighing factor, in this case for the uncovered demand. In practice this factor represents the cost equivalent of any unmet demand, including lost sales, but also effects of negative reputation due to bad service, etc.

Solution

Shen and Daskin use an genetic algorithm to solve the network design problem. A genetic algorithm uses several concepts which follow the genetic selection metaphor. Operators in genetic algorithms are, for example: preservation of elites, mutations and expansions. Using this they reach optimal or near optimal solutions.

Management Insight

The authors gain two major insights from their experiments:

The cost difference between the cost-minimization solution and the service-maximization solution can be quite large.
Significant improvements in customer service can often be achieved at relatively little cost.

Conclusion

Shen and Daskin found the trade-off they promised in the title. At least for most parts of such a trade-off curve it is impossible to make service improvements without adding cost. Even though the insights somehow follow general intuition of increasing marginal cost for any trade-off, it stands as an important finding.
But as usual with modelling, it is important what factors you consider and which you leave out. There very likely could be positive effects on costs if the customer reaction to varying service levels had been considered.

You also already realized that the service measure used is probably not perfect as well. Most companies I talked to are using a more dimensional measure, which includes the fact that also customers out of a specific range can still be serviced.

Furthermore the authors could have used specific methods for calculating the weights of the service consideration (eg. using AHP, analyzed here).

Reference:

Shen, Z., & Daskin, M. (2005). Trade-offs Between Customer Service and Cost in Integrated Supply Chain Design Manufacturing Service Operations Management, 7 (3), 188-207 DOI: 10.1287/msom.1050.0083

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Supply Chain Design: Capacity, Flexibility and Wholesale Price Strategies

Daniel Dumke — Wed, 24 Nov 2010 16:49:00 +0000

This is the forth contribution to my series on doctoral dissertations on Supply Chain Risk Management. An immense effort and dedication is spent on these works only to find the results hidden in the libraries. So the goal is raise interest in the research of my peers.

Author / Topic

This dissertation was written by Brian Tomlin in 1999 as his doctoral thesis at the Massachusetts Institute of Technology, Cambridge, USA. It can be downloaded here at his homepage at his current employer (Tuck School of Business at Dartmouth). The title is:

Supply Chain Design: Capacity, Flexibility and Wholesale Price Strategies

Summary

Tomlin cumulates three distinct topics and analyzes them in depth in his dissertation. His core question is always the supply chain design, with demand uncertainty as the dominant risk.

Wholesale Pricing Strategies
Using a game theoretic model with two actors (supplier and manufacturer) he explores the best strategies for setting the wholesale price. The objective is to maximize the supply chain profit (think of it as a return on investment in the supply chain). He shows, that no matter if quantity-discount or quantity-premium pricing schedules are used, non-linear pricing schedules outperform linear pricing schedules.
Process Flexibility Strategies
He then tackles flexibility strategies in a multi-product, multi-stage supply chain environment. He first identifies two inefficiencies which only occur in a multi-stage environment, namely stage-spanning bottlenecks and floating bottlenecks. He develops a mathematical supply chain optimization model and based on the results he concludes, that “in supply chains with a large number of products or stages, additional flexibility is advisable, especially for stages in which the capacity is not much greater than the expected demand.”
Capacity Decision
With this chapter on capacity decision making in multi-product, multi-stage supply chains he walks on new grounds, since up to then only single-stage models have been analyzed. He uses two criterion for his analysis: service level and expected shortfall. The results of his analysis show that under optimal capacity allocation: for every stage must hold true: for a small increase in a stage’s capacity, the decrease in shortfall divided by the capacity cost should be the same for all stages.

Conclusion

I have to admit that I do not really like the segregated design of the thesis itself, analyzing one topic in depth usually should be sufficient for one dissertation. On the other hand the work by Tomlin on the three topics would probably also have sufficed for two or three separate dissertations as well.

Compared to other dissertations I read it was refreshing to see, that he did not spend (too) much time on making an in depth literature review beforehand but focussed very much on his own works.

Reference:

Brian T. Tomlin (2000). Supply Chain Design: Capacity, Flexibility and Wholesale Price Strategies Massachusetts Institute of Technology, Dissertation

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Supply Chain Performance and its Topological Features

Daniel Dumke — Thu, 24 Jun 2010 08:04:00 +0000

Perhaps this research by Pero et al. can support small and medium sized companies with the design and redesign of its supply chain network.
The goal of the study was to analyze the connection between topological features of the supply chain and the resulting supply chain performance.

Method and Model

The authors used simulation techniques and statistical analysis to simulate a pull based supply network. The network consists of a retailer-, distributor- and manufacturer-level.
Demand and lead times were random, the supply chain performance was measured with stock outs that occured.

Results

The results show that:

An increasing number of nodes at each level increases the probability of stock outs.
The performance of the supply chain was not affected by the number of levels in the supply chain and the distance between the nodes.
Furthermore the number of sources for each node seems to increase the risk for stock outs.

Conclusion

Due to their simple structure the results are very compelling. To get the perfect supply chain performance all you have to do is following the above mentioned “rules” (results).

But you have to keep in mind the premises on which the model builds:
For example, supply chain performance is only measured in terms of “stock outs”. Stock out should never be the single performance measure you rely on, since there may not be a direct correlation with more important company goals like profit, …

Besides, it would be advised to model your own individual supply chain to accommodate for your specific configuration and needs. Especially strategic decisions like the supply chain configuration should be important enough to not take the shortcut.

Reference:

Pero, M., Rossi, T., Noé, C., & Sianesi, A. (2010). An exploratory study of the relation between supply chain topological features and supply chain performance International Journal of Production Economics, 123 (2), 266-278 DOI: 10.1016/j.ijpe.2009.08.030

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Integration of Network Design and Production/Distribution Planning

Daniel Dumke — Tue, 02 Feb 2010 18:20:11 +0000

Jang et al. (2002): “A combined model of network design and production/distribution planning for a supply network” suggest a framework for integrating the strategic supply chain network design with the operational planning needed for production and distribution.
They make the point that strategical and operational planning should be integrated to find the optimal solution for the company. The following problems are affected.

Network design problem from raw material suppliers to customers.
Problem of production quota assignment, by which specific quantities are distributed to each of the
several facilities producing the same items.
Production planning problem, considering the global BOM relationship.

Global capacity planning for various manufacturing facilities in the network.
Distribution planning that determines optimal distribution channel and quantity.

Thereof the first and the fourth are strategic in nature, the second and third are tactical and the last could be classified as operational.

Framework

On top of these goals they build the following framework:
The two major modules are the “supply network design optimization module” and the “production and distribution planning module” (ppdp) these are connected via a “model management module” and a “data management module”. The overall goal of the framework is to make the decision-making easier for the responsible manager by hiding the complexity of the models and data.

Model

Next suggest specific models and solution algorithms for the network design and the ppdp module. Using Lagrangian relaxation algorithm and linear programming for the first and a genetic algorithm for the second problem.

Evaluation

I find the idea to combine strategical, tactical and operational problems in one general framework very compelling. But still there are some drawbacks in this paper.

The different problem types (strategic, …) are not separated clearly. There is no link between the suggested framework and these types. Strategic and operational problems can occur in each of the modules.
Jang et al. argue that there is a benefit from integrating the strategical and operational problems, but in their calculations they only refer to the individual planning problems (network design or ppdp). I still believe that integration improves overall performance, but there is no evidence in this paper.
There is not a single sign of uncertainty within this framework. All parameters are fixed and known. There is only a reference that the manager could use this framework to do scenario analysis.
Well last but not least, I am not sure what this article is about. Is the major goal to suggest a framework for general use? Is it a recipe for implementing a specific model within a company or is it experimental data on how different algorithms lead to different non optimal solutions?

Reference:

Jang, Y. (2002). A combined model of network design and production/distribution planning for a supply network Computers & Industrial Engineering, 43 (1-2), 263-281 DOI: 10.1016/S0360-8352(02)00074-8

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