bullwhip effect

Demand Forecasting, Resilience and Mapping (Book Review)

Daniel Dumke — Mon, 31 Oct 2011 14:30:00 +0000

I am a huge fan of Open Access in research and a while ago I was made aware of a book on supply chain management, which has recently been published under an open access license. The full book can be downloaded on the web site of the publisher.
The book contains 27 chapters / articles on a range of supply chain related topics, such as optimization, public sector supply chains, modeling and simulation, but also several papers which addressed supply chain risks in one way or the other.
I was especially interested in the risk related chapters, so I will give you a short overview on three of the papers:

A Hybrid Fuzzy Approach to Bullwhip Effect in Supply Chain Networks, by H. Tozan and O. Vayvay

The authors of the first paper propose a fuzzy-neural-network approach for demand forecasting. In supply chain management a test case for demand forecasting, has always been the bullwhip effect. The better your demand forecasting the less pronounced the bullwhip effect will be. And this can be a real money saver.

A simulation of their new approach shows that for a three tier supply chain (customer, retailer, factory) especially the fluctuations at the retailer can be reduced quite drastically (figure 1 a and b).

Figure 1: Orders and Production in the Base vs. Proposed Model (Tozan and Vayvay, 2011)

Integrating Lean, Agile, Resilience and Green Paradigms in Supply Chain Management (LARG_SCM), by H. Carvalho and V. Cruz-Machado

The second article I would like to highlight, tries to integrate the supply chain concepts of lean, agile, resilience and sustainability / green. One of the weaknesses in supply chain management research has always been the strong focus on one of the concepts, neglecting the fact that successful companies often have to integrate different aspects in one supply chain. A supply chain of a high-end computer manufacturer for example might have to be green and agile at the same time, a military supply chain probably should integrate the concepts of agility and resilience to be successful.
First the authors introduce the mentioned concepts and compare them in a nice table view.

Next they build a graphical model of the supply chain and added vectors to symbolize how the different concepts influence decisions in the supply chain.
Figure 2 aggregates the results.

Figure 2: Impact of different Concepts on Supply Chain Management Decisions (Carvalho and Cruz-Machado, 2011)

Supply Chain Resilience Using the Mapping Approach, by A.P. Barroso, V.H. Machado and V. Cruz-Machado

The last paper analyzes supply chain resilience using the example of a supply chain in the automotive industry. Supply chain mapping is a simple technique where the elements and connections of a supply chain are drawn on a map. The elements and connections are the enriched with additional information like inventory levels and policies or lead times.
After describing the methodology the authors use a case study to analyze the resilience of the chain. The supply chain mapping enables them to unveil several points for improvement:

The assembler has no alternative suppliers for part B. An identical situation is found for all the other parts.
Like the assembler, the 1st Tier B has no buffer stock. There is no inventory of raw materials, work in progress, and final products.

Conclusion

The first paper shows a quite interesting approach to demand forecasting, which (at least in the simulation) was able to produce much better results than more traditional approaches. If you are interested in this topic, I already discussed several other aspects of forecast risk and problems with forecasting, but I also touched other methods to improve forecasting (e.g. by information sharing).

The second paper contributes to the understanding of the effect of different aspects of some generic concepts in supply chain management. Even though it only considers a small amount of supply chain decisions, the results are quite clear: It is not possible to easily combine the different concepts and it highlights the need either to find trade-offs or to develop new approaches to satisfy all goals.

The third paper introduces the supply chain mapping methodology, which is quite simplistic in nature, but in my point of view one of the most powerful methods to analyze supply chain risks in practice. The case study should be read as an example only and gives a few hints on how to implement the methodology and analyze the results.

Reference:

Tozan, H., & Vayvay, O. (2011). A Hybrid Fuzzy Approach to Bullwhip Effect in Supply Chain Networks Supply Chain Management, 49-72

Carvalho, H., & Cruz-Machado, V. (2011). Integrating Lean, Agile, Resilience and Green Paradigms in Supply Chain Management (LARG_SCM) Supply Chain Management

Barroso, A.P., Machado, V.H., & Cruz Machado, V. (2011). Supply Chain Resilience Using the Mapping Approach Supply Chain Management

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Supply Chain Simulation and the Bullwhip Effect

Daniel Dumke — Wed, 20 Apr 2011 08:59:00 +0000

I already reviewed some articles by Denis Towill primarily because he does some interesting research on simulation and supply chains, but also because I like his clear style in his articles.

In one of his early papers (1992) he teamed up with Naim and Wikner and described state of the art strategies to fight the bullwhip effect or as it is called in the paper by its older name: Industrial Dynamics.

Industrial Dynamics

I already described the causes of the bullwhip effect and how to measure it in an older post. The bullwhip effect describes amplifications in demand within the supply chain. Exemplary effects can be seen in figure 1.

Figure 1: Effect of the Bullwhip Effect (Towill et al. 1992)

Improving SC Dynamics

One way to improve the supply chain dynamics is to introduce a leaner supply chain, which in turn will lead to lower lead times and lower amplifications.

In a lean supply chain stocks are located at a low level of “added value”, to keep the inventory cost lower.

The next decision to make is to find the right trade-off between stock levels and production rates. If you increase the flexibility of production, you might be able to reduce the inventory overall. There are two alternatives:

Fixed process rate / varying inventory levels

Varying process rate / fixed inventory

There are four sectors where where further improvements can be acchieved:

Industrial engineering improvements, eg. changing the product design or changing the layout of the production

Production engineering improvements, eg. integration and sequencing of processes to reduce lead times

Information technology improvements, eg. quicker data capture and electronic data interfaces

Operations engineering improvements, eg. pull system instead of push, shared planning

Optimization

To examine their points, Towill et al. design a simple supply chain simulation model with four stages (retailer, distributor, warehouse and factory). The results (figure 2) show that above mentioned improvements can lead to a reduction in the amplification.

Figure 2: Strategies for Reducing Demand Amplification (Towill et al. 1992)

The authors conclude that reducing the overall lead times in the supply chain may be an expensive solution. As an alternative they suggest to remove some stages from the chain which also leads to significant reductions in the amplifications. Probably the cheapest way to reduce the bullwhip effect may be to increase collaboration in the chain, in this case especially concerning forecasts and planning.

Conclusion

Simulation model cannot only help in theoretical exercises. There are very important applications in real life applications as well when the task is to optimize generic strategy recommendations like those mentioned above.

Reference:

Towill, D., Naim, M., & Wikner, J. (1992). Industrial Dynamics Simulation Models in the Design of Supply Chains International Journal of Physical Distribution & Logistics Management, 22 (5), 3-13 DOI: 10.1108/09600039210016995

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Information Sharing in Supply Chains

Daniel Dumke — Mon, 07 Mar 2011 13:25:00 +0000

Obstacles to Information Sharing

There are many obstacles to information sharing in a supply chain. Confidentiality is probably one of the biggest issues, but there are others not so obvious like antitrust regulations, the timeliness and accuracy of the provided information, differing technologies between the supply chain partners or a mismatch in the alignment of incentives. Therefore trust and cooperation become critical ingredients in a supply chain partnership.

Drivers of Information Sharing

Lee and Whang discuss those obstacles and present some common types of information sharing in their paper of 2000.
In their view supply chain management primarily implies a focus change from the business to the supply chain. Inter-firm communication gains importance. The goal should be to integrate material, information and money as the three supply chain flows. Drivers behind this development are globalization and the more cost effective IT. Nonetheless, information sharing is only an enabler for better coordination and planning of the supply chain.

Types of shared Information

There are several different categories of information shared between companies.
Inventory Level
Why:

Echelon-based inventory control is optimal under certain assumptions
A variation of the order-up-to policy based on the echelon inventory level would achieve near-optimum
Production rule will adjust the production rate at a stage according to the inventory levels at the downstream stages

Forms:

Continuos replenishment programs
Vendor managed inventory

Problem:

Information validity can be a huge problem

Sales Data
Why:

Orders from downstream serve as a critical source of information about future business
The variance of orders is often larger than the variance of sales
Mitigation of the Bullwhip effect. By having sales data, manufacturers would be in a much better position to differentiate real demands and the so-called “phantom” demands.

Order Status for Tracking/Tracing
Sales Forecast
Why:

Elimination of the Bullwhip effect and the associated inefficiencies.

Production/Delivery Schedule
Why:

Information about (raw) material availability helps the buyer to expand the planning horizon of his own production schedule
For the supplier knowledge about the manufacturers schedule can help to ensure reliable resupply

Other Information Sharing
Sharing of performance metrics or capacity can help to identify bottlenecks in the supply chain and improve the performance.

Models of Information Sharing

Lee and Whang explain three models of information sharing:
Information Transfer Model
In this model one partner takes the lead in the supply chain, the other partner(s) transfers their information to the other who maintains the database for decision making.
Third Party Model
The 3rd-party model involves a third party whose main function is to collect information and maintain it in a database for the supply chain.
Information Hub Model
The information hub model is similar to the 3rd-party model except that the third party is replaced by a system. In this case the information hub does not have to exist physically, individual functional modules may reside at scattered locations and be called and invoked only as needs arise. Thus, the information hub could exist only as a logical entity.

Conclusion

Lee and Whang show what information can be shared in a supply chain and how it can be accomplished. Nonetheless the obstacles remain and are an ongoing subject of research.

Reference:

Lee, H., & Whang, S. (2000). Information sharing in a supply chain International Journal of Manufacturing Technology and Management, 1 (1) DOI: 10.1504/IJMTM.2000.001329

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The Supply Chain Uncertainty Circle

Daniel Dumke — Wed, 27 Oct 2010 16:17:00 +0000

How to shrink the “Uncertainty Circle” is the topic of a paper I read today. It has been written by Rachel Mason-Jones and Denis R. Towill and can be downloaded here free of charge.

This article from 1998 focusses on how to shrink the uncertainty within a supply chain and therefore improve performance, since

we believe that those companies which cope best with

uncertainty, are most likely to produce internationally

competitive bottom-line performance.

This hypothesis is also backed by several other authors like, Craighead et al. (2009) or Hendricks and Singhal (2005).

Within the product delivery process for a single company, the authors find four causes of uncertainty: supply side, manufacturing processes, control systems and demand side (see figure 1).

Causes of Supply Chain Uncertainty (Mason-Jones and Towill 1998)

The Circle

With the Uncertainty Circle it is now possible to visualize to effect of shrinking one of the risks without the other: Overall risk is only reduced by a (comparatively) small amount. This furthermore implies the goal for supply chain risk managers: Reduction of risk on all frontiers!

Uncertainty Circle and Risk Reduction Process (Mason-Jones and Towill 1998)

This of course does not have to happen at the same time. Companies usually start with improvements in their own manufacturing processes and establish lean thinking to improve lead times and quality. As a next step this concept may be enlarged to the companies suppliers as well to reduce supply side risks.

The authors then argue that much potential is left untouched on the reduction of demand side uncertainties and control systems.

Demand side risks can be reduced by improving the information flow from the end-customer throughout the supply chain and companies can tackle control risks by improving their ability to act on this new information with a adequate Decision Support System.

Supply Chain Design Strategies

The authors then simulate a given supply chain and test strategies to improve (in figure 3) “Information pipeline time compression only”, “Material flow pipeline compression only”, “Total Cycle Time compression”.

Rating Risk Mitigation Strategies (click to enlarge; Mason-Jones and Towill 1998)

Conclusion

Even though the article has been published twelve years ago the concept of the Uncertainty Circle can still be used as a framework to reduce risks within a supply chain. The comparison table shows the effects of different risk mitigation strategies clearly and should adapted and used by more companies.

Reference:

Mason-Jones, Rachel, & Towill, Denis R. (1998). Shrinking the Supply Chain Uncertainty Circle IOM Control, 24 (7), 17-22

Craighead, C., Hult, G., & Ketchen Jr., D. (2009). The effects of innovation–cost strategy, knowledge, and action in the supply chain on firm performance Journal of Operations Management, 27 (5), 405-421 DOI: 10.1016/j.jom.2009.01.002

Hendricks, K., & Singhal, V. (2005). An empirical analysis of the effect of supply chain disruptions on long-run stock price performance and equity risk of the firm Production and Operations Managment, 14 (1), 35-52

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Measuring the Bullwhip Effect in Supply Chains

Daniel Dumke — Mon, 23 Aug 2010 06:46:00 +0000

The bullwhip effect in supply chains has been around for some time now. The term “bullwhip effect” originated at Procter & Gamble, and is defined as: demand amplification across echelons within a supply chain. This describes the effect that end customer demand may be very static (as for “Pampers” by Procter & Gamble), but the demand experienced by the manufacturer or supplier shows amplified demand variations. (Fransoo and Wouters (2000))

Causes of the Bullwhip Effect

Lee et al. (1997) first analyzed the causes of the bullwhip effect:

Demand forecast updating
Demand forecast usually are based on the orders of the preceding echelon and not on the actual customer demand
Order batching
Orders are usually aggregated to batches to save cost.
Price fluctuations
Promotions and other effects can lead to price fluctuations of the product. When the price is perceived to be lower, orders go up and vis-versa.
Rationing and shortage gaming
For some products (eg. new iPhone) which may be short in supply, game theory suggests that it might be rational to order more than actually needed, since the number of delivered products usually is a percentage of the number of products ordered.

Measuring the Bullwhip Effect

The bullwhip effect is measured per echelon by the following formula:

Measuring the Bullwhip Effect (Source: Fransoo and Wouters (2000))

So first we standardize the standard deviation for incoming and outgoing demand. The bullwhip effect is measured as the quotient of these numbers.

Issues

The following issues arise when measuring the bullwhip effect.

Aggregation
To measure the bullwhip effect correctly one has to be aware of different data aggregation levels one could choose; one can aggregate demand by echelon, outlet, product, time. Each aggregation will give different insights and different levels of the bullwhip effect.
Incomplete data
There may be a conceptual imbalance between incoming demand and outgoing demand, furthermore the information on those values may be incomplete.
Filtering for causes
To analyze and act on the bullwhip effect, one has to understand the causes of the demand variations at hand.

Findings from case studies

Fransoo and Wouters (2000) find in their specific case study that,

filtering for causes of the bullwhip effect can be quite challenging.
the bullwhip effect caused by price fluctuations was negligible.
shortage gaming did occur and posed a major problem for the supply chain.

Reference:

Fransoo, J., & Wouters, M. (2000). Measuring the bullwhip effect in the supply chain Supply Chain Management: An International Journal, 5 (2), 78-89 DOI: 10.1108/13598540010319993

Lee, H., Padmanabhan, V., & Whang, S. (1997). Information Distortion in a Supply Chain: The Bullwhip Effect Management Science, 43 (4), 546-558 DOI: 10.1287/mnsc.43.4.546

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