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Risk Healthcheck - This Week in Supply Chain Management (6 / 2012)

Daniel Dumke — Fri, 10 Feb 2012 11:36:35 +0000

This week was low on news, beside the frosty temperatures in Europe.

Risk assessment tool

It’s less of an article but still nice: The Supply Chain Risk Healthcheck by the insurance company Zurich. By answering a few questions to key drivers in supply chain risk the tool highlights improvement potential. (Zurich Healthcheck)

Already tweeted

There are also some article I already tweeted during the week. You can follow me on twitter.

“Flexibility and risk management” is the topic of David Simchi-Levi’s session at the CSCMP Annual Conference. http://t.co/Og8d5uhU
You’re So Predictable. Daniel Kahneman and the Science of Human Fallibility http://t.co/S6P30aDS
How do I manage risks associated with my supply chain? http://t.co/fidkqOLu

Enjoy your weekend!

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Behavioral Risks in Supply Networks

Daniel Dumke — Mon, 09 Jan 2012 13:20:01 +0000

Not only earthquakes and terrorist attacks can lead to supply chain disruptions. Supply chains are also subject to behavioral risks, meaning that participants of the supply chain could exhibit behavior which might be consistent with their goals, but contrary to the goals of the supply chain’s other participants.

And so one has to ask: How should a company mitigate behavioral risks? And this is what Seiter did and his work was published as chapter 15 of another great book on supply chain risks (Supply Chain Risk by Zsidisin and Ritchie), which can be bought at amazon.com, if you are interested in reading more.

Methods

Seiter starts with the assumptions of the principal agent theory where interactions between individuals, which are built on asymmetric information may lead to problems for either or both parties.

Figure 1 shows the principal-agent relationships in a supply chain.

Figure 1: Principals and Agents in a Supply Chain (Seiter, 2009)

Next, a preliminary study was conducted to gather information on the strategies which are employed in practice. Ten companies were interviewed and the following strategies were found to be the most frequently used:

Communication: Communication is the formal and informal exchange of information between the partners within the supply network. There are many different forms of communication, e.g., oral communication or written communication. All forms of communication have in common that they reduce the degree of asymmetric information. But, the size of this effect depends on the frequency and quality of the communication.

Partner selection: Partner selection is the examination of the match between the potential partners in a supply network (Das and Teng 2003). From a sequence perspective the selection of appropriate partners is the first instrument to prevent the opportunistic behaviour of suppliers. The literature suggests many recommendations concerning the selection criteria to use (for an overview see Seiter and Isensee 2007). By using a set of criteria the partner selection process seeks to ensure partner “fit”. There are different types of fit, e.g., strategic fit, and resources fit (Das and Teng 1999). However, the most important fit is the fit of the partners’ objectives. Only if all partners in the supply network can reach their goals simultaneously, will dysfunctional behaviour become unlikely (Das and Teng 1999).

Inter-organizational cost accounting: A typical form of opportunistic behaviour a supplier can practice is the abuse of the information asymmetry. For example, to pretend that costs are higher and accordingly seek a higher price. In practice different forms of inter-organizational cost accounting may be implemented to avoid such behaviour. The forms vary from partial approaches to full approaches like open-book accounting (Kajüter and Kulmala 2005).

Inter-organizational planning: Another way to prevent opportunistic behaviour is to reduce the extent of asymmetric information by sharing planning data. Several concepts have emerged in practice like “collaborative planning, forecasting and replenishment” or “supply chain planning”. The diffusion of inter-organizational planning is also supported by a great variety of software solutions that support the structured planning along the supply network.

Sanctions: Sanctions, in the event of the occurrence of opportunistic behaviour, are one of the first instruments mentioned by each interviewee. Normally, sanctions are defined in the formal bilateral contracts between buyer and supplier (Wuyts and Geyskens 2005). Especially, in cases where product-specific information is shared in the partnership between buyer and supplier, the contracts include paragraphs preventing the abuse of this information. This is more often the case when it comes to international supply networks.

As a second step the influence of these strategies is tested using a qualitative survey.

Figure 2 shows the proposed hypothesis. It considers not only direct effects on the opportunistic behavior but also indirect effects, which support the build-up of asymmetric information.

Figure 2: Hypothesis for direct and indirect Influence of the proposed Strategies (Seiter, 2009)

Results

The author finds some clear results regarding communication and inter-organizational cost accounting, which both significantly help to reduce opportunistic behavior within the supply chain. But, “no significant effects regarding possible sanctions and inter-organizational planning systems were found.” Furthermore, there was a positive correlation between partner selection efforts and opportunism was found indicating that higher efforts could lead to higher opportunism.

To explain the non-significant results, the author conducted a focus group discussion which lead to the following plausible explanations for the findings:

The group came to the conclusion that sanctions cannot provide perfect protection as all future circumstances would have to be anticipated and planned for accordingly.

The absence of the anticipated effect of inter-organizational planning could be grounded in the construct itself. In practice several different types of planning systems do exist, some very simple and others very complex types. The effective operationalization of this construct may require the indicators to be more specific about the specific types of planning systems used.

[Regarding the positive correlation between partnerselection and opportunism:] The survey captures only the initial partner selection effort but not whether partners are tested continuously. Therefore, this unexpected result may possibly be due to the need to define the indicators more closely to include this possibility.

Finally the author concludes, that “communication seems to be the central factor for preventing opportunistic behaviour in supply networks. For this reason the improvement of communication should be a primary objective.”

Conclusion

I liked the study design due to its concise structure and the extensive elaboration on the study methods. The results show, that there still is room for improvement. A more detailed model might have lead to more significant results with the other strategies employed.

Reference:

Seiter, M. (2009). Behavioural Risks in Supply Networks Supply Chain Risk - A Handbook of Assessment, Management, and Performance, 235-247 DOI: 10.1007/978-0-387-79934-6_15

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Dual Supply Channel Disruption and Supply Chain Design

Daniel Dumke — Mon, 19 Dec 2011 16:35:00 +0000

This is the continuation of the Greece week and today I am going to have a look at a mathematical model to capture the effects of dual disruptions in a news-vendor model.

This time the three authors (Xanthopoulos, Vlachos and Iakovou) come from the Aristotle University of Thessaloniki.

Method

The authors developed a model where a retailer is able to order from two different supply channels. Figure 1 shows the supply chain schematic.
Three different types of the model are modeled separately:

unconstrained model,
fill rate constrained model,
Type I service level constraint,

The decision variables are the order quantities from channel one and two.

Figure 1: Schematics of the Supply Chain Model (Xanthopoulos et al., 2012)

Analysis and managerial insights

The models are analyzed using a numeric analysis with the parameters given in figure 2. Different values for the purchasing cost c_i, disruption probabilities for the supply channels one and two (p_i). The impact of the disruption is denoted by y_i.

Figure 2: Experimentation Data (Xanthopoulos et al., 2012)

144 parameter combinations are analyzed. Figure 2 shows the iso-profit lines for the order quantities Q₁ and Q₂ for different impact levels in the first supply channel (y₁: a) 0.9, b) 0.6, c) 0.1)

Figure 3: Effect of different Disruption Impacts on Supplier 1 (Xanthopoulos et al., 2012)

The authors conclude

It is also observed that as the impact of a disruption on the first channel increases, the optimal solution moves from a solution that mainly utilizes the first supply chain to a solution that mainly utilizes the second one.

Depending on the values of the purchase costs (c_i), the disruption probabilities (p_i), and the effect of a disruption (y_i), one of the suppliers may dominate over the other one, leading to a single supply source.

Nevertheless, for both the uncapacitated and capacitated problems for certain combinations of the values of c_i, p_i, and y_i, a dual-sourcing policy outperforms a single-sourcing one. In such cases, it is optimal to place the larger part of the total order to the dominant channel and its lesser part to the second one, so as to hedge/mitigate the disruption risks.

Moreover, for very high service levels (near 100%), large orders should be placed to both suppliers.

[In] the case in which the first supplier is the dominant supplier as well as the more reliable from the two. In such a case, no matter how high the fill rate will be, orders should be placed only to the first supplier and the second one should not be activated at all.

Conclusion

This article provides some insights into a dual supply channel disruption case. From the title and abstract of the article, I was however expecting more discussion of the results, perhaps the impact of other driving factors beside the impact, probability and ordering cost.

Reference:

Xanthopoulos, A., Vlachos, D., & Iakovou, E. (2012). Optimal newsvendor policies for dual-sourcing supply chains: A disruption risk management framework Computers & Operations Research, 39 (2), 350-357

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Supplier Risk Monitoring for the Automotive Industry

Daniel Dumke — Wed, 23 Nov 2011 10:22:00 +0000

A typical supply chain risk management process consists of four steps: risk identification, assessment, management and monitoring. From those steps, one of the most neglected step is the risk monitoring.
Risk monitoring implies two different actions: Continuous risk assessment and actions, as soon as pre-defined limits are reached.

So this article sheds light on the risk monitoring, from an article by Blackhurst, Scheibe and Johnson (“Supplier risk assessment and monitoring for the automotive industry”).

Literature

After the analysis of the current literature the authors come to the conclusion: “Of the two methodologies we did find to monitor risks, neither addresses measuring, assessing and monitoring supplier risk over time. In addition, research that has examined supplier evaluation models has indicated that most methods are too mathematically complex to implement and understand, require excessive amounts of data, or are too subjective.”

So the authors want to close this gap, by developing a risk assessment and monitoring methodology for a US automotive manufacturer.

Risk assessment and monitoring methodology

Core of the risk monitoring methodology is the risk assessment, which is done using several categories of risk. The authors extend the framework of Chopra and Sodhi (2004), which contains nine risk categories: disruptions, delays, systems, forecast, intellectual property, procurement, receivables, inventory, and capacity.
To adapt this framework to the specific needs of the automotive industry.

For example, in the Chopra and Sodhi framework there was a risk category called delays. While this is a reasonable category in general terms to describe delays in material flows, it is insufficient to capture the necessary detail of risk elicited through our interviews with the auto manufacturer. Consequently, we expanded delays into logistics, supplier dependence, and quality.

Figure 1 shows the selected risk categories divided into internal (controllable) and external (uncontrollable) risks.

Figure 1: Supply Chain Risk Categories (Blackhurst et al., 2008)

Each of these categories are then weighed, so that the sum of all weights adds up to 100%. The subcategories also have to be weighed to show their relative importance within the risk category. In the example of the automotive manufacturer the focus was on quality and disruptions only, with 40 and 60 percent respectively.

Using the example of a disc brake assembly, the next step is to assess the risks of the individual parts of the supply chain product. In this case: caliper, hub and rotor. The results are shown in figure 2 and aggregated in a heat map (figure 3).

Figure 2: Risk Evaluation Sheet (Blackhurst et al., 2008; click to enlarge)

Figure 3: Heat Map for Product Parts (Blackhurst et al., 2008; click to enlarge)

The results can also be interpreted towards the individual suppliers (figure 4).

Figure 4: Heat Map for Suppliers (Blackhurst et al., 2008; click to enlarge)

Continuous process

The authors elaborate extensively on the requirements of a risk assessment and monitoring methodology:

Any risk assessment methodology should be able to develop a list of rank ordered critical parts (whether pre-specified or determined by the assessment tool); with critical risk parts at the top and low-risk parts at the bottom.

Furthermore the main purpose of an implementation of a risk monitoring is to switch from a reactive supply chain risk management to a continuous and proactive one.

To do this, risk ratings and/or risk indices must be tracked over time and trends monitored to determine if they are reaching unacceptable levels.

For this the risk scores developed above have to be updated regularly and can lead to a time dependent analysis shown in figure 6.

Figure 6: Trends in Supplier and Part Risks (Blackhurst et al., 2008)

Operational issues

The authors see four operational issues:

The number of categories and subcategories used becomes a balancing act. As more subcategories are added to a particular category, the relative impact each subcategory has on the score of the overall category declines. Similarly, as more categories are added to the risk assessment and monitoring tool, the relative weight each category contributes to the overall risk index of a supplier or part declines.

A higher weight on a particular factor will cause that factor to have more impact on the calculated risk index. As previously mentioned, the weights can be based on the probability of each category of disruption occurring, the relative impact each category of disruption has on supply, or any other factor considered important to the company.

Each subcategory must be rated. Some factors such as war and terrorism, political issues/unrest, information infrastructure breakdown, level of system integration, etc. are quite subjective and ratings on these factors should be made by higher level managers familiar with assessing these factors.

In order to use the methodology in a proactive manner, the ratings on each subcategory must be updated on a periodic basis and the data analyzed for patterns, high-risk levels, or trends that indicate potential problems. […] Therefore, the time interval between updates will also vary from subcategory to subcategory.

Conclusion

Overall the automobile manufacturer from the case study was reportedly very pleased with the resulting process and its relative ease of use and implementation: “it is too early to use overly sophisticated and brittle methods. We need an easily employable and understandable method such as this.”
I think the authors not only did a good job in summarizing the process, but also reflecting on its implications and shortcomings.

Reference:

Blackhurst, J., Scheibe, K., & Johnson, D. (2008). Supplier risk assessment and monitoring for the automotive industry International Journal of Physical Distribution & Logistics Management, 38 (2), 143-165 DOI: 10.1108/09600030810861215

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Predicting and Managing Supply Chain Risks

Daniel Dumke — Mon, 21 Nov 2011 15:40:00 +0000

I often recommend Supply Chain Risk: A Handbook of Assessment, Management, and Performance as a good overview of supply chain risk management literature. In this article I summarize chapter four of the book on the problem and processes to predict supply chain risks.

Structure and basics

In first section the author starts by defining the basics of risk and uncertainty. The next section briefly introduces different categories for supply chain risks:

Internal to the firm: Process, Control

External to the firm but Internal to the Supply network: Demand, Supply

External to the network: Environmental

These risk can then impact the different dimensions of a supply chain:

Physical movement of goods
Flow of information
Flow of money
Security of the firm’s internal information systems
Relationship between supply chain partners
Corporate social responsibility and the effect on a firm’s reputation.

The next section provides an overview on supply chain risk management, its steps and mitigation strategies.

Predicting Supply Chain Risks

One of the main requirements for an effective proactive risk management process is to obtain good estimates of the probability of the occurrence of any particular disruption and accurately measure the potential impact.

So the estimation of probabilities and impacts, depending on the characteristics of the supply chain and the environment it might be harder to identify all risks and assess them.

The author describes two tools for the estimation of risks: Data Mining and Failure Mode Effect Analysis (FMEA).

Data Mining

The role of data mining is to analyse historical data, to improve prediction capability. Some of the common analytic approaches used by data miners are:

Estimation of the parameters of past performance: Means, Standard deviations, Correlations, and Associations for hypothesis testing

Classification: Segmentation, or Clustering of data units to facilitate the modelling process

Construction of a functional relationship: or model between responses and explanatory variables.

The following tools should be used by a data miner:

Estimation: […] These tools will not lead to patterns but are more useful in analysing the data to identify the most relevant sets of data to concentrate further analysis.

Clustering/segmentation: This approach is used to logically group observations on the basis of similarity in their characteristics, reducing the level of heterogeneity in the data.

Classification/discrimination: The process of assigning observations to a predetermined number of classes.

Prediction: Formal mathematical models are built for the purpose of predicting the occurrence of the phenomenon.

Failure Mode Effect Analysis (FMEA)

The goal of the FMEA is to

Recognise and evaluate the potential failures of a product or process and the effects of that failure.

Identify actions, which could eliminate or reduce the chance of the potential failure occurring.

Document the entire process.

The author highlights ten steps of the process:

Define scope, functional requirements, design parameters and process steps.

Identify potential failure modes: Failure modes indicate the loss of at least one functional requirement. It is the manner in which a failure occurs. This step in the process takes into account a foresight view (based on past experience and any new information) of what could cause a failure to the system or process.

Potential failure effect: This step investigates the effect the failure will have on other entities or processes.

Severity: “How bad” or “serious” the effect of the failure mode is. Usually severity is rated on a discrete scale from 1 (no effect) to 10 (hazardous effect). Severity ratings of 9 and 10 indicate a potential effect of high importance and this could typically be a safety or government regulation issue. Critical effects need deeper study for all causes to the lowest level, using a method of Fault Tree Analysis.

Potential causes: These are the causes of the failure. In this step, all causes that can be attributed to the failure occurring are investigated.

Occurrence:This is the likelihood of the event happening (i.e. failure in the system) on the basis that “the cause occurs”. FMEA assumes that if the cause occurs, failure will occur too. The probability of occurrence is ranked from 1 to 10, where 1 signifies a remote probability of occurrence and 10 a very high probability of occurrence.

Current controls: The objective of the controls is to identify and detect the deficiencies and vulnerabilities as early as possible. This step looks at the current processes in place to mitigate the failures (if already known).

Detection: A subjective rating is assessed corresponding to the likelihood that the detection method will detect the first-level failure of a potential failure mode. This is ranked from 1 to 10, where 1 signifies that it is unlikely to detect and 10 signifies a very high detection potential.

Risk Priority Number (RPN): These are used to prioritise the potential failures and are calculated as “Severity Occurrence Detection ranking”.

Actions Recommended: The team should then select and manage subsequent actions needed to locate and control the situation.

Predictive – Proactive Methodology

Based on these methods the author suggests a predictive-proactive methodology (figure 1) to manage risks.
The two main steps are called predictive mode and proactive mode. In the predictive mode, the focus is on gathering of information on the potential risk sources. The proactive mode focusses on the identification of the risks within the supply chain and assessing the risk level and setting the strategic objectives of the company.

Figure 1: Schematic Diagram of the Predictive-Proactive Approach Supply Chain Risk Management Approach (Dani, 2006)

Conclusion

I read the chapter with a focus on the description of the prediction tools. The model on the other hand is a nice add-on which could be used as food for thought. But I find it a little bit too simplistic and a little bit confusing. It describes a basically linear process (from a risk to the disruption), but why then did the author choose not to represent this in a linear way, but in a diagram with two main parts/frames instead of a clear process and why are there two different types of frames (dashed and solid)?

Reference:

Dani, S. (2006). Predicting and Managing Supply Chain Risks Supply Chain Risk (Ed. Clare Brindley), 53-66 DOI: 10.1007/978-0-387-79934-6_4

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Supply Risk Management from a Small Company's Perspective

Daniel Dumke — Mon, 14 Nov 2011 15:53:00 +0000

This study takes a closer look at supply risk management, but this time from the perspective of several small companies. This article tries to answer the questions what risks small company owners (SCOs) see and how SCOs mitigate those risks.

Method

A case study with eleven small companies was conducted. The companies had between 1 and 12 employees. Overall 26 interviews were conducted (90 minutes on average). In addition, two seminars were held, to present the preliminary results and discuss the findings. The seminars were also used to generate additional data and validate existing results.

Informants rarely used terms such as supply risk, risk management, probability reduction, supply chain or even purchasing. [… So] Instead of forcing risk management terminology upon informants, possibly provoking misunderstandings and incomplete accounts, the idea was to let the SCOs provide rich accounts of their purchasing and SCM practices in layman’s terms. In the dialogue information was sought out that allowed plausible interpretations of supply risk management practices.

Figure 1 summarizes some facts about the companies.

Figure 1: Facts on the Case Study Companies (Ellegaard, 2008; click to enlarge)

Risk mitigation approaches

From literature the author develops three types of supply risk management / mitigation activities, namely: increased knowledge, reduced probability and reduced impact (figure 2).

Figure 2: Risk Management Alternatives (Ellegaard, 2008)

Knowledge

Knowledge increasing practices were infrequent among the studied SCOs. They spend limited time and resources actively acquiring knowledge about suppliers and supply markets and therefore possessed limited information […]. Only one SCO interacted with suppliers often.

Probability reduction

Probability reduction had the highest priority in these companies. Elimination of severe risks formed the backbone of SCO practices. […] In order to eliminate certain severe risks, the SCOs sourced locally, exhibited source loyalty, and were careful not to reveal proprietary product knowledge to suppliers.

Effect reduction

None of the SCOs held reserve capacity and practiced mainly single sourcing, with the aim of tying up as few resources as possible. The very limited reserves left the SCOs quite vulnerable to certain types of risks. Any specific events with the potential to obstruct delivery capabilities of suppliers, such as a fire, take-over, bankruptcy, up-stream material shortages etc. could bring SCO production to a halt.

All results are summarized in figure 3.

Figure 3: Supply Risk Mitigation Approaches (Ellegaard, 2008; click to enlarge)

Lessons learned

Despite the autodidact and informal purchasing practices of

the studied SCOs, there may be much to learn from these practices, even for state-of-the-art purchasing/SCM departments. The SCOs specialised in a few supply risk management practices that seemed to complement each other well. The study suggests that effective supply risk management is not only about adopting a wide range of sophisticated risk reduction practices. The challenge may be more connected to finding the right mix of practices, which fits the available resources and is sufficient to secure against supply risks. Supply chain managers should realise that:

different supply risk management practices require different levels of expertise and resources;

only a limited number of potential supply risk management practices may be necessary; and

supply risk management means adopting the exact mix of practices that provides security.

Conclusion

This study provides some missing insights into risk management practices of small enterprises. As the author, I do think that there are still things even larger companies can learn from their mitigation strategies: Prioritization and focus of risk management activities which deem to be most effective with the given resources may be one of those.

Reference:

Ellegaard, C. (2008). Supply risk management in a small company perspective Supply Chain Management: An International Journal, 13 (6), 425-434 DOI: 10.1108/13598540810905688

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Improving Supply Chain Performance and Managing Risk Under Weather-Related Demand Uncertainty

Daniel Dumke — Wed, 09 Nov 2011 16:18:00 +0000

The demand of many products is connected to the weather patterns during and before the selling season. Ice cream can be best sold during warm summers, of course. But also other food products or clothes exhibit weather dependent demand pattern.

This article by Chen and Yano (2010) has a look at improving contracting between a manufacturer of a product with weather dependent demand and its retailer. One is for sure: uncertain demand causes negative effects for the whole supply chain, and should be handled as such. The full paper can be downloaded here.

Method

The author utilize the classic news vendor setting with one manufacturer (M) and one retailer (R) to focus on the analysis of their relationship.

M is set as the focal company. Due to several reasons (e.g. long lead times, seasonal products) M wants to offer weather related rebates for the retailers.

Model

The sales process is modeled in three steps:

M designs the contract and offers it to R

R decides on the ordering quantity

Depending on the weather and the quantity ordered, payouts are made from M to R

A weather rebate is an alternative to other supply contracts that manufacturers might use to induce retailers not just to order greater […] quantities, but also to order them well in advance of the selling season. Such inducements fall into two broad categories: (1) early-season incentives that reduce the retailer’s financial obligation for any given purchase or commitment level and (2) end-of-season concessions paid by manufacturers when demand is weak.

In this case the authors only have a look at the second type.

Without loss of generality, the authors focus on the temperature as weather index and design the payoff depicted in figure 1, where t is the measured temperature, t* is the “strike”-temperature and K is the payoff.

Figure 1: Suggested Contract for Weather Rebate (Chen and Yano, 2010)

Results

The results show how the effects of the demand risks for the manufacturer can be mitigated in such a setting:

Choice of strike temperature (t*) and rebate function
t* has an huge effect on the risk sharing between the two parties. So in combination with adjusting the payoffs K, it is possible to cater to different degrees of risk aversion between the manufacturer and the retailer

Additionally risks can be hedged by using weather derivatives
Tapping the growing market of derivative products on weather allows to offset the risks of such a contract by buying a corresponding weather certificate.

Conclusion

The authors suggest a very flexible contracting scheme to optimize the distribution of risks between a manufacturer and retailer.

[The authors also see] very significant side benefits that the weather rebate offers (e.g., no auditing of leftover inventory at the retailer that would be required in the case of buy-back contracts or markdown allowances).

But, on the other hand the authors admit that the difficulty of the negotiation process might be increased since many more parameters (rebate, weather indices,…) have to be agreed upon.

Reference:

Chen, F.Y., & Yano, C.A. (2010). Improving Supply Chain Performance and Managing Risk Under Weather-Related Demand Uncertainty Management Science, 56 (8), 1380-1397 DOI: 10.1287/mnsc.1100.1194

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Supply Chain Flexibility in an uncertain Environment: exploratory Findings from Five Case Studies

Daniel Dumke — Mon, 07 Nov 2011 12:47:00 +0000

This is another look at flexibility strategies to reduce uncertainty in supply chains. This time from the point of view of five Chinese companies in the apparel industry.

Method

The authors use a case study to “illustrate and examine the different flexibility strategies adopted by supply chain participants as a result of different environmental uncertainties.”
The five companies cover different parts of the apparel supply chain. A garment and a fabric manufacturer (companies A and E, respectively), two trading companies (C and D) and a brand company (B). This diverse sample should enable the authors to make more general deductions from the generated data.
Figure 1 shows some relevant background information for the companies.

Figure 1: Case Study Companies (Yi et al., 2011; click to enlarge)

Results

The first questions revolved around the perceived uncertainties experienced by the participants. All companies mentioned that emergent orders, uncertainties about material cost and the aggressiveness of the competition are perceived as major uncertainties (figure 2).

Figure 2: Perceived Uncertainties by the Companies (Yi et al., 2011; click to enlarge)

Several different supply chain strategies were employed, a summary is displayed in figure 3.

Figure 3: Flexibility Strategies employed (Yi et al., 2011; click to enlarge)

The authors especially wanted to highlight the influence of different environmental aspects on the chosen strategies. No pattern emerged when analyzing which flexibility strategies are used for which uncertainties.
So next the authors examined the impact of the general level of uncertainty experienced by the companies and compared it with the supply chain flexibility level chosen.
The authors came up with a matrix (figure 4). Where they contrast SC uncertainty and flexibility. Companies D and B find themselves in the conservative corner while E, C and A employ a high level of flexibility to combat the uncertainties. From the interviews the authors also deducted the future positions of the companies A and B, which are indicated as A’ and B’.

Figure 4: Supply Chain Flexibility vs. Uncertainty (Yi et al., 2011)

Next the authors discuss their findings and reach four conclusions:

A low level of uncertainty in the supply chain leads to the adoption of a conservative flexibility strategy.
A high level of uncertainty in the supply chain leads to the adoption of an agile flexibility strategy.
An aggressive flexibility strategy can be proactively adopted through restructuring a firm’s operating system to reduce environmental uncertainties.
An aggressive flexibility strategy can be proactively adopted through creating new opportunities to improve a firm’s supply chain flexibility.

Conclusion

I like case study research, since usually you can gain some really genuine and new insights, in this case: What are other companies doing to contain uncertainty and how are they employing flexibility for that?

In this case I liked the paper a lot as well… up to the matrix. I don’t like matrices where the axes are not independent, because that’s not how you generate a matrix. Take the Growth-Share-Matrix, where you have the axis market growth and market share. The one is dependent on the market and the other on one company’s share in it. Relation to one another: Independent.
Here we have uncertainty on one axis and a strategy to reduce uncertainty on the other. So of course most companies would lie somewhere in the middle. So there is not much to learn from it. Of course there is not a one-to-one relationship, but anyway.

I had several more points to complain (number of interviews also only five, conclusions drawn are directly linked to their flawed matrix-framework and no quantitative measures to assess the flexibility and uncertainty levels).
But I still think the strategies mentioned from the interviews are still worth considering when choosing your own strategies.

Reference:

Yi, C.Y., Ngai, E.W.T., & Moon, K.-L. (2011). Supply chain flexibility in an uncertain environment: exploratory findings from five case studies Supply Chain Management: An International Journal, 16 (4), 271-283

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The Power of Flexibility for Mitigating Supply Chain Risks

Daniel Dumke — Wed, 26 Oct 2011 13:31:00 +0000

This article sheds light on the question of how much flexibility is necessary to secure the supply chain against disruption risks.
The paper reviewed today takes a closer look at three supply chain risks: supply, process and demand risks (figure 1).

Figure 1: Selected Risks for further Analysis (Tang and Tomlin, 2008)

Strategies

The authors focus on the short term agility to reduce risk and chose five strategies, which are analyzed in depth (figure 2).

Figure 2: Strategies for risk reduction using Flexibility (Tang and Tomlin, 2008)

The strategies are backed by several case studies each.
For all five strategies the authors build a different model. First stands the description of the scenario, second is the development of the model which fits the scenario and is able to deploy the strategy. The last step is the description of the results.

Supply risks

Flexibility via multiple suppliers
Multiple alternative suppliers can reduce the supply cost risks, since the company gains the flexibility to purchase from the company with the lowest prices.
The first model is based on a case by Intercon Japan and Li and Fung. One ordering company is confronted with supply cost risk. The price of the identical goods is random and can vary between $5 and $15. The authors calculate the profit for the single up to a five (optional) supplier case and conclude that profits rise with increasing number of suppliers, whereas the marginal increase declines (figure 3).

Figure 3: Profit vs. Number of Supplier to reduce Supply Cost Risks (Tang and Tomlin, 2008)

Flexibility via a flexible supply contract
Flexible supply contracts allow the ordering firm to adjust its predetermined order at short notice usually within a limited quantity range.
This model is based on a case with Canon, HP and Best Buy. Demand is random, the purchasing contracts between buyer and seller are negotiated in the beginning of a period. But the actual ordered quantity can be adjusted between u% less or more than the originally agreed quantity. Calculating the profits for increasing flexibility u% gives the results shown in figure 4.

Figure 4: Profit vs. Contract Flexibility (Tang and Tomlin, 2008)

Process risks

Flexibility via flexible manufacturing processes
Flexible manufacturing requires a plant to be adaptable to produce a multitude of different products.
In this model process flexibility is modeled by the number of products (h) which can be produced by one plant. Different forms are shown in figure 5.

Figure 5: Different Flexibility Systems (Tang and Tomlin, 2008)

The calculations shows that increased process flexibility can also increase profits in an uncertain scenario (figure 6).

Figure 6: Profit vs. Degree of Process Flexibility (Tang and Tomlin, 2008)

Demand risks

Flexibility via postponement
Further flexibility is gained by postponing product differentiation decisions until the latest possible point during the production process.
In this model two products are sold at a market, demand for them is uncertain. Postponing product differentiation (shown in figure 7). Calculating profits for two different random processes (Independent and Identically Distributed (IID) and Random Walk (RW) Model), shows that also increase in postponement leads to increasing profits (figure 8).

Figure 7: Scheme for Product Postponement (Tang and Tomlin, 2008)

Figure 8: Profit vs. Postponement Flexibility (Tang and Tomlin, 2008)

Flexibility via responsive pricing
Case studies show the value of price adaptability to steer customer demand.
In this model the company is able to postpone the pricing decision for the selling season until the time t. Up to this point in time the company is therefore able to observe demand. Timing flexibility increases as t increases. From this it can also be shown that increasing flexibility leads to better profits with uncertain demand. Figure 9 has the results.

Figure 9: Profits vs. Pricing Flexibility (Tang and Tomlin, 2008)

Conclusion

The authors took some effort to generate five different models to analyze the flexibility cases. Nonetheless, I would judge the models to be quite specific for the cases, which is good for the research, but also poses the question: Will these results hold true in any case? For any company and supply chain?
There are other results (e.g. by Tomlin und Wang, 2005, which indicate that flexibility might not be the right choice for risk averse companies, which might be better of by choosing a dedicated strategy instead)

But if you keep this caveat in mind this work is a strong proponent of flexibility as a tool to increase resilience in an uncertain environment.

Reference:

Tang, C., & Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks International Journal of Production Economics, 116 (1), 12-27 DOI: 10.1016/j.ijpe.2008.07.008

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Diversification under Yield Randomness in Inventory Models

Daniel Dumke — Mon, 03 Oct 2011 10:20:00 +0000

I haven’t really touched on the early research on risks in supply chain management. One major stream is on random yields. Parlar and Wang (1993) were one of the firsts to extend the classic Newsboy and EOQ (Economic Order Quantity) models to include uncertainty.

Models

Both models calculate the optimal inventory while including different cost factors (like setup cost for ordering, inventory holding cost) and decision variables (order volume and order timing).
As an extension the Newsboy problem also includes random demand.

Parlar and Wang now extended these models by a second raw material source and making the availability random of both sources. In this case the yield of the supplier is represented by a exponential distribution with two independent expected yield and standard deviation for both suppliers.

Results

An example of some numerical calculations done on the EOQ model is represented in figure 1.

Figure 1: Cost Function depending on the Quantities ordered (Parlar and Wang, 1993)

For both problems the authors find ways to calculate the optimal order decisions (figure 2, for the EOQ model). But they also admit that solving the newsboy problem required some extra effort and any addition to the model (like a third supplier) would make it even harder to solve.

Figure 2: Optimal Order Quantities for EOQ Model (Parlar and Wang, 1993)

Conclusion

These are operational models and they can and should be applied for operational decisions in inventory management and purchasing. But on the other hand they are also very abstract leaving many parameters out and (according to the authors) being hard to extend.

Reference:

Parlar, M., & Wang, D. (1993). Diversification under yield randomness in inventory models European Journal of Operational Research, 66 (1), 52-64 DOI: 10.1016/0377-2217(93)90205-2

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Managing Supply Chain Disruptions in the Chemical Industry

Daniel Dumke — Wed, 14 Sep 2011 17:19:00 +0000

This is already my second article (click here for the first) about managing supply chain risks in the chemical industry. This time by Paul R. Kleindorfer and Germaine H. Saad from Wharton and the Widener University. But this industry is quite interesting since it has to withstand a multitude of risks, so let’s get right to business:

Goal

As the title indicates: the authors focus on risks from disruptions to normal activities and not the omnipresent risk of coordinating supply and demand.
The authors aim to achieve three goals:

Development of a conceptual framework for risk assessment and mitigation
Analyze accident history from the six years between 1995 and 2000
Derive implications for the design of management systems to cope with supply chain disruptions

Figure 1: Risk Management Process in the Extended Supply Chain (click to enlarge; Kleindorfer and Saad, 2005)

Literature analysis

The authors use the SAM method, where the goal is first to analyze the risk S ources, then A ssess the risks and find the right M itigation strategies.
The authors first investigate the SAM process using literature analysis and conclude with the process depicted in figure 1 and a four step process to manage disruption risks:

“Obtain senior management understanding and approval, and set up organizational responsibilities for managing the Disruption Risk Management process.”
“Identify key processes that are likely to be affected by disruptions and characterize the facilities, assets, and human populations that may be affected.”
“Traditional risk management is then undertaken for each key process to identify vulnerabilities, triggers for these vulnerabilities, likelihood of occurrence, and mitigation and risk transfer activities.”
“Reporting, periodic auditing, management, and legal reviews of implementation plans and on-going results (e.g., of near-miss management and other disruption risks) complete the business process for disruption risk management.”

Case study chemical industry

The further analysis of disruption risk management is done using a case study approach. The authors used a database with access to accident activities in the chemical industry during the time between 1995 and 2000. Sources were natural hazards, equipment or human failure and sabotage.

The framework depicted in figure 2 was used as a basis for the analysis.

Figure 2: Research Framework for the Analysis of the Chemical Industry (Kleindorfer and Saad, 2005)

Results

Kleindorfer and Saad conclude that, “two key dimensions emerge as fundamental in guiding management practice of disruption risk in supply chains:”

The first dimension consists of strategies and actions aiming at reducing the frequency and severity of risks faced, at both the firm level and across the supply chain. The second element focuses on increasing the capacity of supply chain participants (whether a separate firm or a subsidiary facility) to sustain/absorb more risk, without serious negative impacts, or major operational disruptions.

Furthermore in the chemical industry special care has to be taken to include the stakeholders, like regulators or the employees in mitigation activities.

Lastly, the authors state two conditions which have to be met to ensure effective implementation of risk management measures:

Condition C1. The approaches used to mitigate disruption risks must “fit” the characteristics and needs of the underlying decision environment. Different supply chain environments will give rise to different approaches to assessment and mitigation. Even within a given sector such as mass retailers with global supply chains, the approaches taken by different companies will be different, given their differences in corporate culture and their methods of managing their supply chains. In the business of disruption risk management, one size will definitely not fit all.

Condition C2. Trust, information and continuing profitability are the basic glue that makes supply chain partnerships a reality. Continuous coordination, cooperation, and collaboration among supply chain partners are needed for risk avoidance, reduction, and mitigation such that the value and benefits generated are maximized and shared fairly. Different contractual and incentive schemes are now emerging as practical means toward this end. Linking risk assessment and risk management to identify vulnerabilities and opportunities for cost-effective mitigation is the first step in providing a rational basis for individual and group action among supply chain partners. Random investments or shots in the dark that do not properly account for the interdependencies across the supply chain will ultimately waste resources and destroy trust.

Conclusion

The authors already admit that the data used for their case study is based on a facility level. These facilities are of course part of the supply chain, but one can pose the question if the results are really relevant for supply chain risk management.

Moreover, one has to differentiate between supply chain security and supply chain disruptions. At least from my practical experience especially in the chemical industry facility security has played a major role since many decades and is deeply rooted in the companies themselves. Supply chain risk management on the other hand is a new approach and very much separated from it. So for a paper on exactly this topic I am missing a discussion of the question how these aspects should be integrated.

Reference:

Kleindorfer, P.R., & Saad, G.H. (2005). Managing Disruption Risks in Supply Chains Production and Operations Management, 14 (1), 53-68

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Improving Performance in Food Supply Chains by Reducing Uncertainty

Daniel Dumke — Mon, 12 Sep 2011 15:51:00 +0000

Today’s article is from the late 90s, but sets a great example for research methodology in supply chain risk management. But don’t worry, I will focus on the results, since they’re very interesting as well. The objective of today’s article (Supply Chain Management in Food Chains: Improving Performance by Reducing Uncertainty) is to show strategies (here called principles) to reduce uncertainty, and at the same time show the beneficial effects of reduced uncertainty.

Methods

Three methods were deployed to ensure the validity of the results. Based on a case study (1) of a chilled salads supply chain sources of uncertainty were generated and improvement principles designed. The analysis of the case study’s processes was also used to feed into a simulation model (2) of the supply chain, which was validated using (another) pilot study (3).
The approach is shown in figure 1 and is meant to ensure that the results presented below are worth reading.

Figure 1: Research Approach (van der Vorst et al., 1998)

Results case studies

The sources of uncertainty, which were uncovered during the process analysis in the first case study, can be found in figure 2.

Figure 2: Sources of Uncertainty and Improvement Strategies (click to enlarge; van der Vorst et al., 1998)

For the pilot study not all of the above mentioned improvement principles have been employed. The authors evaluated the following strategies (figure 3).

Figure 3: Improvement Strategies used at the Pilot Case Study (click to enlarge; van der Vorst et al., 1998)

By implementing them a very drastic decrease in inventory levels could be achieved (examples see figure 4). Overall results can be found in figure 5.

Figure 4: Inventory Level at the Distribution Center before and after implementing the Improvement Strategies (van der Vorst et al., 1998)

Figure 5: Results of the Pilot Study (click to enlarge; van der Vorst et al., 1998)

Results simulation

The results of the pilot study were then compared to the simulation model. Overall there seems to be a good fit between the model and the real data (figure 6).

Figure 6: Validation of the Simulation Model by comparing with Real World Data (click to enlarge; van der Vorst et al., 1998)

Further results were generated using the simulation model:

For fast moving goods optimization could be achieved by increasing the delivery frequency both to the retailer and to the DC by a certain amount. Even though the picking cost rose, inventory levels could be decreased significantly.
Implementation of a computer aided ordering system lead to an improvement of supply chain performance between 10 and 20%.

Conclusion

For the authors the simulation model is a logical extension of the prior case studies. Using it makes it possible also to test alternatives which in reality might be too expensive or even impossible to test.
Another key lesson: Reduction of uncertainties again is a major driver of supply chain performance and therefore should not be neglected.

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Supply Uncertainty and Chain-to-Chain Competition

Daniel Dumke — Mon, 15 Aug 2011 14:22:00 +0000

I just found the first supply chain risk related paper from a professor (Biying Shou) of one of my alma maters: The City University of Hong Kong, and I think this a good time to have a look at his work. The complete article of today can be downloaded from the SSRN.

Research Question and method

For several years now researchers predicted, that in the future supply chains compete, not companies. And there has been some research on the topic, but the larger focus is still very much on single supply chains or even only elements of the chain. The authors’ goal is to analyze this broader situation, in combination with supply uncertainty.
They therefore establish a mathematical model of two simple supply “chains” containing one customers, who is served by two supply chains consisting of a retailer and a supplier each (see figure 1).

Figure 1: Model of the two competing Supply Chains (Shou et al., 2009)

Results

You can have a look at the detailed results directly in the paper. Here I just highlight the major findings. The authors analyze the decision on three different levels:

Operational level: “At the operational level, we show that a retailer should order more (less) if its competing retailer has a less (more) reliable supply.”
Design level: The authors define two possible contracts between supplier and retailer and show that a wholesale price contract is inferior to a revenue sharing model, since the former cannot coordinate the supply chain (see figure 2). Coordination of the supply chain means that all participants of the supply chain actually participate at the supply chain; this would not be the case if e.g. the supplier has a negative profit.
Strategic level: The authors test different competition models to ensure coordination of the supply chain and find: “… that supply chain coordination is always a dominant strategy under supply uncertainty and chain-to-chain competition.”

Conclusion

I was first confused about the naming of the different levels of analysis, and I still think they should be renamed. What is the difference between design and strategic level? What decisions are included in the Operational Level?
But the authors do a great job of analyzing several aspects of this double supply chain, on the “operational” level at the retailer only, on the “design” level between the retailer and the supplier and on the “strategic” level between the chains.
This article is also a great source for some more insights into micro-economics and game theory.

Reference:

Shou, B., Huang, J., & Li, Z. (2009). Managing Supply Uncertainty under Chain-to-Chain Competition SSRN

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Pricing in Times of Disruption

Daniel Dumke — Wed, 10 Aug 2011 16:13:00 +0000

Many articles, including my own research show, that companies tend to focus largely on risk mitigation measures concerning the supply side. Only little is done to include demand side risks or demand side measures into the mitigation of supply chain risks. The study “Pricing During Disruptions: A Cause of the Reverse Bullwhip Effect” focusses on optimal pricing measures during a disruption. And so it helps to close the gap a little bit.

You can download a preprint of today’s paper at SSRN.

Reverse Bullwhip Effect

During a disruptions demand can change quickly. Due to a real or felt shortage customers are likely to order more than they actually need. This effect can be described by the Reverse Bullwhip Effect:

Whenever there is a perceived shortage of supply, it amplifies as it propagates down the supply chain. In fact, forward and reverse bullwhip effects often act as a system. If you start with a sudden upturn in demand, it gets amplified as it goes upstream, which creates a perceived shortage that amplifies as it propagates downstream. This creates a panic and downstream consumers overstate their demand, which amplifies again as it goes upstream. In turn, a greater scarcity is felt, and so on. Each of these effects feeds the other.

Method

The authors establish a mathematical model of a two tier supply chain, containing a single manufacturer (M) and a single (aggregated) demand (C). The process is established as follows:

Before the interaction, the manufacturer is hit by a disruption with a decreasing effect on capacity. The recovery takes place slowly over the course of several periods. The following steps are executed in each period:

M realizes its current capacity

M sets a price

C orders a specific number of products according to its predetermined behavior

The manufacturer has three price setting strategies at its disposal:

Naive: The price is set only based on the capacity and the long term demand curve of the customer, any short term behavior of the customer is ignored
One Period Correction: The deviation between the long term demand and the actual order of the customer is taken into account for the pricing decision.
Regression pricing: A regression analysis between set price and customer orders is included into the decision making process of the manufacturer.

The customer’s order behavior also includes historical prices. Two demand curves are specified, a long term demand curve, where the demand is linear in price, and a short run demand curve where the price change is included as well (figure 1).

Figure 1: Short Term Demand Behavior (Rong et al. 2009)

Results

The results show that the one period correction strategy results in a more volatile customer ordering process and lower revenues than both the naive and regression pricing strategies. Also in these terms the regression pricing strategy performs

worse than the naive strategy. The Reverse Bullwhip Effect is shown to occur in the disruption setting and almost always leads to reduced revenues.

Conclusion

From my point of view the study emphasizes four things:

All strategies involve active price changes by the company. Even though the naive strategy sounds very “lazy” it still involves using the available knowledge to optimize the profits of the manufacturer, including adapting prices.

The limited knowledge of the manufacturer is the major obstacle in the reduction of the Reverse Bullwhip Effect and improved revenues.

As for the Bullwhip Effect itself, the effects might be even worse with longer supply chains

An open question for me would also be how a more realistic (i.e. slower) price setting algorithm would affect the strategies.

Reference:

Rong, Y., Shen, Z.-J. M., & Snyder, L V. (2011). Pricing During Disruptions: A Cause of the Reverse Bullwhip Effect SSRN

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Simplified Supply Chain Flows

Daniel Dumke — Mon, 25 Jul 2011 14:52:00 +0000

I have to distract myself a little bit from thinking about supply chain risks too much, so I thought why not use the chance to read a different article once in a while. Well, at least the authors are familiar: Paul Childerhouse and Denis R. Towill and their paper of 2003: “Simplified material flow holds the key to supply chain integration”

Pre-work

Childerhouse and Towill start with an historical analysis of applications of simplified material flows, starting in the 16th century with the progress made in the supply chain for war ships over to the description of the bullwhip effect all the historic examples are concerned with the simplification of the supply chain, where the…

…emphasis is on “clean” i.e. unbiased and noise-free information flows; time compression of all work processes; achievement of consistent lead times; choice of smallest possible planning period; adherence to the schedule i.e. elimination of pockets of “Just-in-Case” materials, selection by simulation of the “best” supply chain controls; and finally, matching the simulation model to the real work process via process flow and information analyses

In previous papers the authors already analyzed how supply chain simplification is possible and conclude with a list of simplification rules (Figure 1).

Figure 1: Twelve Rules to simplify the Flows within a Supply Chain (click to enlarge; Childerhouse and Towill, 2003)

Figure 2 shows symptoms of a supply chain, when the material flows are too complex.

Figure 2: Symptoms of overly complex Supply Chains (click to enlarge; Childerhouse and Towill, 2003)

Method

A case study with 23 value streams all across Europe have been analyzed and a degree of simplification has been calculated using a score for the above mentioned simplification rules.

Results

The authors present five hypothesis to test using this data:

The effectiveness of a supply chain can be measured by assessing the level of uncertainty for the four segments of supply, process, demand and control.
Conformance to the 12 simplicity rules reduces uncertainty and results in increased supply chain integration.
On average each of the 12 simplicity rules are adhered to equally.
The level of uncertainty in the supply chain can be determined from a set of dynamic behaviour, organisational, situational and process observations.
Companies that undertake major Business Process Re-engineering programmes via the application of the 12 simplicity rules reduce uncertainties in their supply chains.

The hypothesis one and two are supported by the data of the sample. Hypothesis three is rejected, since the authors find that in the sample some of the simplicity rules are implemented more often then others (figure 3).
The fourth and fifth hypothesis are validated as well.

Figure 3: Order of the implementation of the Simplicity Rules within the Sample (Childerhouse and Towill, 2003)

The authors conclude:

Practitioners are faced with a multitude of published alternative ideas and techniques to improve the performance of their supply chains. It can be seen that most of these proposals have their roots in simplified material flow.

Conclusion

So, what do I want to remember from this detour:

Uncertainty is a driving force behind supply chain inefficiencies and risk reduction therefore also a way to improve performance.
Supply chain simplification is according to the authors one of the most common, yet often renamed, ways to reduce uncertainty and improve performance

And I thought I would get rid of risks for now…

Reference:

Childerhouse, P. (2003). Simplified material flow holds the key to supply chain integration Omega, 31 (1), 17-27 DOI: 10.1016/S0305-0483(02)00062-2

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