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Conceptual and Analytical Framework for SCRM

Daniel Dumke — Mon, 12 Mar 2012 18:18:16 +0000

A supply chain risk management framework should help to define the cornerstones of risk related supply chain problems and give hints on how to take actions to mitigate impending disruptions.

Today’s full paper has been published in 2004 and in it the authors (Gaonkar and Viswanadham) deal with this problem.

Core definitions

At the core of their framework the authors define the risk/supply chain related terms.

First, risks can be seen from an organizational-, supply chain- or industry-level.

Network- related risk sources represent the second category of risk sources, which are the primary focus of this paper. These risks are of two broad kinds:

Firms are vulnerable not only to attacks on their own assets, but also to attacks on their suppliers, customers, transportation providers, communication lines, and other elements in their eco-system.

Firms are also vulnerable to irregular behavior of their network partners such as a supplier sharing sensitive product design with a competitor manufacturer.

Second, there are different classes of risk problems:

Deviations: “A deviation is said to have occurred when one or more parameters, such as cost, demand, lead-time, etc., within the supply chain system stray from their expected or mean value, without any changes to the underlying supply chain structure.”
Disruptions: “A disruption occurs when the structure of the supply chain system is radically transformed, through the non-availability of certain production, warehousing and distribution facilities or transportation options due to unexpected events caused by human or natural factors.”
Disasters: “A disaster is defined as a temporary irrecoverable shut-down of the supply chain network due to unforeseen catastrophic system-wide disruptions.”

This classification also contains an implicit rating of the risk impact and while it is possible to create a robust supply chain which can withstand deviations and disruptions “it is impossible to design a supply chain network that is robust enough to react to disasters. This arises from the constraints of any system design, which is limited by its operational specification.”

The authors require the supply chains to be robust at three levels: the strategic, tactical and operational level. So each of these levels has to be prepared for deviations, disruptions and disasters.

For example, at the operational level, companies require decision support systems that can act on information from various partners regarding various deviations and disruptions to reschedule activities so that the business processes are synchronized and deliveries are undertaken within customer delivery windows and cost limitations. At the tactical level, plans need to have redundancies in terms of human and machine resources and also logistics and supply organizations. At the strategic level, more reliable partners with intrinsic capabilities in deviation and disruption handling, and the skills and ability to adapt to changing market conditions will be preferred and selected.

There are two distinct ways to supply chain risk mitigation:

“The first approach involves the time tested “just in case” way of maintaining inventories all along the chain, employing dual or multi-sourcing and manufacturing at multiple sites. This is a highly inefficient option.”
“A better option would be to first design a sourcing strategy taking into account the disruption costs for the most relevant failure modes and then putting in place contingency plans for each disruption that include both description of the procedures to follow and a definition of roles and responsibilities.”

The authors distinguish three analytical approaches to risk management:

Mathematical planning models,
Adaptive control, and
Rule-based control.
And they continue to explain their preventive and interceptive approaches to risk management. Some of which can be found summarized in this article in the blog.

Results

Based on their framework of supply chain risks the authors develop two strategic level (mathematical) models which include risk considerations.

Strategic-level Deviation Management Model: Given the expected costs and variability (deviation) of costs for all suppliers, the first problem relates to the selection of an optimal group of suppliers such that the expected cost of operating the entire supply chain and the risk of variations in total supply chain costs is minimized.

Strategic-level Disruption Management Model: Given the expected probabilities for various supplier disruption scenarios and the supply shortfalls under each of these scenarios the objective for the manufacturer is to choose a set of suppliers that minimize the expected shortfall during the operation of the supply chain.

With the models numerical example case studies are executed and the authors conclude:

Robustness is build into our supply chain design by selecting a portfolio of suppliers that minimize the variability of supply chain performance in terms of cost and output. The models we develop are preventive in nature and employ mathematical programming tools.

Conclusion

This article is a double publication, the models and their results have already been discussed here. Even though this is questionable behavior from a scientific point I did include this article due to the good and aggregated summary of the terms and definitions used in the field of supply chain risk management.
Using those as a minimum to analyze supply chains can already help a great deal in finding fitting mitigation strategies.

Reference:

Gaonkar, R., & Viswanadham, N. (2005). A Conceptual and Analytical Framework for the Management of Risk in Supply Chains Robotics and Automation, 3, 2699-2704

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Supply Chain Crisis and Disaster Pyramid

Daniel Dumke — Wed, 02 Nov 2011 15:43:00 +0000

With this review I take a look at supply chain disruption management. There is a huge number of actual and potential crisis (floods in Thailand, typhoons and risks of a greek collapse).

So it does make sense to think about preparation and recovery ahead of time.

Method

This article presents a new approach on how to improve preparedness and recovery after disaster strikes. It is built completely on a literature review of the resource, risk, crisis and disaster recovery, and preparedness literature. At its core this research is built on four different theories:

resource-based view of the firm

communication theory,

competing values theory, and

relationship management theory.

Figure 1: Four Corners of the Supply Chain Disaster and Crisis Pyramid (Richey, 2009)

Disaster and Crisis Pyramid

The four theories also form the pillars of the Disruption and Crisis Pyramid (DCP; figure 1).

Resource Management

The resources a company controls are an important factor in mitigating the effects of disasters. The authors summarize:

Still, given the prominence of resource management in the literature, and rightfully so, it seems logical that the DCP would “point” to resource management. Human resources must be moved from crisis locations to safe locations or from traditional operations to crisis locations. Physical and technological resources must be allocated, moved, opened, cleared, and customized. Informational resources must be collected, cleaned, warehoused, and redistributed. Financial resources must be acquired, allocated, distributed, and governed. The activity component in DCP research is huge and seems to thus be the consistent under current in the literature.

Collaboration: relationship management theory

The second element of the pyramid is the collaboration between the stakeholders in a disaster situation.

In crisis situations, collaboration will likely be the glue that holds organizations together. Additionally, Stewart et al. (2009) point out the broadness of the web that forms the extended supply chain in disaster and crisis situations. There is little doubt that such a tangled web will provide a huge opportunity for not only new research, but also to challenging the existing assumptions of relationship management logic.

Communication: communication theory

To achieve coordination and collaboration between all stakeholders, established and continued communication methods are essential.

Supply chain disaster and crisis situations demand effective communication. […] Examining issues such as bi-directionality, formality, modality, and frequency could be of serious importance as public and private entities attempt to understand what, when, and how much to communicate.

Contingency planning: competing values theory

A supply chain brings together a multitude of different companies. And usually instead of having on clear cut strategic goal, each company works for its own goals which also often are not properly aligned.

Competing values theory provides grounding for future supply chain disaster and crisis research by suggesting that firms simultaneously pursue various different and often conflicting strategic goals regardless of the situation

Conclusion

All in all Richey’s framework is aimed at providing a guideline for future researchers to find new insights into supply chain disaster management and how to improve supply chain reactions at the intersection of communication, collaboration, resources and values. And these four aspects should not only be considered by researchers, but also by supply chain professionals.

I especially liked the inclusion of the competing value theory, which might lead to a shift in research from the currently leading paradigm that goals of supply chain partners are always well aligned.

On the other hand, this framework could also be used beyond only disaster and crisis management, the aspects could perhaps prove influential in a larger number of supply chain related research fields and applications.

Reference:

Richey, R.G. Jr (2009). The supply chain crisis and disaster pyramid: A theoretical framework for understanding preparedness and recovery International Journal of Physical Distribution & Logistics Management, 39 (7), 619-628 DOI: 10.1108/09600030910996288

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Assessing Vulnerability of a Supply Chain

Daniel Dumke — Mon, 10 Oct 2011 14:42:00 +0000

At this year’s HICL conference in Hamburg, I was able to present some of my own research. In the follow-up discussions several points were highlighted, especially focussing on the viability of supply chain wide cooperation and collaboration efforts and on the difficulties of doing a realistic quantification of supply chain risks.

I already read a great paper on this topic some time ago: “Assessing the vulnerability of supply chains using graph theory” by Stephan M. Wagner and Nikrouz Neshat (2010), which I present you today.

Disruptions and vulnerabilities

Several authors argue that several factors help increase the vulnerabilities of today’s supply chains. When supply chain complexity increases (e.g. supply chain length, higher division of labor, …), the vulnerabilities also rise. Furthermore there is evidence that natural and man-made disasters are on the rise as well (figure 1).

Figure 1: History of Disasters (Wagner and Neshat, 2010)

Figure 2 shows the links between supply chain vulnerability drivers and disruptions.
So, since natural- and man-made-disaster most often cannot be influenced directly, the authors argue that the focus has to be on reducing the vulnerabilities themselves.

Figure 2: Connections between Vulnerabilities, Disruptions and Risks (Wagner and Neshat, 2010)

Assessment of vulnerabilities

Wagner and Neshat suggest a four step algorithm based on graph theory, which is used to calculate a Supply Chain Vulnerability Index (SCVI). The algorithm is based on graph theory (which has been part of another study here). Key to the understanding of the SCVI is the risk driver mentioned above. Figure 3 shows an abstract example with three vulnerability drivers (D1 to D3) and their links.

Figure 3: Vulnerability Drivers and their Links with the corresponding Matrix (Wagner and Neshat, 2010)

To calculate the SCVI the supply chain risk manager has to create a map / graph of the current risk drivers (step 1), find the corresponding relations between the risk drivers (step 2), calculate the “influence matrix” (step 3) and deduce the SCVI. As a forth step SCVIs of different companies can be compared to gain further insights for optimization and risk reduction.

Figure 4 shows the main drivers of supply chain vulnerability.

Figure 4: Selection of Vulnerability Drivers in Structure, Demand and Supply (Wagner and Neshat, 2010)

Using graph theory makes it possible for the risk manager to get an overview of the influencing factors of supply chain risks. It also enables him to act on this knowledge. Figure 5 shows a possible simplification / vulnerability reduction.

Figure 5: Reduction of Supply Chain Vulnerability Drivers leads to a Simplification of the respective Graphs (Wagner and Neshat, 2010)

D3 in figure 5 “is called a ‘sink’ – which means that D3 can only be influenced by three other drivers and cannot influence others. Considering the graph nodes and edges, supply chain managers can apply risk management methods and implement mitigation strategies to omit or alleviate some of the vulnerability drivers. Figure 5 shows the graph after D3 has been omitted. As one can see, the resulting graph contains less vulnerability than it did prior to implementing the measures.”

Empirical data for the SCVI

The authors also conduct a survey with over 700 participants from different industries. Based on their feedback the authors assess the Supply Chain Vulnerability Index for eight different industries. The results are summarized in figure 5. And give support to the hypothesis that the automotive industry has one of the highest risk levels.

Figure 6: Supply Chain Vulnerability within several Industries compared (Wagner and Neshat, 2010)

Conclusion

I really like the graph approach to assessing supply chain vulnerabilities. And I think it is a great method to support the understanding of a complex system like the supply chain. The article combines two very interesting aspects of it: the practical implementation and the assessment of supply chain vulnerability and a survey to compare different vulnerability levels across industries. The complete article, where you find more details on the survey results and the algorithm for calculating the index, can be downloaded here.
From a business and research point of view this article should direct the supply chain risk management efforts especially in the industries with the highest risk levels, Automotive and ICT.

Reference:

Wagner, S.M., & Neshat, N. (2010). Assessing the vulnerability of supply chains using graph theory International Journal of Production Economics, 126, 121-129

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Evaluation of a Firm's Supply Chain Strategy

Daniel Dumke — Wed, 05 Oct 2011 12:10:00 +0000

Today’s paper is brand new and based on the dissertation works of Roberto Perez-Franco. It can be considered as a summary of the current state of the art in supply chain strategy and extends knowledge in the field of strategy evaluation. It can be downloaded for example from Yossi Sheffi’s homepage at the MIT.

Foundation

The authors founded the following findings on two action research project with Saflex and a health care company. Several dozen interviews were conducted on different levels within the companies.

For the further research the authors define the major terms used: supply chain strategy and the difference between the supply chain and the supply chain strategy:

For the purpose of this paper, the supply chain strategy of a firm is understood as the set of ideas behind the activities, decisions and choices of that firm‟s supply chain, which serve as logical bridge between the supply chain operations in the field and the business strategy.

The difference between a supply chain and a supply chain strategy is the difference between a set of physical entities and a set of ideas. So, for example, when AMR Research publishes its “Supply Chain Top 25” list, what they are ranking are supply chains, not supply chain strategies.

Functional Strategy Map (FSM)

The authors introduce a Functional Strategy Map which “is a conceptual representation of the supply chain strategy as a bridge between operations and business strategy”. Three layers are aggregated in the FSM: strategic, functional and operational (figure 1).

Figure 1: Elements of a Functional Strategy Map (Perez-Franco et al., 2011)

A company’s supply chain is then evaluated based on the this concept using the criteria mentioned below.

A example of the FSM might look like that from Saflex in figure 2.

Figure 2: Example of a Functional Strategy Map (Perez-Franco et al., 2011; click to enlarge)

Evaluation criteria

The evaluation criteria were agreed upon before the first action research, but were adapted due to the new findings from the first and second action research.

The final suggested criteria are:

“Support: activities should support the goals of the supply chain strategy”

“Consistency: components of the supply chain strategy should be compatible”

“Coverage: the supply chain strategy should address all important areas”

“Sufficiency: the goals should be fully satisfied by the supply chain strategy”

Figure 3 illustrates the criteria.

Figure 3: Evaluation Criteria (Perez-Franco et al., 2011)

(1) Functional support: the functional themes are expected to support the strategic themes. (3) Strategic consistency: the strategic themes are expected to be compatible among themselves. (7) Functional coverage: the functional themes are expected to address all the areas of interest to the functions. (10) Functional sufficiency: The functional themes should be satisfied by the operational themes.

The authors also defined methods to evaluate these criteria within a company. I highlight the results of the consistency check here and refer you to the full paper if you are interested in more about the methods used here.

Figure 4: Incompatibility Matrix for the Functional Layer (Perez-Franco et al., 2011; click to enlarge)

Figure 4 shows the incompatibility matrix for the second company. Each axis contains the functional goals of the company and the percentages indicate the incompatibility between the ideas as given by the interviewees (lower means more compatible).

Thus this graph gives an good overview of strategic incompatibilities and clashes.

Conclusion

Thats already it. Of course the other criteria have to be evaluated as well and the authors have several of their questionnaires in the appendix.

The topic indeed is cutting edge, since there is not yet any literature on supply chain strategy evaluation.

Maybe the authors also already gave the reason for this lack of literature: Several studies already found that only less than 50% of the companies actually have a supply chain strategy.

But using this concept and these methods is a great way to make the supply chain strategy explicit and documented and evaluate it in the process as well!

Reference:

Perez-Franco, R., Singh, M., & Sheffi, Y. (2011). An approach to evaluate a firm’s supply chain strategy as a conceptual system International Journal of Production Economics

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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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Knowledge-based Supply Chain Risk Management

Daniel Dumke — Mon, 19 Sep 2011 11:26:00 +0000

I just recently had a discussion with a professor at Mahidol University (Bangkok), where I was staying for a research exchange, on how knowledge can be used to mitigate risks within a supply chain. If you search here on the blog for “knowledge” there are already several articles covering parts of how knowledge can be used as a strategy (Internet Induced Risks for global Supply Chains) or the benefits of information sharing (Information Sharing in Supply Chains).

But there is no review on a common framework which contains all / many aspects of the use of knowledge in SCRM. So I looked in my personal library and found the following article, which presents a comprehensive framework for SCRM based on knowledge.

Key aspects in knowledge based SCRM

The author sees several key elements to improve SCRM by using available knowledge within the chain.

Usage and sharing of the knowledge about supply chain risks: In supply chain risk management it is necessary not only to know about the company’s own risks but also include the knowledge about the risks of other supply chain participants
This knowledge sharing should be supported by information technology.
A dynamic knowledge alliance should build the organizational foundation, members are dynamically selected due to their importance in the chain
Positive effects of the knowledge based SCRM do not come instantly so, long term cooperation will be needed

Framework

The author developed the following framework (figure 1), which contains every aspect form identification and assessment of the sources of risk knowledge, the knowledge generation, storage and application.

Figure 1: Knowledge Based Supply Chain Risk Management (Guo, 2011)

The author recommends to base the implementation on a) the accumulation of risk knowledge, emphasizing the importance of building the database and filling it with relevant data and furthermore b) developing a dynamic knowledge alliance. Primarily this alliance also signifies an organizational shift towards a more open exchange between companies, for this processes for risk gathering, storage, etc. have to be standardized across companies.

Conclusion

I present this framework as food for thought primarily from a business perspective. I really liked some of the ideas and conclusions, nonetheless I have to point out some scientific flaws that make me wonder how the paper could be published in a journal.

The paper is much too short; up to now I was under the impression that I had a very brief writing style, but 6 pages are just not even enough to define all the different terms the author used.
The shortness is also reflected in the missing arguments, there is no discussion of either current literature nor other results
The author did not mention any method which he used as a foundation for his findings. So I have to assume that he just thought it up by himself. This actually does count as scientific evidence as well, but it’s very low on the validity scale. This is also the reason why I still would suggest it as a discussion base for a business setting, but I would definitely not start redesigning my organization according to the views mentioned in the paper
There is no discussion of the terms knowledge versus information and as far as I understand these terms he might actually be talking about information management here.
I was happy to see some hints on the implementation, nonetheless there is also no discussion of how the tacit knowledge can be included into this information sharing framework.

But still, I liked the idea of the dynamic knowledge sharing organization, as a virtual frame where companies share information on risk management. Because that’s for sure, not many companies are sharing enough information on the supply chain risks they see.

The full article can be downloaded here.

Reference:

Guo, Y. (2011). Research on Knowledge-Oriented Supply Chain Risk Management System Model Journal of Management and Strategy, 2 (2), 72-77

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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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Resilient and Sustainable Systems

Daniel Dumke — Mon, 08 Aug 2011 14:01:00 +0000

In today’s post I would like to highlight how the concepts of resilience and sustainability can be aligned.
Resilience is often interpreted as a property of a supply chain most often associated in a risk management sense; sustainability on the other hand, usually refers more to social and environmental goal orientation. So how can these concepts be aligned?

You can download a copy of the full article by Joseph Fiksel Designing Resilient, Sustainable Systems.

Sustainability

Nowadays shareholders show more and more interest in the ecological and social footprint of their company, therefore companies aspire to become more sustainable.

Fiksel defines sustainability as:

A product, process, or service contributes to sustainability if it constrains environmental resource consumption and waste generation to an acceptable level, supports the satisfaction of important human needs, and provides enduring economic value to the business enterprise.

Sustainability therefore is not an objective (which could be put into a mathematical model) but a property of a system.
For a supply chain system the analysis starts at the process level up to the an economy and ecosystem (see figure 1).

Figure 1: Levels of System Design (Fiksel, 2003)

Sustainable companies

The author points out a study which has been conducted by Royal Dutch Shell in the 90s to find properties of long-lived companies. They unveiled the following factors:

sensitivity and adaptability to the business environment
cohesion and sense of identity
tolerance of diversity (decentralization)
conservative use of capital

Interestingly, profitability was seen rather as an outcome of these factors than a good prediction for long life. The study concludes that learning, growing and long term survival should be the goal; and these objectives cannot be reached by simply maximizing profits.

Resilience vs. sustainability

First, resilience can be best described using the diagram in figure 2.

A resistant system returns to its desired / stable state very fast, however if the system state is moved too far of the original state (in the diagram too far left or right) it may collapse and not be able to return.
A resilient system has a broader range of possible states, and therefore can survive larger disruptions.
The last system is an extension of the resilient system, and able to withstand even larger changes in the environment, maybe changing its equilibrium point.

Figure 2: Characterizing System Types (Fiksel, 2003)

Resilience in systems design

Fiksel suggests four system characteristics which can be / are used on different system levels to increase resilience of that system:

Diversity, existence of multiple forms and behaviors;
Efficiency, performance with modest resource consumption;
Adaptability, flexibility to change in response to new pressures;
Cohesion, existence of unifying forces or linkages.

Figure 3 contains supporting examples.

Figure 3: Properties of Resilient Systems (click to enlarge; Fiksel, 2003)

Conclusion

The author introduces a system theory framework, where sustainability and resilience are properties of the system. From the title and introduction I was expecting a more systematic approach in comparing these two concepts and defining their relationship.
There is one direct quote (“Perhaps the essence of sustainability is resilience, the ability to resist disorder.”) indicating that resilience might be the more general term, but it is not confirmed.

There are of course some overlaps mentioned, e.g. efficiency as a mean to improve resilience, which works towards a sustainable system. But there is no direct comparison between the two.

The author also introduces a process to design sustainable systems. If interested please download the paper and have a look at page 5335.

Reference:

Fiksel, J. (2003). Designing Resilient, Sustainable Systems Environmental Science & Technology, 37 (23), 5330-5339 DOI: 10.1021/es0344819

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Supply Chain Resilience: Development of a Conceptual Framework, an Assessment Tool and an Implementation Process

Daniel Dumke — Wed, 01 Jun 2011 12:54:00 +0000

This is the sixth 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 Timothy J. Pettit and published as his doctoral thesis at The Ohio State University, Columbus, USA in 2008. It can be downloaded here.
I have already seen his presentation of the results in 2010 at the CSCMP conference in San Diego together with Joseph Fiskel. His current employer is still the US Airforce, now as Assistant Professor of Logistics and Supply Chain Management.

The title of his thesis is:
Supply Chain Resilience: Development of a Conceptual Framework, an Assessment Tool and an Implementation Process

Summary

In his dissertation Pettit develops a framework and processes to improve implementation of resilience into supply chains. His thesis is devided into three parts:

Development of a supply chain resilience framework, based on literature and focus group workshops. The building blocks “vulnerabilities” and “capabilities” emerge as main dimension (see figures below).
Building on this framework Pettit creates an assessment tool (SCRAM) and tests the tool with seven global manufacturing supply chains, by evaluating their current state.
In this part critical links between vulnerability and controllable capabilities are identified and analyzed.

I will not quote the whole dissertation here. But I want to show two graphics from his work highlighting the relationship between capabilities, vulnerabilities and resilience.
Figure 1 shows the first proposition and basically the operationalized definition of resilience: When vulnerabilities decrease and capabilities increase resilience increases.

Figure 1: Measuring Resilience using Capabilities and Vulnerabilities (Pettit, 2008)

Since capabilities are usually costly and vulnerabilities often fixed, figure 2 suggest that there is an optimum balance of capabilities and vulnerabilities in the Zone of Balanced Resilience.

Figure 2: Zone of Balanced Resilience (Pettit, 2008)

Finally, Figure 3 finally presents the combined framework.

Figure 3: Supply Chain Resilience Framework (click to enlarge; Pettit, 2008)

Conclusion

As with Tomlin’s thesis the dissertation is split into three separate parts which are cumulated into the thesis.

Resilience is a necessity if you agree that some risks are just not forecast-able and hedge-able by traditional means. Overall Pettit designed a great way to assess resilience, and this is definitely not just an academic discussion, for businesses it is equally important to measure resilience to, a) be up to date about improvement activities and b) being able to get these activities started in the first place by showing improvement potential:

Although further validation is required, managers should be encouraged to make the minimal investment required to determine their current state of resilience and compare their strategy with the resilience fitness space.

Reference:

Pettit, T.J. (2008). Supply Chain Resilience: Development of a Conceptual Framework, an Assessment Tool and an Implementation Process The Ohio State University, Dissertation

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Agile Supply Chains and Uncertainty

Daniel Dumke — Wed, 27 Apr 2011 12:49:00 +0000

There are many definitions of agility. A supply chain can be defined as agile, when it is flexible and responds quickly to customer needs. Agility can also be seen as a measure to mitigate supply chain risks, building on this thought Dani and Ranganathan (2008) developed a model to mitigate risks using the concept of agility .

Concept for Agile Risk Mitigation

The authors develop the model based on two premises: (1) scenario planning is used to identify supply chain risks and (2) the system is designed to react fast and flexible to mitigate the risk.
Figure 1 shows the resulting concept.

Figure 1: Concept for agile Supply Chain Risk Mitigation (Dani and Ranganathan, 2008)

Two general mitigation “arms” are necessary. One for the foreseen risks which can be mitigated proactively and unforeseen risks which have to be reduced reactively.

Model Validation

This model constitutes a theory build by the authors on recent literature and own experience. To raise the credibility a theory has to be validated. In this case the authors decide to use the Ericsson case (if you are interested, have a look at my review of the case here).
After a breakdown of a Philips semiconductor plant in 2000, Nokia and Ericsson were competing over the remaining capacity. Nokia reacted fast and acquired spare capacity by Philips and other suppliers. Ericsson reacted more slowly and failed to obtain the necessary component and lost about 400 million USD.

When applying the above mentioned model, it is clear that both companies had not thought about this scenario, however Nokia already beforehand had established a fast communication structure which permitted it to react in a fast manner.

Conclusion

The article also contains a extended literature section defining the terms used, building from uncertainty and risk, to risks in supply chains, scenario analysis and the agility concept. Building on that the concept presented on less then one page seems to be quite underweighted. The main statement of the article is that: (1) planning of future scenarios is important to anticipate and mitigate possible risks, (2) depending on the planning mitigation of disruptions happening can be done proactively or reactively. (3) Especially for reactive mitigation the agility of the company plays a major role in the success.

Reference:

Dani, S., & Ranganathan, R. (2008). Agility and supply chain uncertainty: a scenario planning perspective International Journal of Agile Systems and Management, 3 ¾, 178-191

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Measuring agile Capabilities in the Supply Chain

Daniel Dumke — Wed, 13 Apr 2011 12:22:00 +0000

Today I introduce you to the process of measuring agility in a supply chain. Agility is a major concept in the research of the last 10 years or so. I already have written some articles on this topic:

Consequently, the goal of today’s article is to expand on those and define what are the key factors of SC agility and how it could be measured.

Method

The paper presented is based on two sources: an empirical investigation of agile capabilities in Europe and a literature review of the current research available.

The concept developed is based on the literature review and it is then tested using the survey.

Agility Framework

Agility has already been described early in four basic dimensions (according to Goldman et al.,1995): 1) Enriching the customer, 2) cooperating to enhance competitiveness, 3) organizing to master change and uncertainty, 4) leveraging the impact of people and information

An extensive literature review leads to the agility framework shown in figure 1. The dimensions are described below (quoted).

Figure 1: Presentation of the Agility Framework (van Hoek et al., 2001)

Customer sensitivity
Customer centred versus product centred logistics policies (ten questions): assumes that “agile” policies emphasize customers and markets, while “lean” policies focus on the elimination of waste in products and processes.
Virtual integration
Immediate conversion of demand information into new products using knowledge-based methods versus multi-stage, multi-function methods (three questions): assumes that agile policies focus on instantaneous demand capture, interpretation and response while lean policies emphasize stable production periods and protecting the “operations core”.
Process integration
Self management versus work standardization (five questions): assumes that agile policies focus on operator self- management to maximize autonomy and immediate response, while lean policies emphasize work standardization to ensure conformance to quality and productivity standards.
Network integration
Fluid clusters v. long term supply chain partnerships (six questions): assumes that “agile” policies emphasize fluid clusters of network associates, while lean policies focus on a more fixed set of long-term stable partnerships.
Measurement
Capabilities versus “world class” measures of performance (seven questions): assumes that agile policies are based on broad-based measures that underpin capabilities, while lean policies emphasize “hard” measures such as quality and productivity only.

The authors go further and analyze how these dimensions are represented other common supply chain concepts like just in time or lean production (figure 2).

Figure 2: Representation of Agile Dimensions in common SC Concepts (van Hoek et al., 2001)

Changed Approach

The “agile approach” changes supply chain management as a whole. The authors compare the traditional vs. agile approach very extensively. A summary can be found in figure 3.

Figure 3: Approaches to Agility in Supply Chain Management (click to enlarge; van Hoek et al., 2001)

Conclusion

Overall this article is a good starting point to answer the question “what is agility?”. For business it is important to transfer this framework into their measurement systems to get a benchmark how well they perform, since “you cannot manage what you don’t measure”.

Reference:

Hoek, R., Harrison, A., & Christopher, M. (2001). Measuring agile capabilities in the supply chain International Journal of Operations & Production Management, 21 ½, 126-148 DOI: 10.1108/01443570110358495

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Improving Confidence to mitigate Supply Chain Risks

Daniel Dumke — Mon, 04 Apr 2011 14:28:00 +0000

Yet another paper on risk mitigation strategies. This time: How to reduce risks by improving confidence. You can find the complete paper of today in the web.

Lack of Confidence

The authors theorize that especially demand risk (volatility) has increased during the last years. With several SC concepts which are aimed to reduce the slack in the supply chain during the same period, overall risks in the SC have increased dramatically.

This leads to a lack of confidence by the managers responsible for the SC in:

Order cycle time

Current order status

Demand forecasts given

Supplier’s capability to deliver

Manufacturing capabilities

Quality of the products

Transportation reliability

Services delivered

If you are still unsure if in your supply chain the lack of confidence prevails, have a look at this list.

Outcomes of the Lack of Confidence (Christopher and Lee, 2004)

Risk Spiral

The lack of confidence can help to increase risks even further. Since according to the risk spiral, lack of confidence may lead to a increase of buffers in the supply chain to make sure that the product can still be delivered.

This on the other hand leads to longer cycle times / pipelines, which reduces the visibility even further.

Risk Spiral (Christopher and Lee, 2004)

Solution

Instead of increasing buffers the authors recommend to improve visibility and control:

Visibility

The key to improved supply chain visibility is shared information among supply chain members. Traditionally companies have tended to subscribe to the view that ‘information is power’ and to interpret the phrase as meaning power is diminished if that information is shared. In fact in supply chains the reverse is true. If information between supply chain members is shared, its power increases significantly. This is because shared information reduces uncertainty and thus reduces the need for safety stock.

Control
The goal here is to improve flexibility over the supply and own processes. The time to react to the new information gained above has to be used well.

Conclusion

Visibility and control summarizes several existing strategies (like information exchange and reducing lead times) into a new concept for thinking about supply chain risk mitigation. It therefore has the potential to lead to new / better strategies to reduce risks. The table above can be used to analyzed if some supply chain is already affected by low confidence. From a scientific standpoint I am missing the grounds for this conclusions. Obviously, this paper is not proving / supporting any theory, since it does not contain any survey or other modeling method. But on the other hand it also does not contain any hint for the method used for theory creation.

Reference:

Christopher, M., & Lee, H. (2004). Mitigating supply chain risk through improved confidence International Journal of Physical Distribution & Logistics Management, 34 (5), 388-396 DOI: 10.1108/09600030410545436

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Managing Strategic Partnerships

Daniel Dumke — Wed, 09 Mar 2011 06:57:00 +0000

Setting up the right partnerships is hard to do. There are multiple levels to the decision with which companies to setup formal relations and how deep this relationship should be? Christopher and Jüttner (2000) develop a framework for managing partnerships. The full paper can be found here.

Introduction

The authors use focus group interviews and multiple case studies to gain insights into practitioners’ experiences.
Several factors are affecting an increasing focus on partnership management: The value chain orientation, rationalization of the supplier base, growing interdependencies and growing focus on the competition of supply chains and not companies.

Framework for Strategic Partnerships (Christopher and Jüttner, 2000)

Framework

The key factors of the framework are: to define a balanced set of relationships, development of an interface structure, cooperation, change management, relationship promoters and monitoring.

Defining a balanced set of relationships
Keep in mind that partnerships are resource-intensive investments, first a company has to define its own strategy. Since the complexity of the network is dependent on number of levels, the existing relationships have to be evaluated: Can I substitute suppliers, buyers, goods? What are our common interest and what are my transaction cost? Christoper and Jüttner suggest, that this should not be done by multidimensional evaluation matrices, but a simple hierarchy to rank the suppliers / buyers may be sufficient. To generate some incentives for new comers this ranking and the generation process should be made transparent.
Developing the right interface structure
The authors argue that the quality of relationship is influenced by the closeness of the parties. There are distinctive types of interfaces which can be used for such a relationship: 1) only buyer / seller interaction (focus on price and margin, few resources invested, large number of relationships); 2) closer collaboration between buying and selling (“account management”); 3) relationship between companies, where there is direct interaction between a range of functions and usually substantial investment in the relationship
Cooperation
Here aspects like information sharing or data accuracy come into play. Also the sensitive parts of partnership are important
Change management
The authors want to point out that the change towards a company which embraces strategic relationships is not an easy one. Therefore managing people through this change is an essential part of partnership management.
Assigning a relationship promoter
To prevent relationship issues a relationship promoter on each side of the partnership is advised.
Monitoring the relationships
Monitoring can be done in a formal and / or informal way. The important point here is not to loose track of what has been achieved and how it was achieved.

Conclusion

Christopher and Jüttner provide a refreshing read on strategic partnership management. Refreshing, because they are not focussed on only one possible way eg. to measure the performance of the chain. They acknowledge that depending on the context a more informal way of measuring might be appropriate as well.

Reference:

Christopher, M., & Jüttner, U. (2000). Developing strategic partnerships in the supply chain: a practitioner perspective European Journal of Purchasing & Supply Management, 6 (2), 117-127 DOI: 10.1016/S0969-7012(99)00038-6

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Strategic Supply Chain Design and the Product-Relationship Matrix

Daniel Dumke — Mon, 14 Feb 2011 14:06:00 +0000

Yet another case study. This time on strategic network design. Why? A major problem for businesses is to find the right strategy for a given situation. And “right” here means optimal (for a given KPI).
Research question might be: Are there general categories which can be used to make different decision situations comparable? And if yes, what would optimal strategies under these circumstances be?

Definitions

Seuring (2009) tries to answer the first research question using case studies. From literature he defines the strategic decision fields to be

Product design/engineering (products or parts),
Plant and equipment (plant),
Organisation and management (processes),
Labour and staffing (people) and
Production planning and control (planning).

For Seuring the SC decisions best can be categorized using a two dimensional matrix with the supply chain at the y-axis and the product dimension on the x-axis. As you can see in figure 1 he does not focus on the supply / demand side (like eg. Fisher 1997), but in both dimension aims at the process view.
The supply chain dimension with can be divided into configuration and operation, the product dimension into the product design, production and return phase.

Figure 1: The Product-Relationship-Matrix in Supply Chain Management (Seuring, 2009)

Advantages

Overall this framework shows a sequential view on the strategic decisions in a supply chain. But it also supports dynamic adaptations to differing origins, eg: A redesigned product within an existing supply chain would only make the steps II (Product design) and IV (Process optimization) necessary; changes to the network, like adding a new supplier, would concern square III (Formation of production network).

Cases

Using five different cases Seuring goes through the above mentioned strategic decision categories. The cross-case analysis reveals that the categories may sometimes be overlapping (production and return phase), but the decision can still be sorted into one or the other category.

Conclusion

As described in the introduction, the next step would be to use these categories to find general, optimal strategies which should be followed in each of the parts of the matrix in figure 1.
But while I was reading the paper I realized that this might not be possible, since the steps are to vague for that.

Hence, the framework offers an alternative approach, which not only takes a look at the content of the decision, but also adds a process perspec- tive. Such a more dynamic approach allows structuring the related decision based on the relationship and the product life-cycle dimension.

So my question, how can you use this framework beside finding optimal solutions for network design problems?
One suggestion might be, to use the process nature of the framework and analyze the organizational relationships while going through the different decision phases.

What do you think?

Reference:

Seuring, S. (2009). The product-relationship-matrix as framework for strategic supply chain design based on operations theory International Journal of Production Economics, 120 (1), 221-232 DOI: 10.1016/j.ijpe.2008.07.021

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Process oriented Approach to Supply Chain Risk Management

Daniel Dumke — Mon, 24 Jan 2011 14:39:00 +0000

Supply Chain Risk Management started from the need to better control the risks within Supply and Demand Networks. The processes in (Corporate) Risk Management have been developed and convene in the classic, cyclic processes:

Risk Identification
Risk Assessment
Measures (Development and Implementation)
Risk Monitoring

Hallikas et al. (2004) tried to transfer this process into the supply chain setting.

Figure 1: Risk Diagramm (Hallikas et al. 2004)

Risk Management Process in the Network Environment

The authors start with a description of the general steps in the risk management process. The risk assessment is done using a semi quantitative diagram (Figure 1). For the risk management phase the authors suggest the following strategies:

Risk transfer
Risk taking
Risk elimination
Risk reduction
Further analysis of individual risk taking

So up to now this information can also be found on wikipedia, but the genuine part starts with the “network risk management process”, where additional focus has to be put on how to integrate new aspects of collaboration and cooperation. The key is to add a seperate process of mutual risk identification, assessment, … and to feed insights from the individual risk processes into the mutual risk monitoring process. Figure 2 exhibits the details.

Figure 2: Risk Management Process in a Network Setting (Hallikas et al. 2004)

Deeper Understanding of Network Risks

Nevertheless it is important to stress the importance of in depth analysis of the network risks. It is likely that simplistic methods for risk identification sometimes used in a corporate environment are not sufficient for a network setting. Increased complexity and additional risks increase the chance of missing important risks. As an example for a more suited method the authors cite a case study using systems dynamics analysis (see figure 3).

Figure 3: Example Dynamic Complexity (Hallikas et al. 2004)

Conclusion

Goal of the mutual risk assessment process is to benefit both partners, the paper describes some challenges of risk management in a cooperative environment. Communication between the companies on their views on risks can therefore help to find adequate strategies to deal with the risks.

Reference:

Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V., & Tuominen, M. (2004). Risk management processes in supplier networks International Journal of Production Economics, 90 (1), 47-58 DOI: 10.1016/j.ijpe.2004.02.007

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