Design & Strategy

Linking risk management practice and strategies to performance

Daniel Dumke — Mon, 03 Sep 2012 13:41:29 +0000

Agility and robustness are the concepts of choice if you want to reduce supply chain risks. In their upcoming paper (Dealing with supply chain risks: Linking risk management practices and strategies to performance) Andreas Wieland and Carl Marcus Wallenburg analyze the effects of both of these concepts on customer value and business performance.

Since Andreas is a colleague of mine at the “Berlin University of Technology” he kindly provided me with a heads up.
Actually, the paper has been named the most exiting paper of 2012 so far by the International Journal of Physical Distribution & Logistics Management.
Here is what the IJPDLM has to say about it:

Probably the most important topic today in global distribution and SCML is the governance of risk across relationships. Andreas Wieland and Carl Marcus Wallenburg have written a very interesting manuscript titled, “Dealing with supply chain risks: Linking risk management practices and strategies to performance.” What managers need is the ability to justify when to and to not spend money on monitoring and governance practices. To build justification, a value understanding must be developed. This manuscript opens the door for expanded research in this area, while also generating interesting practical outcomes. Researchers should view this manuscript as a call for future research in IJPDLM.

There is also a short video summary of the article.

Methodology

The effects of supply chain risk management (SCRM) on the performance of a supply chain remain unexplored. It is assumed that SCRM helps supply chains to cope with vulnerabilities both proactively by supporting robustness and reactively by supporting agility. Both dimensions are assumed to have an influence on the supply chain’s customer value and on business performance. This research is aimed at providing clarity by empirically testing these hypotheses and scrutinizing the findings by the means of case studies.

The authors employ two empirical methods to assess this question:

First, a survey was conducted with 270 manufacturing companies from Europe.
Second, these findings were then complemented by six case studies to explore the survey findings further and extend on them.

Figure 1 shows the process of the method selection.

Figure 1: Research Concept (Wieland and Wallenburg, 2012)

Building hypothesis: agility and robustness

The authors analyze preventive and reactive strategies (figure 2).

Figure 2: Robustness and Agility to reduce Supply Chain Risks (Wieland and Wallenburg, 2012)

Based on the existing literature and research gaps the authors form the following hypotheses as a foundation for their research:

Supply chain risk management
- H1a: Supply chain risk management has a positive effect on agility.
- H1b: Supply chain risk management has a positive effect on robustness.
Supply chain’s customer value
- H2a: Agility has a positive effect on the supply chain’s customer value.
- H2b: Robustness has a positive effect on the supply chain’s customer value.
Business performance
- H3a: Agility has a positive effect on business performance.
- H3b: Robustness has a positive effect on business performance.
- H4: Business performance is positively influenced by the supply chain’s customer value.

Results

The findings of the survey are shown in figure 3.

Figure 3: Resulting links between Research Concepts (Wieland and Wallenburg, 2012)

The hypotheses 1a and 1b are both supported, which reinforces the notion that agility and robustness are indeed used as measures in supply chain risk management.
Both measures also have a positive impact on the supply chain’s customer value.
On the other hand the link between agility and business performance cannot be supported for agility and only weakly supported for business performance.

The results of the case studies expand on these results. Figure 4 shows the case study participants.

Figure 4: Case Study Participants (Wieland and Wallenburg, 2012)

In general, the cases revealed that all companies strive to be both agile and robust in order to utilize the specific advantages of each approach.

Interestingly, “the case studies show that robustness is rather required on the supplier side (i.e. upstream in a supply chain). For instance, multiple suppliers are helpful, if the quality of a component is low or a supplier has a high insolvency risk. This finding first of all implies that supplier-related risks tend to be more predictable as otherwise a proactive approach would not be feasible and effective.”

The following propositions are made as a summary of the case interviews:

P1: Realizing agility is an effective supply chain approach to deal with customer-related risks.
P2: Realizing robustness is an effective supply chain approach to deal with supplier-related risks.
P3: To be effective, the degree of agility and robustness needs to fit to the overall competitive strategy.

Conclusion

From my point of view, the nomination as most exciting paper of 2012 (so far) seems to be highly justified.
The nomination as well as the high survey response rate of nearly 20% highlights the importance of supply chain risk management in the current research and practice.
Beside the results, I especially liked the elaborate description of the methodological foundation both for the survey and the case study research.

I also found some points with the potential for improvement:
First, the survey results show that there is no link between agility and business performance. On the other hand, the authors claim that the hypothesis on the linkage between robustness and business performance can be supported. However this happens on a very thin foundation the confidence level for this interpretation is quite low with a error 1 probability of nearly 10%.

Second, a key conclusion of the paper is that robustness should be used for supply-side risks, agility for demand-side risks. However this result originates only from the case study interviews. From a formal point of view I would have wished that it would have also be supported by the survey results.

Third, there is a clear disadvantage to using only concepts (like agility and robustness) in one’s research, since those do leave a lot of room for further questions: What robustness measures are really improving the value for the customer? Are there also agility strategies which should be employed for supply side risks?

But these are only minor points, I really recommend you to read the paper: You can download the full (pre-publication) article here. Click one of the following links, if you want to learn more about robustness or agility.

Reference:

Wieland, A., & Wallenburg, C.M. (2012). Dealing with supply chain risks: Linking risk management practices and strategies to performance International Journal of Physical Distribution & Logistics Management, 42 (10)

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Managing supply in the firm of the future

Daniel Dumke — Mon, 27 Aug 2012 11:56:31 +0000

This week I would like to think about the future of supply chain management. Cox and Lamming wrote a corresponding article titled: “Managing supply in the firm of the future”.

Methodology

It is difficult to make predictions, especially about the future. So to infer about future development the authors use case studies and historical analysis.

Short history of corporate sourcing

Within the last century business models have changed quite a lot in nearly all industries.
Aa hundred years ago industries like watchmaking were focussed on small customer niches with few and wealthy customers accustomed to great service.
This changed with industrialization and the advent of mass production (and mass services).
Companies thrived in an environment where new customers were less sophisticated, labor was cheap and plenty and supply compliant.

With the rise of the customer (“The customer is always right”) this changed, when customers demanded even faster, smaller and cheaper products.

The model of purchasing born in the early decades of this century, during the heady days of mass production, has lasted almost unchanged to this day. It is based upon the purchaser having a wide spread of potential suppliers, each of whom is able to supply in accordance with a specification, without deviating from it. When it appeared that the customer was not able to provide this specification, the supplier’s expertise could either be bought (leading to the birth of the vertically integrated mass production leviathans) or extracted under the threat of loss of business within the customer’s supply market.

Themes for future development

In research four key themes emerged in the conceptual development of the management of “supply chains”. These should form the basis for the future development of the perception of the supply function.

Theme 1: It is necessary to take a total supply chain view
Key insights:

“The consumer is perceived to be at the end of a supply chain—a series of value-adding events and activities that leads to the provision of a desirable—valuable—product or service.”
“Supply chain management is […] a process of realignment of activities, from each firm’s point of view, in order to reduce value losses, so that the output from the total chain satisfies the consumer and results in the success of all parties to the chain.”
“Within the [value] stream are barriers—interfaces between companies. The management imperative is thus to design those interfaces with minimum impediment, to allow value to flow.”

Theme 2: The chain is an unsatisfactory metaphor: the firm is part of a network
Key insights:

“The so-called ‘chains’ often contain looped relationships (where the customer is also a supplier to the supplier), lateral links (where the supplier is a supplier to both the customer and another supplier), dependencies (where the performance of one supplier is intrinsically linked to that of another) and other non-linear facets which deny the convenience of thinking in simple terms.”
“Adopting a network perspective can lead to perceiving supplier relationships as indistinguishable from customer relationships.”
“The contribution of the network metaphor to understanding the matter to managing value comes from its method of grappling with complexity.”

Theme 3: The firm concentrates on its core competencies and outsources everything else
Key insights:

“Managers have, for some time, been encouraged to view their firms as a combination of ‘core’ competencies – those which it is deemed essential to own in order to compete in a market – and, by process of elimination, ‘non-core’ competencies.”
“[…] the issue of whether to ‘make or buy’ is not straightforward; it is always a problematic issue for the firm which may be resolved either through vertical integration or through outsourcing. The key strategic deci- sion for the firm is to decide what the boundaries should be between the two extremes of internal or external contract.”

Theme 4: The firm is an unsatisfactory unit of analysis: the flow of value takes place in a loosely aligned array of assets and competencies over which no one commercial organization has ultimate control
Key insights:

“The further away from the core competencies of the firm, the less there is a need for medium asset specific skills to be vertically integrated, and thus the more support may be expected for outsourcing the activity.”
“[…] firms are best viewed as a ‘nexus of contracts’. The importance of this interpretation is that it forces us to see firms not as fixed entities, existing as objects within a static market structure, but as potentially fluid and flexible constructs whose internal structures and external boundaries may change as circumstances dictate and opportunities require.”

Outlook

The themes sketched above lead the authors to several conclusions about the future outlook of supply (chain) management.

The future role of negotiation, contracting, and developing incentives will, if the analysis presented here is correct, be of immense importance for the future success of firms. This arises primarily from the insights of transaction cost and agency theory, and the realisation that the boundaries of the firm are not, nor should be, fixed in time or space.

This means that in the future there will be a definite need for professional managers trained in the arts of – and perhaps with a ‘nose’ for – assessing the relative costs and benefits of internal and external contracts for the successful achievement of a sustainable position on specific supply and value chains

The first task in a strategic supply management approach is to undertake value chain positioning. This refers to the process by which the key decision makers within a firm consciously undertake market positioning through an analysis of the totality of supply and value relationships within their markets. This is achieved through the use of margin-cost analysis.

Conclusion

This article was published 15 years ago and it draws the picture of a future with fluid ever-changing interfaces between companies. Where companies are steered as a collection of contracts.
When I read the article I was amazed how good the selected themes still fit the current development. Even though some parts of the “predictions” seem to have come true (eg. the professionalization of supply chain management), my overall impression is that organizational hurdles prevent companies from being shaped in this way.
But this also may only be a matter of time.

Reference:

Cox, A., & Lamming, R. (1997). Managing supply in the firm of the future European Journal of Purchasing & Supply Management, 3 (2), 53-62 DOI: 10.1016/S0969-7012(97)00002-6

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Supply Chain Redesign for Resilience using Simulation

Daniel Dumke — Mon, 13 Aug 2012 07:30:40 +0000

Today we have a look at current research regarding the improvement of resilience within a supply chain.
In their 2012 paper “Supply chain redesign for resilience using simulation” Carvalho et al. analyze supply chain resilience on the basis off a Portuguese automotive parts manufacturer.

Methodology

As indicated by the paper’s title the authors main method is a simulation study. The simulation model is based on the results of a case study. Semistructured interviews were conducted to gather the relevant data off a Portuguese automotive supply chain.

But first, the authors analyze the current literature on supply chain design and resilience (figure 1).

Figure 1: Literature Review Supply Chain Design (Carvalho et al., 2012)

Simulation model

The structure of the supply chain model is shown in figure 2. The Portuguese automaker has a capacity of over 180,000 vehicles per year and all vehicles are customized.

Figure 2: Supply Chain Structure (Carvalho et al., 2012)

Arena 9.0 in conjunction with Microsoft Excel has been used to implement the model of the supply chain.
The processes which have been identified rely on the SCOR process definitions. Figure 3 shows the simulation model flowchart.

Figure 3: Supply Chain Processes (Carvalho et al., 2012)

Supplier lead-times were estimated together with the case study participants using triangular distributions (figure 4).

Figure 4: Input Data Lead Times between Suppliers (Carvalho et al., 2012)

Performance was measured using two key performance indicators: lead-time and total cost.
Overall six scenarios were designed by the authors. One containing the base scenario without using any strategy to reduce risk, one using a redundancy-strategy, and another one implementing a flexibility-strategy. These scenarios were then duplicated to generate one group with a disruption in the material flow between supplier 2_1 and 1_1 and another group without any disruption.

Figure 5 shows the total cost performance in different scenarios. Scenarios to 4 and 6 are affected by the disruption.

Figure 5: Simulation Results in different Scenarios (Total Cost, Carvalho et al., 2012)

The authors conclude

Two strategies widely used to mitigate disturbance ad- verse effects on SCs were considered (flexibility and redundancy) and six scenarios were designed. To evaluate the different scenar- ios designed, two performance measures were defined and com- puted for each SC entity, Lead Time Ratio and Total Cost.
The results of the simulation allowed to compare SC behavior after the occurrence of the disturbance under the two SC resilience design strategies. Both strategies are effective in reducing the neg- ative effects of the disturbance on SC performance. When the flexibility strategy is applied the Total Cost of the SC is less, in comparison with the redundancy strategy and the Lead Time Ratio is better.

Conclusion

Since my own research revolved in parts around my own simulation model I have two comments on this specific implementation, but I would like to share with you:

I’ve seen this already in other papers: the description of the scenarios is really bad. For one there is no overview summarizing the key differences between each of these scenarios, furthermore the description of how these scenarios are implemented in the supply chain model lack in detail.
Another key aspect to simulation modeling is the validation off the model’s output. In this case the authors are using real input data from the case study, but there is no mention if they also compared the model’s output with the real supply chain.

The conclusion of the authors also highlights the difficulty of interpreting simulation results. What could one learn from this study? Redundancy and flexibility can be used to reduce risk?
For me this insight does not qualify as a groundbreaking revelation. At least not in 2012.

Reference:

Carvalho, H., Barroso, A.P., Machado, V.H., Azevedo, S., & Cruz-Machado, V. (2012). Supply chain redesign for resilience using simulation Computers & Industrial Engineering, 62, 329-341 DOI: 10.1016/j.cie.2011.10.003

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Secure Collaboration in Global Supply Chain Management

Daniel Dumke — Mon, 16 Jul 2012 09:54:07 +0000

One of the fundamental problems in supply chain management in general is that of finding the right trade-offs between information sharing and keeping one’s distance towards potential competitors.

Zeng et al. did a literature review to collect some of the current insights on how to solve this problem.

Collaboration in product design and supply chain environment

Figure 1: Collaborative Product Development Process (Zeng et al., 2012)

Figure 1 shows a typical process from problem formulation up to delivery of the finished product. This process can be decomposed into: collaborative product development (CPD), design chain management (DCM) and supply chain management (SCM).

Collaboration conflicts

Several conflicts threaten the viability of the collaboration. These are all related to the information which is interchanged by the stakeholders.

Figure 2 shows the information flow.

Figure 2: Information Flow between Supply Chain Participants (Zeng et al., 2012)

This information can be divided into information to protect and information to share.
Figure 3 lists some of the relevant information.

Figure 3: Information to Protect and Information to Share (Zeng et al., 2012)

Securing collaboration

Figure 4 summarizes the problem analysis and the corresponding conclusions the authors draw.

Figure 4: Problem Analysis and Potential Solutions (Zeng et al., 2012)

The authors suggest and elaborate on four categories to secure collaboration:

Computer security and privacy technologies
Which consists of measures for access control and secure multi-party computation, where computations can be done on multiple datasets without revealing the underlying information. Furthermore privacy-preserving location-based services are summarized here.
Information partitioning
This contains the aspects of document classification and risk management.
Contract management
Partner relationship management
Here the authors include trust management and innovation capability and reverse engineering mitigation.

Last but not least figure 5 summarizes the current state of the art for the above mentioned problems and potential future research directions.

Figure 5: Future Research Directions on Secure Collaboration (Zeng et al., 2012)

Conclusion

The authors take the view of a computer scientist and find technical solutions (like secure multi-party computation) to fight supply chain problems.
One of the first things that came to my mind: “One cannot use technology to solve social problems.”
I do think that those measures should be pursued further in research as well as business. But the first steps have to be done by describing and defining their individual information buckets, by answering the strategic question: What information is core to our business and should be protected and what can be shared?

Reference:

Zeng, Y., Wang, L., Deng, X., Cao, X., & Khundker, N. (2012). Secure collaboration in global design and supply chain environment: Problem analysis and literature review Computers in Industry, 63 (6), 545-556 DOI: 10.1016/j.compind.2012.05.001

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Negative Default Dependencies in Supplier Networks

Daniel Dumke — Mon, 18 Jun 2012 12:45:08 +0000

If people talk about disruptions and network effects within the supply chain, the associations are most often negative.

The picture of an automotive/just-in-time supply chain comes to mind, where a small screw from a distant supplier did not get delivered in time and all production processes within the whole network suddenly come to an involuntary halt.

But on the other hand there are companies profiting from these smaller and larger disruptions: competition.

To analyze these effects we have a look at the consequences of negative default dependence between suppliers. The full paper can be found here.

Default dependence and method

Empirical research on corporate defaults in the finance literature indicates that corporate defaults often cluster in time and that the default of a company is frequently affected by the defaults of other companies. […]

In the automotive industry there are several reasons why positive default correlation may exist in supplier networks. First, automotive suppliers face similar challenges, such as large and powerful customers who force suppliers constantly to cut costs and invest heavily in R&D or the volatile prices of raw materials. It is likely that the automotive suppliers have to suffer from the consequences of these challenges in a similar way or are reacting in a comparable manner to cope with them. Second, suppliers may maintain relationships with other suppli- ers and share “technical and explicit information as well as tacit information” and “work together closely, exchange ideas, and even engage in joint venture projects.” Being linked so closely may result in comparable strategic and operative actions and behavior of the supplier firms. The consequence is that decisions that lead to financial problems are likely to be taken by both suppliers that are linked through close supplier–supplier relationships.

However there are also reasons/situation in which default-events might be negatively correlated:

First, after a supplier default, customers might shift business to another supplier in the network.

Second, the default of a supplier can result in lay-offs and the competitor will be able to hire more and qualified staff.

Finally, due to the reduced number of alternative sources, the buying firm may become more dependent on the surviving supplier who, through the gained power, may be able to incur higher profit margins and, thus, gain in financial stability.

The authors use copula-functions.

Financial data to calculate default probabilities for a case study are derived from Datastream (Thomson Reuters). This data was used to calculate the individual default intensities.

For the worldwide 100 largest suppliers to the automotive OEMs in 2005 that were included in the Datastream database, we extracted the necessary data required for specifying and adjusting our model.

Figure 1 shows the default intensities for selected companies.

Figure 1: Company Characteristics (Wagner et. al, 2011)

The default dependencies were calculated using numerical results of a simulation.

Results and implications

The authors draw three conclusions from the results of their analysis.

First, our estimation of default intensities of selected first-tier suppliers in the automotive industry supports the concerns raised in the literature about the financial stability of automotive suppliers. Supplier default intensities above 5% are disquieting for the respective automotive OEMs.

Second, the simulation results depict that negative default dependence among suppliers in a supplier network has consequences for the survival probabilities of the entities in the network. The higher the individual default intensity of a supplier, the stronger the effect of negative default dependence on its survival probability after the default of the other supplier. […] for example, the portfolio with low default intensity suppliers demonstrated to increase the survival probability of the second supplier by 2.7% and the portfolio with high default intensity suppliers by 15.4% (in comparison to the independence case).

Third, in addition to the dependence level, the dependence structure, reflected in our model by the choice of copula, is an important factor for modeling default dependence in a supplier portfolio.

The following management implications can be given:

Purchasing managers should be aware that negative default dependence between suppliers may exist and take this into account for their sourcing decisions. A better understanding of the randomness and relatedness of supplier defaults internal to the supplier network can help firms to plan for uncertainty, take proactive measures to reduce risk (e.g., switch a supplier), and achieve better, less variable outcomes.

Firms should preferably establish relation- ships with suppliers that have low default intensities, and with suppliers that will benefit from the default of their competitors – given that the default of the competitor will not significantly shift the power in the buyer–supplier relationship

Conclusion

Not only surviving competitors can potentially profit from the default of its contestant, but also their clients may profit.

This research shows that interdependencies – no matter if positive or negative – have to be analyzed and should included in the decision making process.

One has to keep in mind though, that Wagner et al.‘s results heavily rely on their method to estimate the default dependencies within the supplier portfolio.

This might induce additional uncertainty in the form of model risk.

Reference:

Wagner, S., Bode, C., & Koziol, P. (2011). Negative default dependence in supplier networks International Journal of Production Economics, 134 (2), 398-406 DOI: 10.1016/j.ijpe.2009.11.013

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Modeling Defaults of Companies in Multi-Stage SC Networks

Daniel Dumke — Mon, 28 May 2012 14:07:06 +0000

Agent-based supply chain models are build using small entities (agents), which might represent a single company.
Each of the agents has its own goals and rules of operation programmed into a computer. The interaction between several agents of this kind leads to a more realistic and complex behavior of the system.

There are several different schools for the quantitative analysis of supply chain risks. Simulation is one of them and agent-based models show several distinct advantages: They are both easier to understand and allow for a more complex system behavior, than other quantitative methods.

After this introduction I would like to have a look at a current agent-based supply chain model, which analyzes the effect of bankruptcies on supply chains. The full paper can be downloaded here.

Model

The authors focus on a comprehensive view on the supply chain: several stages (horizontally and vertically) are modeled. Figure 1 shows an exemplary supply chain.

Figure 1: Exemplary Supply Chain Structure (Mizgier et al. 2012)

Each circle represents one node or agent, the connections are drawn as lines.
The model features five additional characteristics worth mentioning:

Price dispersion (prices can vary between companies)
Evolution of supply chain topology (links between companies can be changed, by the agents themselves)
Network reconfiguration (the reconfiguration is based on the price)
Production dynamics (output is determined by the invested working capital)
Dynamics of costs of production (random changes to the environment every five periods, lead to changes in the cost function of the companies)

Last but not least, companies may go bankrupt if they are not able to perform their short therm debts. There are no loans.

Results

First, the network performs as expected. During the first period turbulences can be observed. Figure 2 shows the utilization of working capital during the simulation (1 equals 100%).

Figure 2: Capacity Utilization (Mizgier et al. 2012)

After 200 iterations a typical start scenario might look like figure 3.

Figure 3: Network Structure after Initialization Period (Mizgier et al. 2012)

Starting from this state the

The firms with the best profit/cost ratio are growing and adding new suppliers, whereas the working capital of the firms whose sales price is higher than the mean price of the given stage is slowly decaying and results in the defaults of firms.

A stable state might look like figure 4.

Figure 3: Network Structure after Stabilization (Mizgier et al. 2012)

The authors deduce three implications from those results:

The first implication is that during the process of assessment of the company’s risk exposure, managers should keep their focus on the global structure of the supply chain network instead of being restricted to the own portfolio of suppliers and customers.

Secondly, as a result of the dynamics of the topology of the supply chain network, strong competition in prices and fast changing technology, even the most reliable firms should be monitored and constantly re-evaluated in terms of their production capacity and risks associated with their structure of connections.

Third and most important, managers should find ways to cut costs and reinvest the free cash flows in new technology of production, which will allow further cost reductions and the development of new innovative and cheaper products.

Conclusion

Being stuck in the supply chain can lead to negative consequences, when ripple effects cause multiple suppliers and customers to default. Furthermore, these supply chain partners might also not be the most efficient ones, and these prices affect each participating company.
All in all, a great paper, but I would have liked to read more about the authors’ efforts to validate and verify the model’s integrity.

Reference:

Mizgier, K., Wagner, S., & Holyst, J. (2012). Modeling defaults of companies in multi-stage supply chain networks International Journal of Production Economics, 135 (1), 14-23 DOI: 10.1016/j.ijpe.2010.09.022

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The Postponement Strategy from a Supply Chain Perspective

Daniel Dumke — Mon, 07 May 2012 14:11:59 +0000

If you think about it. Postponement is one of the more involving strategies available in supply chain management. At least from a design perspective, postponement requires changes to the value-generation process, which may comprise several echelons within the supply chain.

The paper I review today analyzed the implementation of postponement strategies in China and suggests factors to help with the decision which kind of postponement to select.

Types of postponement and its determinants

Figure 1 shows a summary of different types of postponement strategies, found in literature.

It can be defined “as a strategy that changes the differentiation of goods (form, identity and inventory location) to as late a time as possible.”

However:

Today researchers view postponement differently. Van Hoek (2001) views it as an organizational concept whereby some of the activities in the supply chain are not performed until customer orders are received.

The complement of postponement is speculation, “which means changing form and moving goods to inventories as early as possible to reduce the cost of supply chain.”

Figure 1: Different Postponement Strategies (Yeung et al., 2007)

Several factors affect the decision on which type of strategy to use. Other researchers already found the following determinants (beside others):

technology and process characteristics (feasible to decouple primary and postponed operations, limited complexity of customizing operations, modular product designs, and sourcing from multiple locations);

product characteristics (high commonality of modules, specific formulation of products, specific peripherals, high value density of products and product cube and/or weight increases through customization); and

market characteristics (short product life cycle, high sales fluctuations, short and reliable lead times, price competition and varied markets and customers).

Method

Beside the literature review, of which some of the results are summarized above, the authors use a grounded theory approach to come to their conclusions.

This method comprises several semi-structured interviews with senior executives. The sample companies were taken from the Pearl River Delta (PRD) a region within the Guangdong province bordering Hong Kong.

Overall the interviews with eight companies were used for the grounded theory. The characteristics of the sample companies are presented in figure 2

Figure 2: Sample Companies and their Supply Chains (Yeung et al., 2007)

Cross-case analysis

In figure 3 the results of three (of eight) companies are compared.

Figure 3: Cross-Case Comparison (extract; Yeung et al., 2007)

The supply chain structure is characterized by a four letter acronym (second column in figure 3):

The first two letters refer to the supply side the second pair to the demand side.

Of those pairs the first letter (O/F) refers to the market type: *o*ligarch or *f*ree-market.

And the second letter (C/L) refers to the closeness of the actors: *c*lose or *l*oose.

Results

From these results the authors draw the following propositions:

P1. When a supply chain has a balanced structure, it should use speculation or production postponement.

In the balanced supply chain structure, no single actor is significantly more powerful than any other actor. In order not be “locked” in by a specific partner and losing business opportunities and/or bargaining power, a company will not tailor their processes for a specific partner. However, the key concept of postponement is to produce based on actual orders instead of forecasts, and this requires a close relationship between partners.

P2. When the supply chain has an unbalanced structure, it should use purchasing postponement or product development postponement.

The unbalanced supply chain is characterized by a leading company who has more power than other companies in the supply chain. In order to improve efficiency and provide a high service level, the leading company often demands other companies to tailor their production process and share information. As such, it is easier to build close relationships in an unbalanced structure than it is in a balanced one. This makes high degree postponement possible and suitable.

Figure 4 shows the summary of the strategic implications.

Figure 4: Strategy Selection based on Supply Chain Structure and Information Sharing (Yeung et al., 2007)

Conclusion

First the ugly:

Key to a grounded theory approach in accordance with Strauss and Corbin (as the authors want to do) is the number of cases used. This number has to be determined by the so-called point of theoretical saturation. This means that the researcher only stops once the last case(s) did not produce any new insights. A typical number of interviews for a grounded theory would be between twenty and thirty.

In this case the authors set themselves an artificial limit of about 7 cases. This is actually often used in case-study research, which is another research methodology. But in case study research one usually taps on several other data sources to triangulate and validate the data.

To conclude, I really do not know what method the authors used. And therefore the validity of the study suffers.

Second the good:

Nonetheless figure 4 can be used as a guideline to select from different types of postponement strategies depending on the supply chain characteristics. It should just be used with a certain wariness.

Reference:

Yeung, J. H. Y., Selen, W., Deming, Z., & Min, Z. (2007). Postponement strategy from a supply chain perspective: cases from China International Journal of Physical Distribution & Logistics Management, 37 (4), 331-356 DOI: 10.1108/09600030710752532

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Optimizing Efficiency-Robustness Trade-Offs in SC-Design

Daniel Dumke — Mon, 16 Apr 2012 15:04:18 +0000

Efficiency is about trade-offs. Effectiveness is about achieving a goal, making it happen no matter what.
But in reality resources are scarce and efficiently reaching a goal is nearly as important as reaching it at all.

Low-risk supply chains often contradict the efficiency demands of the company’s stakeholders.
Robust strategies, which reduce risks while keeping performance up, are still the holy-grail of supply chain risk management.
Simple strategies are not able to accomplish this goal, only a extensive redesign of the supply chain (as for example using the postponement strategies) may indeed be able to reduce risks while keeping performance up.

How these trade-offs can be optimized according to the goals of the company is the topic of the 2011 paper: “Optimizing efficiency-robustness trade-offs in supply chain design under uncertainty due to disruptions” by Shukla, Lalit and Venkatasubramanian.

Model and robustness metric

The authors use a mathematical model to implement their robustness metric, which “is based on expected losses incurred due to network failures. It defines efficiency and robustness in terms of operational cost and expected disruption cost (EDC), respectively. The EDC is defined in terms of loss of opportunity cost incurred due to not meeting demand on time after a disruption has occurred.”

Decision variables of the mixed-integer linear model are:

the assignment of the warehouse to the manufacturing center and
the assignment of the warehouse to the customer.

The objective function is defined as the weighted sum of efficiency and robustness. Efficiency is defined in terms of OC of the supply chain and robustness is defined in terms of the EDC [figure 1]:

Figure 1: Objective Function (Shukla et al., 2011)

The authors use secondary data to build a case study with scenarios from the current US.
Figure 2 shows an extract of the demand numbers used.

Figure 2: Aggregated Demand as Input for the Model (Shukla et al., 2011)

Figure 3 contains details of the risks experienced by the respective warehouse locations.

Figure 3: Warehouse Distances and Risk Data (Shukla et al., 2011)

Four case studies are conducted, I picked number one as an example and refer you to the paper for further details.

Case study 1 deals with node failure or failure of warehouses for functional products. Since most of the warehouse locations are far apart we assume that failures are independent of each other and multiple failures can occur simultaneously. The probability of failure of a warehouse depends on the region in which the warehouse is located.

Results

Figure 4 shows the efficient supply chain design for the first case study and figure 5 the corresponding results for more robust results.

Figure 4: Case Study: Efficient Supply Chain Design (Shukla et al., 2011)

Figure 5: Case Study: Robust Supply Chain Design (Shukla et al., 2011)

The authors state that:

The resulting supply chain is much more reliable in the long term since we have shown that a significant amount of robustness can be built into the system without compromising a lot on efficiency.

Conclusion

So there is no free lunch and it seems inevitable to sacrifice some of the efficiency to gain robustness, but this paper shows that in the case studies with only small cost increases risks can effectively be reduced based on supply chain design changes.
So if your customers value reliability this might be the right approach for your chain.

Reference:

Shukla, A., Lalit, V., & Venkatasubramanian, V. (2011). Optimizing efficiency-robustness trade-offs in supply chain design under uncertainty due to disruptions International Journal of Physical Distribution & Logistics Management, 41 (6), 623-647 DOI: 10.1108/09600031111147844

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Fragile Food Supply Chains: Reacting to Risks

Daniel Dumke — Mon, 05 Mar 2012 12:22:00 +0000

The foods supply chain satisfies one of the most basic Maslowian needs.
Interruptions can quickly become major crisis. Assessment and reactions to risks therefore seems to be a vital point.

This article presents a framework by Dani and Deep on how specific food supply chain risks can be analyzed and how reactions can be tailored.

Status quo

The authors start by sketching the status quo of a foods supply chain and the trends affecting it.

Figure 1: Food Supply Chain Schematic (Dani and Deep, 2010)

A typical foods supply chain consists of six echelons starting at the farmer. The second stage is usually an aggregator/marketer who provides the input for the processing facilities. The distribution stage starts with the wholesaler which delivers the product to the customer usually via a retail stage.

Several trends affect the supply, processing and demand for food products:

Consumer: In the past decades demand shifts have been observed, leading for parts to more meat based diets (e.g. China) or to a more health-concious consumption.
Food standards: Internal (company-made) and external (government-made) standards seem to be on the rise to provide a high degree of food safety.
Technology: Advances especially in IT technology and electronics have made it possible to provide uninterrupted tracking within the foods supply chain to improve safety and responsibility further.

Research goal and method

The aim of the presented paper is to answer the following questions:

Question 1: If a risk were to materialise, what can be done to minimise its impact?

Question 2: What are the reactive risk management peculiarities of the food supply chain?

The paper is build as a meta-study using secondary sources: “research publications, journal papers, newspaper reports, articles from business magazines, websites and government reports”

From these sources thoughts and ideas were extracted and then clustered with the goal to distill common themes which can be used for the general framework.

Results

The following themes were identified:

Speed of response: Speed of response is often cited as the most crucial variable in controlling the effects of risk. […] A quick response to food supply chain incidents may include: 1) Deploying a crisis management team, 2) Scope and extent definition of the problem, 3) Identifying affected areas and their impact on critical activities, 4) Recall procedures.

Communication: Communication and information sharing is often beneficial in controlling the damage caused by a risk. […] Crises are situations of extreme stress and therefore an organisation needs to ensure that they are able to provide coherent, precise and timely information to all concerned members.

Escalation: Escalation refers to the procedure for making information available to the most relevant authority.

Resource and fund availability

Multi-partner collaboration

Leadership: Leadership is an essential catalyst for the above factors to combine.

Two distinct risk types can be separated:

Type I: These are risks which are concerned with food safety, as well as maintaining a secure supply of food. These are differentiated on the basis of the responsibility and involvement of regulatory authorities. Food contamination is the most prominent of these risks and involves any incident which may constitute a public health emergency of domestic or international concern.

Type II: These are all other risks which affect the supply chain but do not have a direct impact on food safety. The involvement of these types of risks is primarily the organisation and its direct supply chain. These risks include transportation strikes, loss of power, flooding, etc.

Combining these aspects into a single conceptual model results in the following diagram.

Figure 2: Conceptual Model (Dani and Deep, 2010)

Case based validation

The author then use three case studies (summarized in figure 3) to post-hoc validate their model.

Figure 3: Case Studies for the Framework Validation (Dani and Deep, 2010)

This is the conclusion of the author of what went wrong in the first case (for the other cases I refer you to the original paper):

Case highlights:
One of the biggest cases of food product recall in US history is the most recent case of outbreak of illness caused by Salmonella typhimurium. The FDA and Centres for Disease Control and Prevention (CDC) identified the source as peanut butter and peanut butter paste in the processing plant of Peanut Corporation of America (PCA) at Blakely, Georgia.

Case analysis:
In this case, clearly the type of risk was type 1. Referring to the conceptual model depicted in Figure 2, it can be seen that although all entities were involved within the crisis management process, the speed of response was very slow causing failure of the PCA [Peanut Corporation of America]. The Peanut Corporation, despite being aware of a salmonella poisoning investigation at King Nut Corporation, did not initiate any damage control steps. The FDA inspection at the PCA started on 9 January, but the recall announcement did not happen until 13 January. The collaboration, communication and information between different members were possibly inadequate as the recall list grew from 21 products to all products manufactured at the location. The date of recall moved back to any product manufactured at the facility since 1 January 2007, which highlights gross inconsistencies in process controls within the organisation. There were no escalation procedures set up until the FDA took control over the crisis management process. Also, multi-partner collaboration was lacking as a second plant of PCA was also found contaminated with Salmonella 2 weeks after the initial inspection at PCA.
In the investigation (Weise and Schmit 2009), it was highlighted that the leadership of the company had the chance and information to intervene much earlier in the process to stop the con- taminated product reaching the customer. However, poor leadership and improper risk mitigation procedures led to the risk propagating widely and causing human fatalities. As per the model, even though the FDA intervened and took over the risk mitigation process, the speed of response was slow and there were no proactive measures instilled by PCA with regard to risk mitigation. Hence with reference to the model, although this is a type 1 risk and process A is involved, the success of the intervention is dependent upon the capability of process B.

The authors conclude that the framework is a good fit to analyze cases after the crisis happened. Furthermore they are confident that it should also be applicable for proactive and predictive analyses.

Conclusion

I think focussing the research questions to include only food supply chains in this case results in a very focussed model of the impact factors in food supply chain resilience.
On the other hand as often (this may be good or bad) I fail to see why this model should only be applicable to disruptions in foods supply chains. Leadership, communication, and collaboration are key factors in many (most?) supply chains and in times of disruptions even more so.

Reference:

Dani, S., & Deep, A. (2010). Fragile food supply chains: reacting to risks International Journal of Logistics Research and Applications, 13 (5), 395-410 DOI: 10.1080/13675567.2010.518564

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SC Design: Organizational Roles in Network Management

Daniel Dumke — Mon, 20 Feb 2012 10:46:17 +0000

Supply chain design and optimization has been covered in this blog to a great extend. The concept of design implicitly assumes that there is at least one designer, who decides how the desired “optimal” supply chain design should look like.

Defining a supply chain as a group of legally independent companies, shows that the complexity in this decision process might be drastically increased, since one has to include multiple players and their goals in the process.

In their 2005 article on “Managing Supply Networks: Organizational Roles in Network Management” Knight and Harland analyze the roles that companies can assume in this process and therefore contribute to the foundations of supply chain design.

Background and method

It has been argued that adopting the network perspective necessarily requires us to accept that the complexity and dynamics of interdependencies between network actors, resources and activities render it impossible for any one organization to manage a network […]. At best, organizations can manage within a network by developing and enacting strategies to improve their network position.

The authors employ role theory to analyze the supply chain decision making process:

Role theory’s central premise is that an actor should be viewed as a collection of roles; role theory suggests that “roles are evoked by situations and the content of roles is socially constructed”. Roles are seen as clusters of behaviours expected of parties in particular statuses or positions. In considering roles we analyse behaviour less by the characteristics of a focal organization or the network in which it is embedded and more in terms of the part the organization is playing. Taking a dramaturgical approach, roles are “like scripts which we then enact”.

Roles can be dynamically adjusted, even though “some roles are more institutionalised, and that, in this situation, the role enactor has less flexibility.”

The distribution of roles between the participants is usually not “imposed” by the context, but derived through negotiation between the elements of the supply chain.

The authors used a case study approach to analyze different roles within the supply chain. Core example was the UK National Health Service (NHS) Purchasing and Supply Agency, responsible for a budget of about GBP 2.5bn.

The purchased product portfolio is displayed in figure 1. Each of which indicates a different supply chain stream.

Figure 1: Portfolio of Purchased Material by the NHS Purchasing and Supply Agency (Knight and Harland, 2005)

To separate the different roles within the supply chain interviews were conducted with the supply chain participants and strategic “plans and activities” were identified and analyzed to deduct underlying roles within the chain.

Findings

Six roles could be identified with distinct properties.

Innovation facilitator covers promoting and facilitating product and process innovation. One team established a programme of meetings with each of the main component suppliers in a network to consider jointly their research and development plans and activities. The team’s short-term aim was to support suppliers’ efforts to reduce product costs and increase functionality, but the wider objective was to foster higher levels of investment in R & D and co-ordinate the purchaser input. In a more reactive mode, teams often respond to requests for assistance from suppliers who, for example, believe they have a product which may be adapted for use in healthcare.

At the time of the research, this role mostly involved liaison with suppliers, but relationships were also being formed with research institutions and research sponsors.

Co-ordinator is a role with two closely inter-related facets. First, portfolio teams acted as administrators or project managers of inter-organizational activities. These may be finite initiatives, for example coordinating the implementation of new EU regulations on CE marking arrangements in the prosthetic service and components network, or on-going, such as coordinating the work of the Prosthetic Strategic Supply Group which brings together representatives from across the supply base and the NHS (Harland and Knight, 2001b). Second, the role of co-ordinator can also be less formalised. In a number of networks, team members are actively involved in facilitating intra-network relations, communication and working practices.

Supply policy maker and implementer is also a two-faceted role. The Agency is charged with determining policy for supply structure and practice in the NHS (Dept. of Health, 1999), and where appropriate implementing such policy. For example, it may be appropriate for the acquisition of some goods and services to be centralised, whilst others that are currently acquired with the support of buyers in the Agency might best be sourced by personnel based in local NHS hospital Trusts. The Agency is responsible for setting standards for purchasing practice, and providing support for developing purchasing staff competence throughout the NHS. The second aspect of this role relates to determining policy on specific issues.

Advisor Portfolio teams were called upon to provide formal and informal advice to NHS hospital Trusts, Health Authorities, suppliers, the NHS Executive and government. In some cases, this was on specific supply issues; in others, members of portfolio teams contributed to, for example, working groups on wider problems, as the supply expert.

Information broker entails collating, analysing and disseminating information to various parties (as for Advisor), sometimes when requested, but often pro-actively to monitor demand and spending pat- terns, and to encourage focus on key issues.

Network structuring agent In this role, teams moni- tor and influence the structure of exchange relation- ships between the NHS and the private sector. An important element of this role is to take a sector level perspective on supply markets and acting to promote competitiveness. This can involve protecting critical suppliers from the detrimental consequences of fragmented purchasing by the NHS (e.g. peaks and troughs in demand for ambulance bodybuilding work; absence of forward planning of demand for electronic assistive technology). It also covers restructuring supply routes to interface directly with manufacturers rather than wholesalers, thus reducing costs and prices.

The roles can also overlap and so some teams can work on multiple issues at the same time.

Conclusion

Undoubtedly , this is a great descriptive framework and the roles might help to align strategy discussions internally and between companies. And therefore presents a “common language”, which can be used to facilitate the supply chain design process.

On the other hand I missed a more in-depth discussion of the role dynamics and especially how and how fast these roles might change. If relatively stable, the roles might enable a prudent supply chain participant to analyze past behavior more in depth and make predictions for future behavior. If very dynamic, even the descriptive power might be in question, since they would not provide a stable descriptor to be of value.

From the study design the core case company (NHS) seems to be large enough to provide a huge diversity of different supply chains, but on the other hand might be so large that itself is prone to assume certain roles and therefore skew the case study result to a significant degree.

Reference:

Knight, L., & Harland, C. (2005). Managing Supply Networks: Organizational Roles in Network Management European Management Journal, 23 (3), 281-292

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Design and Operation of a Hydrogen Supply Chain

Daniel Dumke — Mon, 13 Feb 2012 10:41:04 +0000

Increasing oil prices make it more rewarding to look for alternative energy sources to fuel future propulsion.
In the case of the reviewed paper today I selected one of a few papers I recently discovered on this topic. If you like to know more just let me know.The basic assumption of this paper sets hydrogen as the replacement energy storage for oil.

Oil availability peak

Expert opinions diverge on how long oil based energy reserves might last, but even energy companies only admit another 70 years. That sounds like a lot of time… But the implementation of a hydrogen supply chain, which differs to a great extend from current processes also would take decades.

Problem / Model

The authors focus their model on the hydrogen demand for transportation vehicles only. Other aspects where energy/hydrogen might be needed are not considered. The problem can be defined as follows:

The hydrogen supply chain of interest consists of: medium-to-large centralized hydrogen production facilities, transportation modes and large-scale storage facilities. We assume that hydrogen may be produced from three different energy sources: natural gas (methane), coal and biomass via two distinct types of commercially proven technologies, namely steam methane reforming and gasification. The purified hydrogen generated from the central facility has to either be liquefied or compressed before being stored or distributed. Liquid hydrogen is stored in super-insulated spherical tanks to minimize heat loss and boil-off rate, then delivered via tanker trucks or railway tank cars. In contrast, compressed-gaseous hydrogen is stored in pressurized cylindrical vessels to increase the energy density, and distributed by tube trailers or railway tube cars. The different types of storage facilities would be located either next to the production facilities or away from the production source serving as distribution terminals.

The model focusses on three strategic decisions to fulfill customer needs:

number,
location and
type and capacity of hydrogen production plants and storage facilities.

Three scenarios for the configuration are analyzed:

Distribution of liquid hydrogen with railway and truck-transportation to various storage facilities,
Distribution of the hydrogen in compressed form, and
Distribution only by tanker trucks.

The authors apply their model to the case of Great Britain and use this as a basis to estimate the demand level for transportation purposes.
Based on these assumptions the authors develop a mixed-integer linear program.
The objective function is based on the facility capital and operating cost:

The aim of the proposed model is to minimise both capital and operating costs of the hydrogen supply chain. The former are one-time costs associated with the establishment of production plants, storage facilities, and transportation links. On the other hand, operating costs are incurred on a daily basis and are associated with the cost of production of hydrogen at the plants, the cost of their storage, and the cost of their transportation through the network. Although a variety of metrics could be investigated in a more detailed study, such as well-to-wheel analysis of CO2 emissions, we focus on cost here.

Results

Figure 1 shows the selected optimal design of the supply chain for Great Britain. And figure 2 lists the overall costs.

Figure 1: Scenario 3: Optimal Network Structure (Almansoori and Shah, 2006)

Figure 2: Cost of the Hydrogen Network in three Cases (Almansoori and Shah, 2006)

The authors conclude:

The model and assumptions presented in this paper reveal that the optimum future hydrogen supply chain might consist of medium-to-large, centralized methane steam reforming plants. The hydrogen produced from these plants will then be delivered as a liquid via tanker trucks and stored in centralized storage facilities.

Conclusions

The authors already mention some weaknesses of the proposed model. The focus is on the simulation of a future state of the network, any pre-existing facilities and capabilities are basically ignored and on these grounds the “best” model is selected. But the current state might influence the future network. Also the authors do not cover how the future network should be developed and which parts should be established first.

Also, one has to note that the difference between the cost of scenarios 1 and 3 (figure 2) are quite low, the authors still decide against an additional distribution via railway: “This is because it is more favourable to use tanker trucks instead of railway tank cars for transporting liquid hydrogen due to the flexibility in operations.”
An assumption which I find questionable at least: A mixed transportation should usually combine the aspects of flexibility of a road transportation and the cost advantages of rail transport.

Reference:

Almansoori, A., & Shah, N. (2009). Design and operation of a future hydrogen supply chain: Multi-period model International Journal of Hydrogen Energy, 34 (19), 7883-7897 DOI: 10.1016/j.ijhydene.2009.07.109

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Multi-level Supply Chain Design

Daniel Dumke — Mon, 06 Feb 2012 16:48:10 +0000

The quantification of supply chain planning is the next step in the field of supply chain optimization. After operational and logistical aspects have been modeled and optimized, margins for further improvement remain slim.
Based on this premise the paper I review today suggests and tests several alternative multilevel planning approaches to gain further supply chain improvements by optimizing the mid-term supply chain design.

Case

The authors use a case of an agrochemical supply chain to establish their model and methods.
The problem can be stated as follows:

Product X (PX) is a chemical compound used as an active ingredient (AI) in several commercial herbicides. PY is chemically similar to PX, and its uses are nearly identical to those proposed for PX. They are produced by a multinational agrochemicals company.

The authors continue explaining why further optimization is dearly necessary.

A factor that has been putting enormous pressure on the low cost strategy for these products is the price of raw materials. The manufacturing methods are robust and very well established and do not leave any margin for improvement for cost cutting purposes, so the product management team turned to supply chain optimisation as a way of controlling and even reducing costs while improving service levels.

Model

Figure 1 highlights the supply chain structure of this case. The upper part shows a high-level overview, while the lower part displays the structure of the distribution network in the US.

Figure 1: Supply Chain Structure Chemical Industry (Sousa et al., 2008)

The authors employ a two-stage modelling approach to include different aspects of the planning process.

In the first stage we develop a high level planning model with a cyclic time horizon of one year (discretised into twelve months), including all the nodes in the US and worldwide networks as described above.

In the second stage, a detailed operational model is built for each month, with a time resolution of one day to assess the feasibility of the upper level plan at the operational level. […] The US manufacturing sites are described in detail and individual orders are considered.
The outputs are a detailed production and distribution plan for the US network, while accomplishing the export plan established in the first level. The second stage outputs also provide information on how to improve the accuracy of the upper level planning.

The authors then elaborate two mixed-integer linear programs tailored to the demands of the chemical industry. The short-term model is built in a way that environmental variables are used which have been set by the optimization in the mid-term model.
The results therefore can be interpreted as

The objective function of the mid-term and short-term models are the gross profits (NPV). The mid-term model also includes an additional penalty for unmet demand.

Results

For the first / base case figure 2 highlights the percentage of on time delivered products (P3, … P23). Bold numbers are below the 90% target value.

Figure 2: Deliveries on time and in full per Product and per Month for the unaligned Models (Sousa et al., 2008)

It must also be noted that the first stage and the second stage model do not quite fit together. The first stage model consistently projects a higher utilization rate than the second stage (figure 3).

Figure 3: Prediction of Resource Utilization by the first and second stage models (Sousa et al., 2008)

In a second case based on the above mentioned results the capacity of the bottle neck manufacturing sites are relieved. This leads to a slightly higher average percentage of global delivery, but on the other hand also to a lower sales figure for the US market.

Next a multi-level integration of the two different model stages is done. The goal is to use feedback from the second stage model already in the first stage.
Therefore the authors propose the following adjustments to the first stage model:

A capacity correction factor, to adjust selected capacity levels based on learnings from the second stage.
A (reduced) maximum utilization level for certain processes in the stage one model to prevent bottlenecks from happening.
Introduction of a minimum demand coverage by inventory in the first stage model.

After these adjustments the congruence of the two stage models improves and the average on time delivery rises to 97.5 % (figure 4).

Figure 4: Case 3: Increased on time an in full Deliveries compared to Base Case (Sousa et al., 2008)

Conclusion

Multi-level planning is commonly used in research and practice. In businesses very often the planning departments for strategic, mid- and short-term planning are functionally separated. And therefore communication is slowed down.
This article highlights the importance of an integrated planning approach, because if the models are not aligned the end result cannot be optimal.

The authors therefore suggest approaches to adjust the mid-term planning model to the needs of the short-term one. Overall this has quite positive effects on the results of the SC network.

On the other hand the authors neglect to argue in another direction:
The ultimate goal should not be to use more or less subjective adjustment factors and trail-and-error to force the mid-term / first stage model in the right direction, but to integrate supply chain modeling altogether.
Only a fully integrated and comprehensive model can result in real optimization. Of course this would require a whole new, joint planning approach.

Reference:

Sousa, R., Shah, N., & Papageorgiou, L.G. (2008). Supply chain design and multilevel planning—An industrial case Computers and Chemical Engineering, 32, 2643-2663

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The Impact of Supply Chain Integration on Performance

Daniel Dumke — Mon, 30 Jan 2012 14:09:15 +0000

An ongoing debate in supply chain management is about the degree to which companies should collaborate with their supply chain partners. In business and research the concept is called supply chain integration and may also be a useful strategy for reducing certain risks. And of course it is an often used strategy in supply chain management in general
Analyzing the effect of supply chain integration on performance therefore is an important issue in SCM research.

Supply Chain Integration

In the case of this study supply chain integration (SCI) can be defined as

the degree to which a manufacturer strategically collaborates with its supply chain partners and collaboratively manages intra- and inter-organization processes. The goal is to achieve effective and efficient flows of products and services, information, money and decisions, to provide maximum value to the customer at low cost and high speed.

The authors split SCI in three dimensions: customer, supplier and internal integration.

Internal integration and external integration play different roles in the context of SCI. While internal integration recognizes that the departments and functions within a manufacturer should function as part of an integrated process, external integration recognizes the importance of establishing close, interactive relationships with customers and suppliers.

Methodology

The paper employs a survey design. Participants of the survey were manufacturing companies based in China.

Because of China’s size and economic diversity, we strategically selected five cities representing different stages of economic development.

Overall 4569 companies were contacted, 1356 questionnaires were distributed with a result of 617 usable replies.
Figure 1 shows profiles of the responding companies.

Figure 1: Company Profiles

The authors suggest the following hypothesis.

H1a. Internal integration is positively related to the operational performance of the manufacturer within a supply chain.

H1b. Internal integration is positively related to the business performance of the manufacturer within a supply chain.

H2a. Customer and supplier integration are positively related to the operational performance of the manufacturer within a supply chain, given the relationship between internal integration and operational performance.

H2b. Customer and supplier integration are positively related to the business performance of the manufacturer within a supply chain, given the relationship between internal integration and business performance.

H3a. Customer and supplier integration will moderate the relationship between internal integration and operational performance.

H3b. Customer and supplier integration will moderate the relationship between internal integration and business performance.

H4. An emergent taxonomy of manufacturers can be developed, based on their patterns of supplier, internal and customer integration.

H5a. The patterns of SCI are related to the operational performance of the manufacturer within a supply chain.

H5b. The patterns of SCI are related to the business performance of the manufacturer within a supply chain.

Results

The analysis of the survey offers several interesting results, these are the main points:

We found that most of our hypotheses were supported or partially supported, broadly indicating that SCI is related to performance.

Internal integration was directly related to both business and operational performance and that customer integration was directly related to operational performance.

Although supplier integration was not directly related to either type of performance, the interaction of supplier and customer integration was related to operational performance.

Our findings indicate that supplier integration is important to performance, but in a different way than internal and customer integration are.

Since the objective of SCI is to provide maximum value to the customer, the measurement of operational performance is necessarily customer-oriented.

Overall, the contingency approach reveals that internal integration forms the foundation upon which customer and supplier integration build. This suggests that companies should begin SCI with internal integration, laying the foundation for customer and supplier integration.

The configuration approach showed that the overall SCI construct was related to both operational and business performance.

The configuration approach also reveals that the effect of SCI is cumulative, providing significant insight for companies in implementing it.

Conclusion

The study shows that all three dimensions of supply chain integration are important for the performance of a manufacturing firm across industries. And China as the modern manufacturing hub is a great place to do research on this topic.

Reference:

Flynn, B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach Journal of Operations Management, 28 (1), 58-71 DOI: 10.1016/j.jom.2009.06.001

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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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