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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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Information Overflow - This Week in Supply Chain Management (20 / 2012)

Daniel Dumke — Fri, 18 May 2012 08:47:32 +0000

This week was filled with preparations for our summer vacation. This year we are going to Norway. We will start start in Bergen on the west coast and for the first week stay in a small vacation home nearby Sand directly at the Fjord. Later on we will drive to Oslo (east coast) passing Hardangervidda National Park. On our way back we will use a more northern route, but that’s still in planning.

This week I found three really nice articles for you to read. Have a look!

Articles

The resource-based view supports the notion that a company can generate competitive advantages from the resources it controls. Andreas Wieland points to an article which discusses the question if a supply chain can be viewed as a company’s asset as well. (SCM Research)
Enterra Insights talks about the obstacles to thorough supply chain risk analyses. Despite the growing need for a more rigorous risk management approach many companies get overwhelmed by the complexities involved. (Enterra Insights)

Software

Infosys put a short presentation online, covering the basic risk concepts included in their SCRM Product Suite. (Infosys)

Enjoy your weekend!

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Battery Risks - This Week in Supply Chain Management (13 / 2012)

Daniel Dumke — Fri, 30 Mar 2012 07:28:05 +0000

This week I finally finished the first complete version of my dissertation. All graphs and tables now are now built using the same template and I finally decided on the final typeface: For the main texts it’s Gyre Bonum which is built on the Bookman typeface) and for the headlines I choose Bera Serif a nice slab serif font.
Font selection is a really hard process and after reading so much I could probably write a long article about it, but I am looking forward seeing the results in print! If you happen to know a good publisher willing to print it, I am still open for suggestions.

Looks are not everything of course so I am looking forward to my first full-content feedback by our post-doc next week and if everything goes well I will be handing in the first copy for comments to my professor after the easter holidays.

Now let’s get back to business.

Articles

Enterra Insights discusses the use of information for risk reduction in a broad context, referencing also one of my articles on knowledge-based supply chain risk management (Enterra Insights)
Supply Chain Quarterly links to a white paper on “Collaborative Outsourcing: Preparing for a Successful Logistics Outsource” (C.H. Robinson)
This article at the National Alliance for Advanced Technology Batteries (NAATBat) talks about the implications of supply and demand disruptions of the battery supply chain (NAATBat)

Already tweeted

These are the tweets from this week. You can follow me on twitter.

Risks To Your Business Supply Chain: Key Strategies for 2012 http://t.co/OXr4wlZz
Professor Martin Christopher focuses on the major supply chain challenges that face managers via @BWBX http://t.co/anp5ZVeg

Enjoy your weekend!

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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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Model for Optimal Regulation for Risk Reduction

Daniel Dumke — Mon, 06 Jun 2011 13:54:00 +0000

As in several of my former posts (1, 2, 3, 4, 5, 6) sometimes I deviate a little from the extreme focus on supply chain risk management, to think about related topics, like today, where I want to highlight the effects of regulation to minimize risks. This article was already written in 1984 by Steven Shavell and it can be downloaded at Harvard University.

Study background

The author, Steven M. Shavell, is a law professor at Harvard law school and associated with the theory of Welfarism.

In his 1984 paper with the title “A model of the optimal use of liability and safety regulation”, he elaborates on the question of how a society should optimally enact legislation to reduce risks on a societal level.

Liability or safety regulation

The model considers accident risks. The two means for reducing this risk, which can be used by legislators, are to a) establish binding safety regulation (e.g. safety standards) or b) establish penalties for liabilities apart from the safety regulation.

Model

Shavell disproves the term “judex non calculat” (judges don’t do math), and establishes a mathematical model with the parameters

level of care (x),

probability of an accident (p(x)) and

the magnitude of harm contingent on the accident happening (h)

Results

The author shows that the level of care with only liability as a measure is in general restricted by the assets of the company and lower then the optimal care (see figure 1).

Figure 1: Level of Care when using only Liability as Risk Mitigation Strategy (Shavell, 1984)

The resulting care under optimal regulation is on the other hand restricted by the likelihood of being caught of non-compliance and the assigned penalties. The comparison of the two measured is displayed in figure 2.

Figure 2: Level of Care when using only Safety Regulation as Risk Mitigation Strategy (Shavell, 1984)

Since both measures do not show optimal results Shavell suggests a combination of both strategies to mitigate the risk and shows that this solution is superior to a single strategy approach in all cases (see figure 3).

Figure 2: Level of Care when using a combination of Safety Regulation and Liability as Risk Mitigation Strategies (Shavell, 1984)

Conclusion

What we see in most societies that there already exists a combination of both measure to reduce the risk of accidents. In most companies liability is still upheld even if current security standards are met. The paper therefore puts emphasis on the point of that both measure should be considered as basis for new legislation.

The paper can also be used to analyze the current state of the art in accident prevention in different legislation systems or industries and deduce findings for further legislation.

Two examples:

Germany has a huge emphasis on setting security standards, whereas on the other hand corporate liabilities compared to many other countries is fairly minimal.

Due to their high impact of nuclear catastrophes the nuclear power industry has a risk of high liabilities. This risk on the other hand is restricted by the fact that, if an accident happens the cost often outweigh the assets of the company leading to a default. Therefore sole reliance on the liability mitigation probably does not have a huge effect on the security level. As a result legislation has to emphasis the security standards to achieve an adequate level of care.

Reference:

Shavell, S. (1984). A Model of the Optimal Use of Liability and Safety Regulation The RAND Journal of Economics, 15 (2) DOI: 10.2307/2555680

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Information Sharing in Supply Chains

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

Obstacles to Information Sharing

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

Drivers of Information Sharing

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

Types of shared Information

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

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

Forms:

Continuos replenishment programs
Vendor managed inventory

Problem:

Information validity can be a huge problem

Sales Data
Why:

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

Order Status for Tracking/Tracing
Sales Forecast
Why:

Elimination of the Bullwhip effect and the associated inefficiencies.

Production/Delivery Schedule
Why:

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

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

Models of Information Sharing

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

Conclusion

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

Reference:

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

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Risk, Information and Incentives in Telecom Supply Chains

Daniel Dumke — Wed, 09 Feb 2011 16:26:00 +0000

Supply chains risks can also be analyzed in a specific industry context and this is exactly what Agrell et al. (2004) did with telecom supply chains. They used a three tier SC (2nd tier supplier, EMS, OEM) to include the selection, coordination and motivation of independently operating suppliers in the model.

In terms of risk handling and sharing the telco industry is to some extent unique as well; there are several possible complications, like

differing business logic in the different stages,
individual relations between revenue and product life cycles
design of products and processes and
a lack of effective incentive structure to induce global supply chain optimization promotes the opportunistic and myopic behavior of the chain firms.

Figure 1: Historical Development within the Telco Industry (Agrell et al., 2004)

Telco Industry

The telco industry can be described using the following dimensions.

Figure 2: Diverging Business Logic in the Telco Industry (Agrell et al., 2004)

Demand uncertainty
- Level
- Timing
Changing Roles
- Outsourcing
- Unclear Interfaces
Growth vs. Consolidation
- Shifting power balance due to consolidation at supplier level
Heterogeneous Business Logic (see Figure 2)
- The cost and revenue functions are different

Model

The authors developed a three stage model with the 2nd tier supplier (for components), the EMS (Electronics Manufacturing Services Provider) and the OEM (Original Equipment Manufactur) in focus (gray box in Figure 3).

Figure 3: Base Supply Chain Model (Agrell et al., 2004)

The experiments with the model take place using a two period time horizon. The OEM tries to maximize the profit over this period.
Knowledge about the system is explicitly divided between the participants: Common knowledge: current demand, Supplier: its cost function and investment opportunities.
Information can be shared: The OEM can send a forecast to its suppliers and it sends order quantities for the first and second period to its suppliers.
The following alternative scenarios are then compared:

Centralized model
OEM coordinates with asymmetric information
Coordination by EMS

Results

The differences between the scenarios are quite low, nonetheless the following conclusions can be drawn:
The performance of the supply chain as a whole may be undermined by the shifting positions of bargaining strength (towards the suppliers).
And the model shows furthermore that simple price-quantity-only coordination (without long-term contracts) as some shortcomings which leads to reduced performance as well.

Conclusion

One question the article did not answer: Why specialize on the Telco industry? The above mentioned description of the industry might fit on others as well, so it would have been more straight forward to develop the model based on the abstract scenario description and use the telco industry as a case study.
Contrary to the description, the model is basically a four-tier model. Since also the customer of the OEM is taken into account as the final source of demand.
But in my view what stands out is not the number of tiers in the model, but the very explicit modeling of the knowledge distribution. Many papers only take a very brief detour on the knowledge that each participant has, or leave it out entirely. But not only for the quality of the model, but also its reproducibility it is important to take the information distribution into account.

Reference:

Agrell, P., Lindroth, R., & Norrman, A. (2004). Risk, information and incentives in telecom supply chains International Journal of Production Economics, 90 (1), 1-16 DOI: 10.1016/S0925-5273(02)00471-1

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Managing Information Risks

Daniel Dumke — Wed, 17 Nov 2010 12:32:00 +0000

At the moment I am looking for gaps in my reading up to now and I found that I have not read much about information risks. It also seems that those risks are not (yet?) in focus, neither in research nor business. So I was happy to find “Information risks management in supply chains: an assessment and mitigation framework” by Faisal, Banwet and Shankar.

Topic / Methodology

The purpose of the paper is to provide a framework for the management of information risks, and to device a way to measure them. The authors therefore use graph theory and interpretive structural modeling to model the interrelations between risks and their drivers and develop a framework for mitigating the risks. They start categorizing information risks as follows:

Information security / breakdown risks (I1)
forecast risks (I2)
intellectual property rights risks (I3)
IT/IS outsourcing risks (I4)

Figure 1: Model for Information Risk Mitigation in a Supply Chain (Faisal et al. 2007)

Framework

The authors next developed the variables which help to reduce information risks using brainstorming sessions. This resulted in 12 variables which where then analyzed using above mentioned methods.

Figure 1 shows the resulting framework. I found it especially interesting that a “soft factor” like management commitment had such an integral role in this framework, pointing the finger on something that business often neglects when managing risks.

Risk Measurement

The framework implies a focal role of measuring the impacts of different risks. The authors conclude:

Measurement of information risks is important to understand their contribution to overall risk susceptibility of the supply chain, and also to determine the impact of the efforts to mitigate them.

With this in mind the authors first determine the interrelationships between the above mentioned risks and then suggest the formula of their “information risk index”.

Figure 2: Representation of the four Information Risk Variables (Faisal et al. 2007)

Figure 3: Information Risk Index of the Supply Chain (Faisal et al. 2007)

Conclusion

I liked reading this article and I agree with the conclusions. When analyzing the results, you also have to keep in mind that this work has not been based on a widespread consensus or even tested empirically.

Reference:

Faisal, M., Banwet, D., & Shankar, R. (2007). Information risks management in supply chains: an assessment and mitigation framework Journal of Enterprise Information Management, 20 (6), 677-699 DOI: 10.1108/17410390710830727

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