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After the 2004 tsunami, which heavily affected parts of Thailand and Indonesia, national and international disaster response was quick to support the affected regions.

Within several weeks of the disaster, approximately 400 international non government organizations ( NGOs) were working in Indonesia alone providing basic assistance to the affected population.

Introduction to disaster relief

Several factors are necessary to improve response activities:

  • Preparedness in vulnerable regions, focussing on the “ability to respond quickly and appropriately”.
  • Local involvement. The local authorities and population directly involved in a disaster also are “in the best position to respond immediately” to a disruption.
  • Coordinated needs assessment, which also includes local groups ensures that support can be given on efficiently where required
  • Information sharing: “Emergency preparedness and response stages are driven by information”. Therefore sharing information between the disaster response parties is an important factor to improve overall outcome.
  • Logistics expertise and efficiency. Natural disasters often leave most critical infrastructure destroyed. To quickly support a large number of road access is of utmost importance. “Logisticians play an important role during the initial emergency period, they are often given limited authority to carry out their decisions. Frequently too, the assessment teams sent by humanitarian agencies to determine the needs of the affected population do not include logisticians. When logisticians are not included in the planning and decision-making process this causes delays in distributing relief.” Also local logistics expertise should be levered to further foster the speed of delivery.


To build her disaster response model the author conducted several interviews with disaster relief managers involved in the 2004 tsunami. The goal was to assess the degree of execution of the above mentioned factors.
The interviews were carried out with of five NGO and government managers (figure 1).

Interviewee group
Figure 1: Interviewee Sample Group (Perry, 2007)

The author summarized shortfalls in several key areas, including preparedness, local involvement and coordination (figure 2).

Shortfalls in effective tsunami response
Figure 2: Disaster Response Shortfalls during the 2004 Tsunami (Perry, 2007)


Drawing from the cumulative findings of the extensive pre- and post-tsunami literature analysis and the research findings, a hindsight model of effective natural disaster response management planning has been developed that is holistic and inclusive.

Figure 3 summarizes the relevant stakeholders and tasks.

Effective response as part of holistic, inclusive natural disaster management planning
Figure 3: Holistic Model for Effective Disaster Response (Perry, 2007)

Key elements include:
  • the rigorous monitoring and forecasting of natural disaster risk and mitigating the effects of an impending disaster, with risk reduction activity, natural hazard forecasting, adoption of viable early warning systems;
  • the building of awareness through high profile, broad-based disaster planning and awareness programs led by the local government and building networks and trust;
  • the addressing of demographic vulnerability, poverty and long-term, sustainable livelihoods;
  • the linking of all stages from forecasting, warning, mitigation, response and recovery to community development for resilience;
  • the incorporation of disaster management protocols, social policy, international support, training programs in logistics and response, simulation programs, empowerment of local communities and encouragement of improvisation in chaotic scenarios; and
  • assurances that there is adequate funding for all facets of natural disaster management and reducing risk, with suitable early warning systems and protocols, the development of a cadre of local expertise, particularly in the field of logistics as well as planning for a positive future for vulnerable communities.


I presented this model for two reasons. First, for its inclusion of the logistics aspects of humanitarian disaster relief efforts. Second, for the aspects which might be transferable a business situation.
The model especially highlights the need for quick information by extensive communication and local knowledge and capabilities to deal with disasters swiftly.

One caveat I would like to mention, even though the study and the model are backed extensively using related literature, I was missing a broader empirical foundation of the work. Five interviews (as in-depth as they may be) just are not enough to build a reliable model. Further testing is therefore required!


Perry, M. (2007). Natural disaster management planning: A study of logistics managers responding to the tsunami International Journal of Physical Distribution & Logistics Management, 37 (5), 409-433 DOI: 10.1108/09600030710758455

Originally posted by Daniel Dumke at

This week we’ll keep it short.
Even though I stumbled upon several articles this week, I found only few useful to post them here.

What I did find were three nice videos by Elsevier (huge publisher in science literature) on the publishing process and how to get your paper/findings published ( Elsevier: 1, 2, 3)

Already tweeted

Slow tweets as well this week with yet another video. You can follow me on twitter.

Have a nice weekend!


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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.

Case organization purchasing portfolio structure
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.


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.


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.


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

Originally posted by Daniel Dumke at

I hope you had a good week. My simulation model gets its final touches, so that all my scenarios run smoothly and (hopefully) produce some interesting results.
Last week I started with some basic supply chain scenarios like serial, convergent and divergent — and I liked the results. The next step will be to include some more demanding structures. First will be a consumer goods supply chain as highlighted here in the blog.

And this is what others wrote about this week.

  • Jan Husdal had a look at the WEF report on “Supply Chain and Transport Risk” and summarizes the key figures and conclusions. (
  • Tim Cook talked about the Apple supply chain and also the recent litigations regarding the working conditions within their supplier companies. One of the conclusions is to publish monthly updates on the changes within the supply chain. ( ZDNet)
  • The OR Blog reported on possibly upcoming legislation relevant to supply chain resilience and the ISO 28002 “Standard for Resilience in the Supply Chain”. ( Operational Risk Management Blog)
Already tweeted

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

Have a nice weekend!


Originally posted by Daniel Dumke at
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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.


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

Network structure of liquid hydrogen produced via medium- to-large steam methane reforming plants, stored in medium-to-large storage facilities, and distributed via tanker trucks.
Figure 1: Scenario 3: Optimal Network Structure (Almansoori and Shah, 2006)

Breakdown of total hydrogen network costs.
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.


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.


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

Originally posted by Daniel Dumke at

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

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

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

  • “Flexibility and risk management” is the topic of David Simchi-Levi’s session at the CSCMP Annual Conference.
  • You’re So Predictable. Daniel Kahneman and the Science of Human Fallibility
  • How do I manage risks associated with my supply chain?

Enjoy your weekend!

Originally posted by Daniel Dumke at
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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.


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.


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.

Supply Chain Structure Chemical Industry

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.


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.

Deliveries on time and in full per Product and per Month
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).

Prediction of Resource Utilization by the first and second stage models
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).

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


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.


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

Originally posted by Daniel Dumke at

This week I draft-finished my second to last chapter of the dissertation. But I saved the best for last so beginning on Monday I will summarize the key results of my simulation study on disruptions in network design.
I still found the time to browse the web and these are the articles I found interesting this week.

  • On the research front have a look at the article by Andreas. He highlights a new literature review on the different types of methods used. Conclusion: “Particularly, the article confirms my suspicion that there is “shortage of studies conducted on the supply chain as a network of enterprises“. Instead, most research turns out to focus on a single enterprise or on the relationships of a single enterprise with its suppliers or customers”. ( SCM Research)
  • Furthermore I discovered this new paper on a related topic: how greening the supply chain design impacts cost and environmental footprint. “The results indicate that in most cases using shared warehouses from Third Party Logistics operators improves both the cost and the environmental performance of a company. In all cases shared use of transportation operations minimizes the amount of CO2 and PM emissions generated, while dedicated use minimizes costs”. ( Sciencedirect)
  • Finally, I stumbled upon a blog by Professor Tim Zak of the Carnegie Mellon University, which since 2011 contains contributions by him and several students of his. I for example liked the summary of “IKEA’s intelligent packaging implies LEGO’s bricks”. ( CMUSCM Blog)
Already tweeted

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

Enjoy your weekend!


Originally posted by Daniel Dumke at