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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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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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An Empirical Investigation into Supply Chain Vulnerability

Daniel Dumke — Tue, 05 Jun 2012 06:30:56 +0000

One basic assumption in risk-aware supply chain design is the notion that the design of the supply chain actually has an impact on the vulnerability of the supply chain.
This question has been analyzed about six years ago in a broad empirical study by Wagner and Bode.

Method

The authors use a rather large sample of companies in Germany. Overall nearly 5000 supply chain professionals were asked to participate and 760 actually took part in the study.
Most sample companies had an industrial focus (72% versus service (20%) and trade (9%)).
This study is founded on a similar sample as this other study by Wagner and Bode analyzing the impact of risks on supply chain performance.

Model

The author focus on some supply chain design variables, which supposedly increase supply chain vulnerabilities. Figure 1 shows the assumed relationship between those drivers of supply chain vulnerability and three supply chain risk categories.

Figure 1: Concept: Relationship between Design and Risk

The authors propose the following hypothesis, which are then tested using the empirical data:

H1: The higher the drivers of supply chain vulnerability, the higher the level of demand side risk a firm faces.

H2: The higher the drivers of supply chain vulnerability, the higher the level of supply side risk a firm faces.

H3: The higher the drivers of supply chain vulnerability, the higher the level of catastrophic side risk a firm faces.

Results

The results show that all hypothesis are supported by the findings of the authors. However the design factors/vulnerabilities only explain part of the observed supply chain risks (7% for H1, 13% for H2, 3% for H3).
Demand side risk was influenced by strong customer dependence and strong supplier dependence.
Supply side risk was influenced by supplier dependence, single sourcing and global sourcing.
Lastly, catastrophic risk was impacted by the degree of global sourcing.

The authors draw the following conclusions:

First, the supply chain vulnerability variables in our model explain a rather small portion of the variance in the risk arising from demand side risk sources. It is a low but not astonishing value since the majority of the vulnerability variables focuses on the upstream supply chain. However, the results reveal that customer dependence increases demand side risk. This finding indicates that firms that are dependent on some customers are exposed to a higher risk of suffering from the detrimental effects of demand volatility and poor downstream information. This could be because of order batching or limited possibilities of demand pooling. […] This leads to the hypothesis that, beyond the investigated variables, there are several additional aspects both internal and external to the supply chain that determine a firm’s exposure to supply chain risk.

Second, risk derived from supply side risk sources is elevated by supplier dependence, single sourcing and global sourcing. Supplier dependence obviously amplifies the threat from poor quality, supply shortages, sudden demise of one of these suppliers, and poor logistics performance. Although this argumentation also applies to single sourcing, the single sourcing approach seems to be less hazardous than general dependence on some suppliers. This is because single sourcing is usually aligned with a closer relationship that might absorb some of the supply side risk.

Third, when it comes to risk from catastrophic risk sources it has to be taken into consideration that the sample data was collected in Germany which has been a very “calm” place with regard to disasters. Here, it is solely global sourcing that is a significant factor that exposes firms to higher risk from catastrophes. The implementation of a global sourcing strategy stretches the supply chain geographically which ultimately means more peril points for the information and material flow. The robustness and resilience of regional or national supply chains is usually higher. Surprisingly, the study shows that supplier dependence decreases the risk exposure to catastrophes. Again we would argue that this is because of lack in supply flexibility. Firefighting against the consequences of catastrophic events might be more successful with the ability to quickly adjust the supply.

Conclusion

I would argue that it is always hard to measure risk consistently in a qualitative study. People are likely to evaluate the same risk quite differently, which might lead to unclear results.
Furthermore the low impact of these specific design variables emphasizes the view that there are many more factors (internal and external) that impact the exposure to supply chain risk.

Reference:

Wagner, S.M., & Bode, C. (2006). An Empirical Investigation into Supply Chain Vulnerability Experienced by German Firms Erich Schmidt Verlag, 79-96

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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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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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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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Optimal Design of Supply Chain Networks with uncertain Demand

Daniel Dumke — Mon, 12 Dec 2011 16:02:00 +0000

This week is dedicated to the works on supply chain management from Greek supply chain researchers. Today’s article has been published in the Journal of Management Sciences (Omega) by four researchers from northern Greece and the UK.
After my last reviews which focused more on the conceptual aspects of supply chain risk and management. This paper is again more hands-on in the sense that it describes a mathematical model which integrates supply chain design and uncertain demand and therefore leads to a more robust supply chain design.

Method

The authors propose a mixed-integer linear program to solve a strategic supply chain design problem. Strategic design decisions include:

Where to locate new facilities (be they production, storage, logistics, etc.).

Significant changes to existing facilities, e.g. expansion, contraction or closure.

Sourcing decisions – what suppliers and supply base to use for each facility.

Allocation decisions – e.g., what products should be produced at each production facility; which markets should be served by which warehouses, etc.

Figure 1: Location and possible Locations of Plants and other Facilities (Georgiadis et al., 2011)

Model parameters

To be useful supply chain models usually are limited to a specific supply chain context. In this case the goal is to select an optimal design as well as some tactical / operational parameters.

Figure 1 and 2 describe the location design aspects of the model. The locations of plants and customers are fixed; for the warehouses and distribution centers a set of possible locations is given, and the optimal location has to be selected from the sets.

Figure 2: Locational Limitations to the Supply Chain Design Decisions (Georgiadis et al., 2011)

There are several more constraints implemented, which are concerned with the transportation flows, production resources, safety stocks and capacities. Inventory can be held at different locations, which is solved during the optimization of the model.

The objective is to minimize expected total cost over the planning horizon.

Uncertainty

There are two basic options to integrate uncertainty into a mathematical model:

scenario approach, which discretize the uncertain parameters into a limited number of specified scenarios, or a
probabilistic approach, using stochastic programming.

The authors select the first approach:

In this paper, we adopt a scenario planning approach for handling the uncertainty in time varying product demands. A question that needs to be addressed in this context concerns the generation of the scenarios to be considered. It is, of course, possible to assume that the demand for each product in each customer zone is an independent random parameter. However, more realistically, demands for similar products will tend to be correlated and will ultimately be controlled by a small number of major factors such as economic growth, political stability, competitor actions, and so on.
The complexity of the overall-model is then dependent on the complexity of the basic model (e.g. number of possible connections and locations) and the number of selected scenarios.

Two kind of decisions must be considered in the model: here-and-now decisions (the “really strategic ones”), those have to be selected before any more knowledge about the outcome of the uncertainty can be obtained. The wait-and-see decisions are those which can be altered during a model run. The concept is shown in figure 3.

Figure 3: Types of Decisions in a Strategic Model: ‘here-and-now’ and ‘wait-and-see’ (Georgiadis et al., 2011)

Case study

The authors then set the parameters for the model using an “European wide production and distribution network comprising of three manufacturing plants producing 14 different types of products and located in three different European countries, namely the UK, Spain and Italy” (see figure 1).
The proposed demand volume is given in four scenarios for all customer areas and products. Two cases are compared: one with low safety stock and another one with a general higher safety stock level.
The resulting optimal supply configuration for the high inventory case is shown in figure 4.

Figure 4: Optimal Solution of the Supply Chain Case in the high Safety Stock Setting (Georgiadis et al., 2011)

Conclusion

The authors keep up to their promise and delivered a quite detailed model description and its results. But still, would it be possible to reproduce their results or rebuild their model using this data only? Very unlikely.

Even though the model is detailed. There is still a lot of information missing about the specific parameters used and the interconnections in the model. One major factor in the scientific acceptance and validity of research is the reproducibility of the results. And sadly, that’s one of the reasons, why complex models are still not commonly presented in renowned journals.

In my opinion the only chance to circumvent this obstacle is not only to publish the article, but also the complete model source code and the parameters used.

In the article these omissions are necessary to stay below a certain page limit – the authors already had to distribute the result charts of their case study throughout the paper to have a chance to include the most relevant ones.

But beside these necessary exclusions, I found that some other things would have been interesting to read about.

Demand risk: For a strategic (i.e. long term) model and so many different demand centers, I think only four demand scenarios might be too few to represent reality in a sufficient way. More scenarios could have been included, since the CPU time it took to calculate one optimal solution was quite low (some hundred seconds only).
Other risks: Furthermore it would have been interesting to analyze the effects of other risks in the model, but they were omitted as well.
Overview: I was also missing a short general overview over the given scenarios.
Sensitivity analysis: Lastly, the validity of a model can be further improved by analyzing the sensitivity of the model towards parameter change. The authors did not omit this point, but they choose to test and present only two deviations from their original model parameters, which I think is too little to assess the validity of the model sufficiently.

I think my conclusion can be summarized as follows: It is definitely hard to present a complex supply chain model in a way which sustains the validity and reproducibility of the results. But, since the description of the model is quite elaborate, this paper can still be a great source and foundation for one’s own strategic supply chain model.

Reference:

Georgiadis, M.C., Tsiakis, P., Longinidis, P., & Sofioglou, M.K. (2011). Optimal design of supply chain networks under uncertain transient demand variations Omega, 39 (3), 254-272

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Exception Handling for Robust Supply Chain Design

Daniel Dumke — Wed, 07 Dec 2011 18:33:00 +0000

This article considers the design of robust supply chains from the viewpoint of exception handling. Disruptions of the supply chain happen all the time. Smaller disruptions like quality issues are part of the daily business; but a look at the recent ten years shows that large disruptions, happen more often as well (think of terrorist attacks or earthquakes). “Thus, exception management is an important issue in global supply chain networks.”

Basics and definition

If one accepts the fact, that exception cannot be eliminated completely there are two ways to treat them: preventive and interceptive. Figure 1 shows different exception management strategies.

Figure 1: Exception Management Strategies (Gaonkar and Viswanadham, 2003; click to zoom)

The authors define a supply chain exception as the opposite to the classical seven-Rs of logistics:

We can use this description to define a supply chain failure or exception occurring whenever the supply chain deviates from any one of the […] required specifications – either in terms of delivering the wrong product, in the wrong quantity, in the wrong condition, at the wrong place, at the wrong time, at the wrong cost and to the wrong customer.

Exception management

In this case exception management is addressed “at the strategic level through the preventive selection of supply chain partners that mitigate risk in the network.”

In the first step the consequences of a given exception (here: supplier non-performance) are analyzed. The authors use a Cause-Consequence-Diagram for this end (figure 2).

Figure 2: Cause and Consequence Diagram for one Example (Supplier Non-Performance) (Gaonkar and Viswanadham, 2003; click to zoom)

Then, “given the probability of occurrence of the initiating event, which is supplier non-performance, and the probabilities for the various intermedialy events, we can calculate the probability of occurrences for each of the end states or outcomes. Furthermore, each of these end states may result in different levels of supply shortfalls and financial cost. Hence, given the probability of each end state and the supply shortfall or financial cost for each end state, we can calculate the expected shortfall or financial risk for the non-performance of a given supplier.”

If repeated for each supplier, the supplier with the least expected impact can be identified.

Model

To implement their approach the authors develop a small linear mixed-integer model.

The model was formulated in Microsoft Excel and solved using the Solver add-in. The model was solved for a problem with a single manufacturer (located in the US), dealing with 5 suppliers. The probabilities of supplier disruption for all the suppliers (individually and in various combination) were considered as given [the first part is shown in figure 3]. The relation cost was taken as $5000 and the quantity required by the manufacturer was 520 units.

Figure 3: Scenario / Probability Table (Gaonkar and Viswanadham, 2003)

Conclusion

The presented approach makes it easy to include uncertainty into the supplier selection process. Considering the moderate efforts necessary for implementing the supplier assessment and implementing the model, I would like to read more about the effectiveness in a real business situation.

Reference:

Gaonkar, R., & Viswanadham, N. (2003). Robust Supply Chain Design: a Strategic Approach for Exception Handling Proceeings of the 2003 IEEE International Conference on Robotics & Automation, 1762-1767

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Matching Product Architecture with Supply Chain Design

Daniel Dumke — Wed, 19 Oct 2011 15:31:00 +0000

This review is about a preprint article which already has been accepted for publication by the “European Journal of Operational Research”. But since there is only a limited space for articles in each issue of the journal, final publication of the article is delayed.
One could now argue in a general note that this behavior also signifies a delay of the progress of supply chain research over all, with all its negative long term effects. Furthermore, in the days of the internet journals should not limit themselves to an artificial (article) limit, but see only the sky or in this case the number of quality publications as their limit.
But in this case I was able to gain access to an early copy.

Product design & supply chain management

I already wrote several times (1, 2, 3, 4) on how product architecture and supply chain design could be integrated, so this is not really a new topic. But it also touches a more integrative approach of supply chain management.

Building from the early models, where a supply chain could easily be defined by a handful of properties, supply chain models nowadays reach a new level of complexity. One of these extensions is the integration of supply chain activities into the product development process, which is supposed to yield (if we trust case studies like IKEA) enormous benefits in every performance aspect of the supply chain and ultimately the company.In the study reviewed Nepal, Monplaisir and Famuyiwa first develop a conceptual and mathematical model to integrate the supply chain design decisions into the product development process. In a second step this model is then tested using two case studies.

Model

The authors suggest an integrated three step process: 1) selection of product architecture, 2) evaluation of potential suppliers, and 3) optimal configuration of supply chain.

The first step includes the development of a generic bill of materials (GBOM) for every possible product architecture scenario. Figure 1 shows the GBOM for an example Product X.

Figure 1: Generic Bill of Materials for an exemplary product (Nepal et al., 2011)

This product design also leads to a similar generic supply chain structure which is shown in figure 2.

Figure 2: Corresponding Generic Supply Chain for Product X (Nepal et al., 2011)

The second step focusses on the identification and evaluation of available the supply chain participants. Necessary information, like production cost, lead-time and compatibility index has to be collected.
The compatibility index itself is developed by the authors as well and contains information on the compatibility of this potential supplier regarding the structural, managerial and financial dimension. Each of the dimensions is then weighted to generate the complete index. Since the compatibility of the future suppliers is hard to judge before the product is even designed, the authors employ fuzzy logic, allowing them to use ranges for each of the compatibility indices instead of fixed numbers.

The last step aims to find the optimal supply chain configuration. This is done for each product architecture scenario by using a linear goal programming model, where total supply chain cost are minimized while maximizing the compatibility between the partners.

Case studies

There are two case studies, one on bulldozer assembly and manufacturing and the other on an automotive climate control system. I will only talk about the first case since I found it more interesting.

For the case the authors exercise the three steps mentioned above. First they design two different product architectures (integral and modular architectures; figure 3) and the corresponding generic supply chain networks (figure 4).

Figure 3: Case Study: Generic Bill of Materials for Bulldozer Manufacturing (Nepal et al., 2011; click to enlarge)

Figure 4: Case Study: Generic Supply Chain Network for Bulldozer Manufacturing (Nepal et al., 2011; click to enlarge)

Lastly, the authors collect the necessary information of the potential suppliers regarding cost, lead-time and ranges for the compatibility index for two different process options:

Two alternatives are considered for each node. If the node is a procurement stage, the first alternative represents the standard supply option (that is, the existing procurement arrangement). The second option represents a consignment option where the supplier is responsible for providing immediate delivery to the bulldozer line. Similarly, for the assembly node, the first option represents the standard manufacturing method while the second option represents an expedited alternative that corresponds to a supplier who has invested in process improvement efforts in order to decrease its supply lead-time.

After solving the model in the next step, the authors conclude:

In the majority of the bulldozer supply chain stages for a modular architectural design, option two has been selected. While it is more expensive than option one, option two has a lower production lead-time and higher compatibility ratings for all stages because its modularity increases the degree of dependency, based on relative costs of inputs, between supply chain nodes.

Compared with base results, where only supply chain costs are considered, this model delivers a solution which performs (slightly) worse in cost (+0,36%), but on the other hand has the potential to deliver a much more balanced and robust solution considering also compatibility issues.

Conclusion

I found the paper to be a great read and I cannot add anything else to the issues the authors already detected and mentioned in their own conclusion:

Risk factors are missing in the model (e.g. random service times)
Additional factors like sustainability and flexibility should be considered
It is very obvious that this is again a supply oriented model, so it would be great to see how distribution network decisions behave when including product design decisions (since the customers might also change depending on the product design decisions)

Reference:

Nepal, B., Monplaisir, L., & Famuyiwa, O. (2011). Matching Product Architecture with Supply Chain Design European Journal of Operational Research : 10.1016/j.ejor.2011.07.041

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Finding the Right Supply Chain for your Product!

Daniel Dumke — Wed, 12 Oct 2011 14:08:00 +0000

This again is an old classic in supply chain risk literature. In 1997 Marshall L. Fisher published this article in the Harvard Business Review targeting a simple question: “What is the Right Supply Chain for Your Product?”

It is noteworthy that this appears to be one of the most often cited papers in supply chain management. So I overlook the fact that it is quite weak on the methodological foundations.

The full text of this paper can be found here.

Product types

One of the major highlights of Fisher’s work is its simplicity. The author defines two basic products: functional and innovative products (figure 1). The major differentiating factor is the uncertainty of demand: Functional products (e.g. a classic Coke) show a rather predictable demand pattern and have long product cycles. Innovative products on the other hand show an unpredictable demand, and the life cycle can be as short as a few month.

Figure 1: Product Properties (Fisher 1997)

Supply chain types

Staying in the realm of simplicity Fisher also sees two basic types of supply chains or supply chain strategies a company can select (figure 2).

First there is the Physically Efficient Process, which can supply a specified amount of products at the lowest cost possible.

Second there is the Market-Responsive Process, which focusses on quick adaptability towards changing market needs.

Figure 2: Supply Chain Categorization (Fisher 1997)

Strategic Implications

The strategic implications become obvious quite quickly. One does not want to match a efficient supply chain with a fast changing market, since this would probably leave the company either with a huge excess inventory or, if too little is produced, with many unsatisfied customers.

On the opposite side one should not match a responsive supply chain with a steady market demand. Cost for maintaining this flexibility are potentially to high to maintain a positive product margin.

Figure 3 shows the matches in a matrix.

Figure 3: Matrix Matching Products with Supply Chain (Fisher 1997)

Conclusion

The implications of this concept are intriguing, just define the type of your products and adapt the supply chain accordingly. Fisher validates his points by describing two case studies (Campbell Soups and Sport Obermeyer) where his concept was successfully employed.

On the other hand, the concept is really simplistic. There are overlaps in the defining factors of the process and product categories making them harder to separate than it seems: What really is a predictive demand and when is it unpredictable?

And the critique continues. In 2011 Perez-Franco, Singh and Sheffi documented some of it in their article (which I reviewed here), which indicates that the concept is not viable in reality.

Of course this does not mean that I underrate Fisher’s contributions: with this article he still contributed to the discussion of supply chain strategy and its influencing factors and it serves as a basis for many more works, as I mentioned above.

The conclusion I draw:

Consistency: Of course there should be a connection between the product of a company and it’s supply chain strategy. But there are many other factors which have to influence an effective and efficient supply chain design as well.

Methodological: Never rely solely on a handful of sample case studies as the foundation of a concept. Sometimes you will be right, but very often you will be wrong.

You can continue reading on Discovering the Right Planning Approach for your Supply Chain.

Reference:

Fisher, M.L. (1997). What is the Right Supply Chain for Your Product? Harvard Business Review, March-April, 105-116

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Improving Performance in Food Supply Chains by Reducing Uncertainty

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

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

Methods

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

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

Results case studies

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

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

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

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

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

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

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

Results simulation

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

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

Further results were generated using the simulation model:

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

Conclusion

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

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Relative Improvements of Supply Chain Redesign Strategies

Daniel Dumke — Mon, 29 Aug 2011 13:21:00 +0000

The Bullwhip Effect was first discovered and analyzed in the 1950s. It triggered more intense research on the supply chain system (even though the term supply chain was not yet coined).
Starting in the early 1980s, this research finally lead to significant changes in real supply chains as well.
This article does a follow up on these developments and evaluates the relative improvements of each of the strategic stages.

Supply chain redesign process

The authors refer to a supply chain redesign process called “Cardiff methodology”. It is depicted in figure 1, key elements are the real supply chain as a starting point, this supply chain is then analyzed and converted into a conceptual model of the chain. In the the next step this conceptual model is transformed into a mathematical representation and then simulated to generate new insights on the dynamic behavior of the system. Lastly, this knowledge is used to adapt the real supply chain, and the process is restarted.

Figure 1: Supply Chain Design Process (Berry and Naim, 1996)

History of supply chain redesigns

The article in great length on the shifting redesign paradigms since the 1980s. Four phases are distinguished here. I will highlight the corner stones of each of them.

Phase 1 – Just in time
Just in time initiatives were launched in the early 1980s, with the goal of reducing in-plant lead times. As a result: “The application of JIT techniques have achieved very significant reductions in lead-times and inventory. In European plants, the average manufacturing cycle time from component start to finished good shipment has been reduced by around 50%, with an accompanying drop in inventory.”
Phase 2 – Interplant planning and logistics integration
The next step was to improve the information flows thoughout within the company and on its edges. Key components here was the introduction of MRP systems and the supporting EDI.
Phase 3 – Vendor Integration
During the next phase the focus shifted towards the supply side. And the vendors were integrated into the systems established in Phase 2.
Phase 4 – Time based management
Should be seen as a broader view on the above mentioned steps to lead time reduction. So time based management includes the comprehensive reduction of total cycle times from product development to the end customer.

Case study

The author use a case study to highlight the effects of above mentioned strategies on supply chain performance. Using the example of a PC supply chain the authors create a computer simulation model of this specific chain and test the above mentioned redesign strategies. Overall five different models are generated one baseline case including the state of the supply chain before the redesign strategies and one model for each of the four phases mentioned above.

The results can best be seen in figure 2.
At about time 5 a positive demand shock is induced into the system, after that customer demand is constant again. The results show decreasing factory order rate fluctuations with each new strategy employed.

Figure 1: Supply Chain Dynamics with different Supply Chain Designs (Berry and Naim, 1996)

Conclusion

This article has two major contributions first it has a very informative listing of the strategic paradigms during a 20 year time period. Furthermore it also shows that these strategic changes have not been in vain, but each of the phases has the potential to improve the supply chain performance.

Reference:

Berry, D., & Naim, M. (1996). Quantifying the relative improvements of redesign strategies in a P.C. supply chain International Journal of Production Economics, 46-47, 181-196 DOI: 10.1016/0925-5273(95)00181-6

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performance management

The organizational Antecedents of a Firm's Supply Chain Agility

Daniel Dumke — Wed, 24 Aug 2011 12:54:00 +0000

This time I’d like to have a look at supply chain risk management from a strategic point of view: What are the prerequisites in the design and culture of an organization to mitigate supply chain risks? The title of the article I review today is: “The organizational antecedents of a firm’s supply chain agility for risk mitigation and response”.

Method

The authors use structural equation modeling technique to establish the relations within their model (figure 1).

Figure 1: Relationship Model (Braunscheidel and Suresh, 2009)

The model proposes a linkage between the organizational orientation of a company related to the market and continuous learning. Those design factors should lead towards internal and external integration of the company and its supply chain as well as external flexibility as practices within the organization. This in turn should lead to agility, which is defined as

the capability of the firm, internally, and in conjunction with its key suppliers and customers, to adapt or respond in a speedy manner to a changing marketplace, contributing to agility of the extended supply chain.

Agility is measured by the proxies: joint planning, demand response, increased visibility and customer responsiveness.
The authors augment their model by several proposed relationships / hypothesis shown in figure 2.

Figure 2: Augmented Model with proposed Relationships (click to enlarge; Braunscheidel and Suresh, 2009)

As the method of choice to validate or reject the established hypothesis a survey was created and validated by a panel of ten experts from research and business. Of a sample size (email addresses) of 2955, 218 usable responses were received.

Results

The final results for each hypothesis is shown in figure 3. The hypothesis regarding the relationship between learning orientation and internal and external orientation were not supported as were the hypothesis on the relationship between internal and external integration and internal and external flexibility.

Figure 3: Results (Braunscheidel and Suresh, 2009)

Nonetheless the authors can conclude:

All three organizational practices of internal integra tion, external integration and adoption of external flexible practices were found to be direct antecedents of a firm’s supply chain agility, explaining a significant portion of the variance in the FSCA construct. Among these three antecedents, external integration with key suppliers and customers was seen to be the strongest predictor of a firm’s supply chain agility.

Also the cultural antecedents are shown to have an impact on the organizational practices.

Of the cultural drivers, market orientation was seen to directly affect both types of integration. […] Learning orientation was shown to have a strong and significant influence on internal integration.

Conclusion

I think the article highlights several important aspects. Coming from the supply chain strategy it summarized different organizational practices / tactics which have to be implemented to create a agile company in the supply chain context. The authors do a good job in operationalizing these different states / activities and therefore define quite precisely what they mean by the cultural antecedents, practices and agility.

On the other hand after reading the title (”… agility for risk mitigation and response”) I was expecting some more research on the connection between a companies agility and the reduction of risk, but it seems that the impact of the firm’s supply chain agility was not part of the main survey but “only” elaborated in the literature review.

Reference:

Braunscheidel, M., & Suresh, N. (2009). The organizational antecedents of a firm’s supply chain agility for risk mitigation and response Journal of Operations Management, 27 (2), 119-140 DOI: 10.1016/j.jom.2008.09.006

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Combination of Product and Supply Chain Design

Daniel Dumke — Wed, 20 Jul 2011 08:01:00 +0000

In research the decisions on the product and the corresponding supply chain are usually separated. This happens for a variety of reasons, one may be the reduction of problem complexity, another that the research focus is on a brown field approach where the products are seen as given.

This makes this article even more interesting, since it combines the both decisions in one conceptual process and a model to optimize the decision.

Figure 1: Iterative Process for simultaneous Design of the Product and Supply Chain (Lamothe et al., 2006)

Process

The process (figure 1) consists of two major steps. First product variants are developed. In the second step a simultaneous optimization of the supply chain and the product variants is conducted.
The process is meant to be interactive, so that the product designer has the chance to make redesign decisions after step two to learn from the optimization results.
This leads to a generic Bill of Materials (BOM) which contains the different options for the product design alternatives.

Figure 2: Generic Bill of Material for different Markets and Service Requirements (Lamothe et al., 2006)

Model

The authors use a classic Global Supply Chain Model and extend it to consider the generic BOM. The objective is to minimize the total costs (addition of all fixed and variable costs), which is subject to the generic BOM and other constraints (e.g. capacity, shipping, …).

Application

Finally, the authors apply their model at a wiring harness supplier of a car manufacturer. Overall there are 48 possible product families. Using an implementation of their model the authors solve the supply chain optimization and conclude with the result in figure 2.

Figure 3: Resulting Supply Chain after the Optimization (Lamothe et al., 2006)

Conclusion

The idea of the generic Bill of Materials which contains all product variants makes it possible to integrate multiple product variants in the mixed integer linear program.
The algorithm seems to be fast enough to produce optimal solutions within minutes and can therefore be used as a basis for new models integrating other global aspects like taxes and exchange rates.

Reference:

Lamothe, J., Hadjhamou, K., & Aldanondo, M. (2006). An optimization model for selecting a product family and designing its supply chain European Journal of Operational Research, 169 (3), 1030-1047 DOI: 10.1016/j.ejor.2005.02.007

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Supply Chain Network Design

Daniel Dumke — Mon, 28 Mar 2011 13:10:00 +0000

What is supply chain network design? Where does the “design” come into play? I recently read the book “Supply Chain and Logistics in National, International and Governmental Environment” by Zanjirani Farahani, Asgari, Davarzani (Eds.) (you can find more information on the publishers web site and buy it online at amazon.com), from which I present you today one of the chapters, which is on supply network design.

Supply Chain Design Questions

The authors start with the questions which have to be answered within the supply chain design domain, like

Which producers should we use?

How many DCs should there be and where should they be located?

How do we set the DC capacity at each location?

What products should each factory produce?

Network Design Problems

For solving those problems mentioned above, it is convenient to use a model of your supply chain, so you would be able to test different configurations or use optimization methods to find the perfect configuration.

So, the next step is to build a model of the supply chain. Davarzani and Rezapour give a comprehensive listing of the items you have to pay attention to while building the model.

Classification of Supply Chain Design Problems (Adaptation from Davarzani and Rezapour,2009)

Some of the major points are the objective function (cost oriented, profit oriented or service oriented), the number of layers you have to locate (do you have the ability to influence the location of your customers or suppliers?). Furthermore you have to answer questions on the demand (stochastic, deterministic) and international features of the network.

Network Design Models

As soon as you have answered these questions for your supply chain Davarzani and Rezapour give you a choice of different, current mathematical models which can be used for your needs. Here I can only give a short description of the models proposed:

Basic Model
three-tiered supply chain system with one or more suppliers, DCs and retailers

Capacitated DC
additional assumption that DCs have capacity restrictions

Service Considerations
inclusion of a service level within the objective function

Parameter Uncertainty
allowing parameters to be described by discrete scenarios (with probability of occurrence)

Closed-loop Network Design
inclusion of reverse logistics
International Distribution Network Design

activities and sourcing decisions can be adapted to countries conditions

Conclusion

After reading this chapter I was exited to get such a concise overview over the current (2009) state of supply chain network design. But after more careful considerations I am somewhat disappointed, first, from the state of the art itself: You still can find the basic model in it, a model which in a little bit more simplistic form has already been taught at the university, if you have a look at the first extensions: eg. capacitated distribution centers, you have a direct look at the limitations of the model before. Well I have never seen a supply chain without capacity constraints, have you?

Second, modeling in general is the process of abstraction of the real world, so the mentioned limitations are a natural cause of this. But sadly research on the effects of decisions which have been taken due to these restricted models are rare, so you as the decision maker always have the risk of choosing the wrong (eg. too much aggregated) model. This is known as model risk, and sadly this risk is only seldomly taken into account in supply chain management. To be honest I have not found a single article about it.

Third, to alleviate some of the restrictions other methods (than mathematical programming) can be used. My favorite would be supply chain simulation. Of course, you still have abstraction, but if the designer decides on lifting some of them (eg. have a different distribution of demand, …) this can usually be done much easier than with a mathematical model. Of course you usually also loose the ability to have optimal recommendations, since optimization of such a model takes a lot of CPU power.

Reference:

Davarzani, H. and Rezapour, S. (2009). Supply Network Design in Supply Chain and Logistics in National, International and Governmental Environment, Zanjirani Farahani, R.; Asgari, N.; Davarzani, H. (Eds.), Springer-Verlag, April 2009.

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