supply chain

Solution to Strategic Supply Chain Mapping

Daniel Dumke — Mon, 23 Apr 2012 13:05:55 +0000

I just realized that my favorite book on crafting diagrams to better communicate in a graphical way (Envisioning Information) just has been turned into a paper on mapping supply chains.

So I am happy to summarize an article, which focusses on how to convey complex information in an concise and comprehensive way.

Supply chain mapping

I already wrote about this topic several times. But because this is a very important issue, I did not hesitate to do it again from a different angle.

There are several key advantages to supply chain mapping:

To link corporate strategy to supply chain strategy.

To catalog and distribute key information for survival in a dynamic environment (in order) to direct the focus of the managers.

To offer a basis for supply chain redesign or modification.

Current channel dynamics can be displayed in a supply chain map.

The process of building the strategic supply chain map, in itself, will help define the perspective of the supply chain integration effort.

continued in the article

However there are obstacles preventing an effective supply chain mapping. One is, as often, the complexity of the supply chain.

Even with only a few echelons in focus the number of entities and connections just explodes. Figure 1 shows the results of some simple calculation. A supply chain with five tiers and only two connections per tier already contains 13 chain elements.

Figure 1: Number of Entities in a Supply Chain Map (Farris, 2010)

So in no time a supply chain map looks like the one in figure 2 or 3.

Figure 2: Complexity of a “short” Supply Chain (Farris, 2010)

Figure 3: Bad Example for the Representation of Material Flow (Farris, 2010)

Issues in supply chain mapping

Four issues in supply chain mapping can be recognized.

[…] Four issues [were identified] which must be addressed to further the development of strategic supply chain mapping:

proprietary information;

inadvertently changing channel dynamics;

getting lost in too many details; and

providing an ineffective perspective for management use.

Recommended supply chain mapping

Over a five year period the author conducted several “classroom laboratories” with his students doing experiments on how to improve current mapping techniques.

This is where the above mentioned book comes into play. In it you find a picture of probably the oldest geo-visualization (figure 4). In this case it was used to describe the declining strength (measured in number of soldiers) of Napoleon’s invasion of Russia and the way back. Highlighting the fact that most soldiers did not die in battle, but on their way to and from Moskow.

Figure 4: First Geovisual Map (click to zoom; by Minard, 1844-1870; Farris, 2010)

While this also highlights the importance of a good supply chain. Focus in this case is on the massive amount of information which can be extracted from this diagram (strategies, movement, location, time, strength), while still keeping a relatively low size and low perceived complexity.

Based on this idea and other insights, several iterations with the students lead to several key learnings and a recommended supply chain map. Figure 5 shows the recommended map, displaying the supply chain interactions on an industry- (not company!) level.

Figure 5: Example of a Recommended Supply Chain Map (Farris, 2010)

Lessons learned:

Utilize geovisualization techniques

The mapping efforts utilized a geovisual technique combining weighted solid and dashed arrows and lines, defined symbology representing different trading relationships, and the representation of financial flow to develop maps rich in content.

Start at a higher level

It is highly unlikely a company would be able to, or would desire to, expend the resources required to map 100 per cent of all of their customers and suppliers. Each mapping effort must determine the economic trade-off between the level of detail of their map, the cost to gather the detail, and the benefit received. […] Owing to the ready access to economic data, an industry macro map may offer the greatest mapping value for a relatively minimal investment. It is recommended that the map designer begin at a high level and then drill down.

Strive to keep the maps strategic

[…] strategic supply chain maps must maintain a strategic emphasis, any users with a desire to drill down to an operational level should utilize alternative process-related tools such as the SCOR model […]. Keep the strategic supply chain maps at a high, strategic level and avoid undue complexity.

Manage a synergistic network

As value chain mapping continues to evolve, map designers may find that the term “supply chain” is a misnomer. Strategic supply chain maps quickly reflect a “supply network” as it is not unlikely that a customer’s customer may be a critical supplier’s supplier. Recognition of these types of relationships may have far-reaching strategic impact in terms of keiretsu-type relationships or jointly beneficial marketing efforts […].

Embrace mapping creativity

[…] it was determined there was no single approach to developing a strategic supply chain map. Creating a strategic supply chain map is as much an artistic endeavor as it is a defined process. […] It is recommended that firms attempting to strategically map their supply chain embrace this fact by asking multiple individuals who create a map to use the same data set. Each variation may result in components within the map which offer easier readability and should be incorporated into the final map.

Conclusion

The resulting supply chain map is overwhelming at first, but which map wouldn’t be. A complex system like a supply chain and its supporting parameters are just hard to zip onto a single page.

After a short while of looking at the map I was just amazed how much information could be deduced from it.

I think using the lessons learned and (not too many) different information layers in a map can result in highly aggregated and informative results, ergo in a real supply chain map.

Reference:

Faris II, M.T. (2010). Solutions to strategic supply chain mapping issues International Journal of Physical Distribution & Logistics Management, 40 (3), 164-180 DOI: 10.1108/09600031011035074

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Supply Chain Apps for iPhone and iPad

Daniel Dumke — Mon, 26 Mar 2012 15:17:29 +0000

Some say that tablet computers are a great fit, not only for games and surfing the web, but for business applications as well.

But since the tablet computer is not much more than a big screen, software is key to a productive use of the pad.

To test this hypothesis I picked the iPad as an example for a tablet computer, because:

I own one,

there are more apps available than for any other platform,

the apps quality is often better.

Magazines

First, there are several apps by magazines, which often can also be read in paper or online. I picked three supply chain related examples:

Supply Chain Quarterly: The app features free issues of the CSCMP. The navigation performs ok (tested on an iPad 2). But the pages seem to be just rendered images. So loading takes a while and compression artifacts are clearly visible, which is distracting and not business-like. Furthermore it contains a lot of ads. But I like the contents journal. (App Store)

The E&Y Performance Journal: The journal sometimes features articles on logistics and supply chain management as well. Furthermore it is graphically stunning and uses the features of a touch-screen-display. (App Store)

Supply Chain Digital: The issue took the longest to download (probably about 5 minutes on a 25MBit connection). When I wanted to switch apps to get some distraction, the download was interrupted (even though it could have downloaded the issue in the background). Since I dislike badly written apps, I deleted it immediately.

The Supply Chain Quarterly Journal on the iPad

The Article Overview in the EY Performance Journal

Business applications

There are a few supply chain production apps as well, even one for selecting supplier management strategies based on related risks (app store USD 0.99). But other than this one I did not find any real business-applications worth mentioning.

Supplier Management Evaluation

Conclusion

So, it seems that supply chain specific apps are still missing on the app store. Of course you can always use Numbers as a spreadsheet and Pages for doing some writing, but I really was expecting more of this research…

At least at the moment there does not seem to be many business, let alone supply chain, related applications, which would make an iPad a useful device for business purposes.

If you find other useful apps, just let me know an I’ll post them here as well.

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Conceptual and Analytical Framework for SCRM

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

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

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

Core definitions

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

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

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

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

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

Second, there are different classes of risk problems:

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

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

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

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

There are two distinct ways to supply chain risk mitigation:

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

The authors distinguish three analytical approaches to risk management:

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

Results

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

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

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

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

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

Conclusion

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

Reference:

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

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SC Design: Organizational Roles in Network Management

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

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

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

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

Background and method

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

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

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

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

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

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

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

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

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

Findings

Six roles could be identified with distinct properties.

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

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

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

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

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

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

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

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

Conclusion

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

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

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

Reference:

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

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Design and Operation of a Hydrogen Supply Chain

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

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

Oil availability peak

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

Problem / Model

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

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

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

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

Three scenarios for the configuration are analyzed:

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

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

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

Results

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

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

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

The authors conclude:

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

Conclusions

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

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

Reference:

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

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Multi-level Supply Chain Design

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

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

Case

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

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

The authors continue explaining why further optimization is dearly necessary.

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

Model

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

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

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

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

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

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

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

Results

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

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

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

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

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

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

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

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

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

Conclusion

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

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

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

Reference:

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

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The Impact of Supply Chain Integration on Performance

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

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

Supply Chain Integration

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

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

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

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

Methodology

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

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

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

Figure 1: Company Profiles

The authors suggest the following hypothesis.

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

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

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

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

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

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

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

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

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

Results

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

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

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

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

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

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

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

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

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

Conclusion

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

Reference:

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

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Behavioral Risks in Supply Networks

Daniel Dumke — Mon, 09 Jan 2012 13:20:01 +0000

Not only earthquakes and terrorist attacks can lead to supply chain disruptions. Supply chains are also subject to behavioral risks, meaning that participants of the supply chain could exhibit behavior which might be consistent with their goals, but contrary to the goals of the supply chain’s other participants.

And so one has to ask: How should a company mitigate behavioral risks? And this is what Seiter did and his work was published as chapter 15 of another great book on supply chain risks (Supply Chain Risk by Zsidisin and Ritchie), which can be bought at amazon.com, if you are interested in reading more.

Methods

Seiter starts with the assumptions of the principal agent theory where interactions between individuals, which are built on asymmetric information may lead to problems for either or both parties.

Figure 1 shows the principal-agent relationships in a supply chain.

Figure 1: Principals and Agents in a Supply Chain (Seiter, 2009)

Next, a preliminary study was conducted to gather information on the strategies which are employed in practice. Ten companies were interviewed and the following strategies were found to be the most frequently used:

Communication: Communication is the formal and informal exchange of information between the partners within the supply network. There are many different forms of communication, e.g., oral communication or written communication. All forms of communication have in common that they reduce the degree of asymmetric information. But, the size of this effect depends on the frequency and quality of the communication.

Partner selection: Partner selection is the examination of the match between the potential partners in a supply network (Das and Teng 2003). From a sequence perspective the selection of appropriate partners is the first instrument to prevent the opportunistic behaviour of suppliers. The literature suggests many recommendations concerning the selection criteria to use (for an overview see Seiter and Isensee 2007). By using a set of criteria the partner selection process seeks to ensure partner “fit”. There are different types of fit, e.g., strategic fit, and resources fit (Das and Teng 1999). However, the most important fit is the fit of the partners’ objectives. Only if all partners in the supply network can reach their goals simultaneously, will dysfunctional behaviour become unlikely (Das and Teng 1999).

Inter-organizational cost accounting: A typical form of opportunistic behaviour a supplier can practice is the abuse of the information asymmetry. For example, to pretend that costs are higher and accordingly seek a higher price. In practice different forms of inter-organizational cost accounting may be implemented to avoid such behaviour. The forms vary from partial approaches to full approaches like open-book accounting (Kajüter and Kulmala 2005).

Inter-organizational planning: Another way to prevent opportunistic behaviour is to reduce the extent of asymmetric information by sharing planning data. Several concepts have emerged in practice like “collaborative planning, forecasting and replenishment” or “supply chain planning”. The diffusion of inter-organizational planning is also supported by a great variety of software solutions that support the structured planning along the supply network.

Sanctions: Sanctions, in the event of the occurrence of opportunistic behaviour, are one of the first instruments mentioned by each interviewee. Normally, sanctions are defined in the formal bilateral contracts between buyer and supplier (Wuyts and Geyskens 2005). Especially, in cases where product-specific information is shared in the partnership between buyer and supplier, the contracts include paragraphs preventing the abuse of this information. This is more often the case when it comes to international supply networks.

As a second step the influence of these strategies is tested using a qualitative survey.

Figure 2 shows the proposed hypothesis. It considers not only direct effects on the opportunistic behavior but also indirect effects, which support the build-up of asymmetric information.

Figure 2: Hypothesis for direct and indirect Influence of the proposed Strategies (Seiter, 2009)

Results

The author finds some clear results regarding communication and inter-organizational cost accounting, which both significantly help to reduce opportunistic behavior within the supply chain. But, “no significant effects regarding possible sanctions and inter-organizational planning systems were found.” Furthermore, there was a positive correlation between partner selection efforts and opportunism was found indicating that higher efforts could lead to higher opportunism.

To explain the non-significant results, the author conducted a focus group discussion which lead to the following plausible explanations for the findings:

The group came to the conclusion that sanctions cannot provide perfect protection as all future circumstances would have to be anticipated and planned for accordingly.

The absence of the anticipated effect of inter-organizational planning could be grounded in the construct itself. In practice several different types of planning systems do exist, some very simple and others very complex types. The effective operationalization of this construct may require the indicators to be more specific about the specific types of planning systems used.

[Regarding the positive correlation between partnerselection and opportunism:] The survey captures only the initial partner selection effort but not whether partners are tested continuously. Therefore, this unexpected result may possibly be due to the need to define the indicators more closely to include this possibility.

Finally the author concludes, that “communication seems to be the central factor for preventing opportunistic behaviour in supply networks. For this reason the improvement of communication should be a primary objective.”

Conclusion

I liked the study design due to its concise structure and the extensive elaboration on the study methods. The results show, that there still is room for improvement. A more detailed model might have lead to more significant results with the other strategies employed.

Reference:

Seiter, M. (2009). Behavioural Risks in Supply Networks Supply Chain Risk - A Handbook of Assessment, Management, and Performance, 235-247 DOI: 10.1007/978-0-387-79934-6_15

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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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Uncertainty in Value Stream Mapping Analysis

Daniel Dumke — Wed, 30 Nov 2011 15:09:00 +0000

Supply chain mapping can be a great tool to foster the understanding and from its results improve a supply chain network overall. Supply chain mapping can also be used to analyze the risks of a supply chain and improve its resilience (for an example in the blog follow this link).
A SC mapping can also lead to implications for risk management, but how do you include existing information about the risks themselves in the analysis?

Braglia et al. (2009) analyze this problem and suggest two approaches to include variability in a value stream mapping (VSM) with the goal of better identification of the wastes in a single plant setting, but I think their insights might also be interesting for a supply chain setting.

Method

Value stream mapping can be described as “a graphical representation of both materials and information flow within a facility”. This permits to analyze the process and make calculations, e.g. regarding the total production lead time (TPLT), the total value-added time (TVAT) and the efficiency of the process (η = TVAT/TPLT)

VSM is not the method of choice for all problems, beside some pros there are also the cons:
Pro

It shows the linkage between product flow and information flow;

it includes information related to production time as well as to inventory levels;

it helps to visualise the production process at the plant level, not just at the single process level.

The main cons include

it is a paper- and pencil-based technique, thus the accuracy level is limited and the number of versions that can be handled is low;

it lacks the spatial structure of the facility layout and how that impacts interoperation material handling delays;

it cannot address the complexity of high-variety low-volume type companies, whose value streams are composed of hundreds of industrial parts and products.

Uncertainty and VSM

The authors suggest two similar approaches to include uncertainty within the VSM: the stochastic and the fuzzy approach.

In the stochastic approach uncertainty is described using distribution function (figure 1) with a mean and standard deviation.

Figure 1: Representation of a Stochastic Variable (Braglia et al., 2009)

In the case of a fuzzy logic the uncertainty is described using a triangle function (figure 2), which can be described by the start and end values (a and c) and the maximum b.

Figure 2: Representation of a Fuzzy Number (Braglia et al., 2009)

Application

When applying one of the mapping approaches the authors suggest to follow the following seven step process:

The analysis starts with the identification of the value stream (i.e. product family) that has to be mapped.

Walking backward in the process (i.e. from the finished good inventory to the raw materials warehouse), practitioners collect all the relevant data and draw a sketch of the actual state map.

[To include the random aspects of the value stream,] the waiting/processing time of each activity is approximated [and represented by a corresponding function.

The actual state map is completed substituting, in the corresponding process boxes and on the timeline, deterministic times with the random variables.

[The TPLTs are calculated using stochastic methods]

Using lean principles possible future state maps are designed and their expected TPLT is evaluated […]

Obtained improvements are evaluated comparing the TPLT.

The major milestones in the process are therefore the completion of the map of the actual state and possible future state maps.

Figure 3 shows the actual state map for a case study company presented in the article. Underneath the map you find the process times with deterministic values, stochastic representation and fuzzy representation of the uncertainty (from top to bottom, click on the image to show a larger version).

Figure 3: Actual State Map (Braglia et al., 2009; click to enlarge)

One example for a possible future state of the supply chain is shown in figure 4, again with the respective lead times.

Figure 4: Future State Map (Braglia et al., 2009; click to enlarge)

Conclusion

Value stream mapping can be used in a supply chain setting as well. And it can help to find bottle-necks – and as shown here the randomness of processes can be included as well. The authors make a great job of explaining the mentioned approaches and sketching the implementation as well.
Sadly, they forgot to describe the advantages of the inclusion of randomness in the value stream mapping: After presenting a second future state the authors do a statistical comparison of the two outcome distributions and conclude: “Therefore both methodologies show that the first solution is definitely better than the second one.”
But, if they had just compared the deterministic process times they would have come to the same conclusion! So where is the point in including the randomness at all?

Reference:

Braglia, M., Frosolini, M., & Zammori, F. (2009). Uncertainty in value stream mapping analysis International Journal of Logistics Research and Applications, 12 (6), 435-453 DOI: 10.1080/13675560802601559

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Types of Supply Chains

Daniel Dumke — Mon, 28 Nov 2011 15:59:00 +0000

As a good book with current research on supply chain management I often recommend Supply Chain Management and Advanced Planning: Concepts, Models, Software, and Case Studies, edited by Stadtler and Kilger. In this review I refer to chapter 3 of that book, which goes into the details of different types of supply chains.

The understanding of the specific types of supply chains is especially useful from a viewpoint where multiple supply chains are analyzed. This could be within a larger company with several different supply chains or a consultant or researcher who wants to tailer strategies to reduce risks for a specific type of supply chain network.

Structure

The chapter is conceptual in nature and prescribes supply chain attributes which may be functional or structural in nature. As the last part a consumer goods and a computer assembly supply chain from a previous case study are characterized according to that scheme.

Functional attributes

The functional attributes fall into four categories: procurement type, production type, distribution type and sales type (figure 1).

Figure 1: Supply Chain Typology: Functional Attributes (Meyr, Stadtler, 2008)

Structural attributes

The structural attributes define the topography of the supply chain and the integration and coordination. They are summarized in figure 2.

Figure 2: Supply Chain Typology: Structural Attributes (Meyr, Stadtler, 2008)

Case: consumer goods

Using the data from a case study the authors then select the parameters of a typical supply chain in the consumer goods industry. Figure 3 shows the supply chain’s distribution network, starting with the factories, the central and regional warehouses and the trans-shipment points (CW, RW, TP) and ending with the retailers.

Figure 3: Case Study: Consumer Goods (Meyr, Stadtler, 2008)

From the case study the authors select the following functional and structural attributes of a typical consumer goods supply chain (figure 4).

Figure 4: Case Study: Consumer Goods Supply Chain (Meyr, Stadtler, 2008)

Case: computer assembly

Another case study unravels the typical properties of a supply chain for computer assembly (figure 5).

Figure 5: Case Study: Computer Assembly (Meyr, Stadtler, 2008)

Conclusion

Analyzing the strategic properties of supply chains is also quite important to my own research as well, and I find the selection of the different attributes in this chapter quite intriguing, even though some of them might be too far on the operational level.

The application of such a framework on the other hand requires a lot of care, since the parameters have to be analyzed and applied equally for each supply chain. Especially in the case of a generalization, where a “typical” case should be described, it is, in my point of view, necessary that the foundation of these common denominators are clearly described, a feature which is missing in the chapter completely.

I also doubt that it is possible to deduce the properties of an average supply chain from a singular case study at all. So one has to ask if the right method has been chosen for this chapter.

Nonetheless, overall I do think the categories which have been suggested can be very useful to analyze and differentiate supply chain types.

Reference:

Meyr, H., & Stadtler, H (2008). Types of Supply Chains Supply Chain Management and Advanced Planning, 65-80

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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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Evaluation of a Firm's Supply Chain Strategy

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

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

Foundation

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

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

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

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

Functional Strategy Map (FSM)

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

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

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

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

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

Evaluation criteria

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

The final suggested criteria are:

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

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

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

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

Figure 3 illustrates the criteria.

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

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

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

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

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

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

Conclusion

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

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

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

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

Reference:

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

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