Business & Research Tools

Alternate Scenario Planning

Daniel Dumke — Mon, 02 Jul 2012 13:36:17 +0000

Scenario Planning has been around for some time now. By some companies it is seen as a core tool to assess a risky future and support strategic planning. Up to now I only mentioned it briefly in a few articles.

In 1977 Vanston et al. were one of the first authors to document a complete scenario planning methodology.
So this article answers questions of what scenarios are and how to generate and analyze them.

Scenarios

In order to minimize the risk inherent in planning against a single, unforeseeable future and to be in a position to profit from different possible trends and events, many governmental agencies and private companies are finding it desirable to plan against, not one, but rather a range of possible futures. Obviously, for the technique to be used effectively a set of alternate scenarios which are relevant, reasonable, and logically interrelated needs to be developed.

There are four requirements which each scenario should fulfill:

Plausibility.

Self-consistency.

Inclusion of all critical, relevant factors.

Similarity to other scenarios in form and scope.

Scenario Planning

The methodology consist of twelve steps, which are described briefly and afterwards discussed in a case study. In the case study the University of Texas (UT) was asked by the National Aeronautics and Space Administration (NASA) to develop a set of alternate scenarios for use in a workshop to assess possible national policies concerning portable fuels.

Define Purpose and Organize Development Team
The goal has to be set first, building on the objective the candidates for a development team are selected.

Case study: The general purpose of the workshop and the proposed use of the scenarios to be developed by The University of Texas (UT) team (composed of members of the Center for Energy Studies and the Population Research Center (PRC)) were outlined in the original NASA work statement. Subsequent meetings of UT, TRW, and NASA representatives allowed further definition of the intended nature of the scenarios and the methodol- ogy for integrating the scenarios into the workshop procedures. It was agreed that approximately six different scenarios could be accommodated-given the size of the workshop. […]
Gather Relevant Data
The underlying data is key to any good scenario and determines its credibility and completeness.

Case study: The contract work statement for the university team emphasized nontechnical aspects of the energy problem. The scenario development committee hence focused most of its data-gathering activities on “futures” literature and societal factors and demographic trends.
List all relevant factors
Now the factors which are relevant for the project in a social, political, economic, technical and ecological way. This selection should be broad.

Case study: the committee next listed all components of the social structure which could reasonably be associated with energy consumption or which would limit or restrict the production of energy. All relevant factors were listed even though in some cases their impact on the energy dimension appeared remote. In all,
approximately ninety factors were identified.
Determine the most pertinent factors
In a team work approach a further specification and selection of the factors has to be conducted. An inclusion of management staff is recommended.

Case study: At this point three additional consultants were engaged to assist in factor evaluation. These consultants included an economist, a sociologist, and a political policy specialist. After a general meeting of the committee with the consultants, members of both groups were asked to rank the factors in order of their importance to the energy status of the nation.
Choose themes for alternate scenarios
General topics for the scenarios should be defined. “Obviously, the company or agency can plan against only a finite, generally small, number of futures. Although the exact number of scenarios to be developed will vary, experience has shown that from three to six are usually appropriate. As will be discussed later, one of the scenarios should be the one believed to be the most probable. The other scenarios should be chosen according to the degree to which they provide maximum value to the planning process.”

Case study: The [6] themes were chosen as follows:
(a) Economic expansion: A future in which the nation puts primary emphasis on economic growth, increased production, and improved material well-being.
(b) Environmental concern: A future in which the nation puts major emphasis on environmental and ecological improvement even, if necessary, at the expense of other factors. […]
Arrange factors into related groups
The factors found in step 4 have to be summarized into groups to describe the interdependencies between them.

Case study: After the factors developed in step 4 were compared with the six chosen themes, it was decided that the factors could be grouped into nine general topics.
(a) Population, (b) Urbanization, (c) Labor Force […]
Define present situation in terms of the chosen factors
“Using information on the status at present and in the recent past of the previously chosen factors, a narrative statement is written regarding the present state of the relevant society and the manner whereby this state came into being.”

Case study: At this time the scenario development committee prepared a narrative description of
the present status and recent history of the United States based on the above groups of factors. All listed data were carefully referenced and a glossary of terms was attached.
Develop most probable scenario
The authors suggest to start of with the most probable scenario and set the factors. To generate the necessary information either own forecast and/or projections of others shall be used.

Case study: To begin the development process, values were assigned to each of the relevant factors.
These values were carefully chosen after comparing estimates from various technical and “futures” sources with trend extrapolations developed by the committee itself.
Alter basic factors to support alternate scenarios
Finding alternative possibilities for factor combinations then support the other scenarios.

Case study: The scenario development committee next examined each of the relevant factors and
determined how they might be affected by the futures envisioned in the six alternative themes. When appropriate, factors were modified to reflect projected effects.
Prepare alternate scenarios
“All scenarios should be as closely alike in format, wording, and style as practical. This congruency will assist in comparison of the planning programs based on the different scenarios. As with the most probable scenario, projections should be referenced where possible, and reasoning carefully explained.”

Case study: Using the modified factor values developed in Step 9, scenarios were developed for each
bounding theme. To the extent possible, each alternate scenario had the same format and, in many cases, the same phrasing as the most probable scenario. This parallelism was intended to facilitate comparison of the plans which would be developed using the different scenarios.
Check all scenarios for consistency, clarity, and completeness
“It is very easy in a complex scenario to overlook internal inconsistencies and normally
obvious violations of logic and reason. All scenarios should be checked by people not involved in their preparation to guarantee their clarity, correctness, and completeness.”

Case study: The completed scenarios were then sent to two editors for review and rewrite as necessary. Copies were also sent to selected consultants for suggestions and comments.
Modify scenarios as necessary and organize for use
Depending on the purpose of this method the scenarios should be organized and finalized.

Case study: The comments of the trial run participants were carefully weighed and scenarios altered as appropriate.

Conclusion

Scenario planning can help companies to try to get a better grasp of future events and their effects on strategic variables.
The method presented here does show several drawbacks. It does leave a lot of room for interpretation and any reasoning for using exactly this process is just missing.

On the other hand I think the major advantages lie in the communication processes which are induced by using this or any similar methodology and I agree with the authors’ conclusions:

It forces planners to accept and act on the fact that the future can never be exactly known. Thus, the plans resulting from the use of this technique should involve more flexibility than those drawn up to meet one set of postulated events.

It serves as a tool for communication between people with very different points of view and encourages cross-fertilization of ideas.

It provides a vehicle for integrating relevant technical and nontechnical factors into the planning process.

It encourages the development of a structured system for monitoring trends and events of import to the organization. Thus, it aids in preventing the organization from being faced with unexpected threats and from failing to take advantage of emerging opportunities.

It helps to identify the point at which important decisions will Irave to be made in the future. This should allow more time for consideration and data acquisition.

Reference:

Vanston, J., Frisbie, W., Lopreato, S., & Poston, D. (1977). Alternate scenario planning Technological Forecasting and Social Change, 10 (2), 159-180 DOI: 10.1016/0040-1625(77)90043-9

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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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Assessment of Supply Chain Vulnerabilities

Daniel Dumke — Mon, 02 Apr 2012 14:34:31 +0000

This is a review of another chapter of the book by Zsidisin and Ritchie (Supply Chain Risk). The book can be bought at amazon.com, if you are interested in reading more.

I already reviewed chapter 15 on Behavioral Risks in Supply Networks.

The title of this weeks article is “Assessing the Vulnerability of Supply Chains” and since the author works for a consulting firm you can expect a more practice oriented approach to risk analysis.

Goals and definitions

There are three main questions to be answered in this article:

Understand the nature and types of factors that may pose threats and risks to the achievement of the supply chain system’s short and long term mission.

Understand the scenarios (processes and mechanisms) through which these threats, risks and vulnerabilities may evolve.

Understand how through the use of vulnerability scenarios, the likelihood and consequences of such threats may be reduced and managed in a cost- and service effective manner, whilst achieving an acceptable vulnerability level.

A vulnerability of a supply chain in this context is defined as

the properties of a supply chain system; its premises, facilities, and equipment, including its human resources, human organization and all its software, hardware, and net-ware, that may weaken or limit its ability to endure threats and survive accidental events that originate both within and outside the system boundaries.

Vulnerability analysis is seen as an extension to risk analysis.

Risk analysis is focused towards the human, environmental and property impacts of an accidental event, while a vulnerability analysis is focused towards the system mission and the survivability of the system.

In a risk analysis three questions make up the basis of the analysis: (i) what can go wrong, (ii) how likely is it to happen, and (iii) what are the consequences.

A vulnerability analysis, on the other hand, focuses upon (a) an extended set of threats and consequences, (b) adequate resources to mitigate and bring the system back to new stability, and © the disruption time before new stability is established [figure 1].

Figure 1: Disruption Sequence (Asbjornslett, 2009)

Vulnerability Analysis

Based on this definitions the author builds his approach on a generic approach for risk assessment. The flow chart in figure 2 highlights the seven steps.

Figure 2: Vulnerability Analysis Process (Asbjornslett, 2009)

These steps fall into three categories:

Understanding the context-specific threat and risk picture of the given supply chain and SCM context, and structure this into a taxonomy of the vulnerability factors [steps 1 to 3].

Analyse and rank the vulnerability scenarios, resulting in a criticality ranking of the scenarios [steps 4 and 5].

Handling of the vulnerability through cost- or service-effective likelihood or consequence reducing measures, bringing the vulnerability down to an acceptable level [steps 6 and 7].

The goal of the first step is to have a common understanding of the specific objectives of the vulnerability analysis, the level of analysis and setting the levels of acceptable risk.

In the second step the processes and infrastructure has to be mapped. Flows of money, information and goods are highlighted. Here, “it is recommended not to make the context description too fine-grained, but rather make notes of how the context could further be detailed if required.”

In the third step factors which lead to vulnerabilities are collected in a structured manner. Figure 3 shows a fishbone diagram with several different categories which can lead to vulnerabilities.

Figure 3: Example of Factors Contributing to Vulnerabilities (Asbjornslett, 2009)

Based on these vulnerabilities adverse scenarios are developed in the next step.

A scenario is a sequence of possible events, originating from an accidental event, where the events may be separated in time and space, and where barriers to prevent the sequence are part of the scenario.

In the next step the mentioned scenarios have to be documented. The author suggests the worksheet in figure 4.

Figure 4: Template for Scenario Documentation (Asbjornslett, 2009)

To get a better grasp of the actual criticality of the revealed vulnerabilities, each scenario has to be evaluated according to its likelihood and consequences (step 5). Figure 5 has a sample sheet.

Figure 5: Assessment of the Scenario’s Criticality (Asbjornslett, 2009)

Step 6 is about ranking the different vulnerabilities to best align risk mitigation efforts (figure 6).

Figure 6: Likelihood/Consequence Diagramm (Asbjornslett, 2009)

As such we have ‘low-criticality’ scenarios in the lower left corner (white), and ‘high-criticality’ scenarios in the upper right corner (dark grey shading). The ‘criticality areas’ should be based on the acceptance criteria developed in step one, both for the un-mitigated and the mitigated consequences.

The last step deals with finding mitigation strategies for selected scenarios. Figure 7 shows the template.

Figure 6: Mitigation Activities to Reduce Likelihood or Consequences of a Vulnerability (Asbjornslett, 2009)

Conclusion

This article presented a really business oriented approach to vulnerability analysis. In a similar manner how it could be found in a presentation of a business consultant.

This business orientation can be also seen in the structure of the article: A conclusive derivation of the process is missing completely at least the definitions are mentioned.

Nonetheless, from experience I can tell that this process contains several important steps which are also known from scientific literature. And it really is immediately applicable.

Why not include some of the insights and steps presented here in your next risk-management-meeting?

If you want to have a more scientific view on vulnerability analysis have a look at this article.

Reference:

Asbjornslet, B. E. (2009). Assessing the Vulnerability of Supply Chains Supply Chain Risk - A Handbook of Assessment, Management, and Performance, 15-33 DOI: 10.1007/978-0-387-79934-6_2

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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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Psychological Foundations of Supply Chain Risk Management

Daniel Dumke — Mon, 16 Jan 2012 15:44:09 +0000

Psychological research shows that risk is never a objective matter (1, 2). This is made even more obvious by the fact that demographics play a huge role in the decisions made by supply chain risk managers.

But how could one integrate these findings into a more general framework?

Problem

In business uncertainty is a problem of perception. As the name “uncertainty” implies there is no certainty about a business or in this case supply chain performance property. So the amount of uncertainty has to be estimated or guessed and since most of the estimation is done by mangers, guessing is a matter of how the problem is perceived by them.

In the process of analyzing a supply chain problem (well, any problem indeed) there are several psychological effects working mostly against a rational decision.
The process within the mind could be modeled as two competing thought processes:

The first branch of thought processes initially addresses the problem with a quick intuitive answer, and the second branch monitors and adjusts the proposed answers.

The authors admits that even though he supplies a long list of psychological biases, there are still many more so these points should be seen as what they are – an excerpt:

One of the heuristics we use to simplify decision making is to assume that “one thing is like something else”.
“Among the problems that have been observed based upon representativeness is the misrepresentation of the role of chance in events.”
The frequency of the presentation of an issue and the manner in which it is presented influences the way we think about it, so “in the supply risk realm, major disasters are likely to color managerial perceptions of the frequency of major disruptions.”
“Overconfidence […] is typical in assessment of uncertain quantities. In the context of supply risk management such overconfidence is likely to arise when a particular manager who has substantial experience infers (e.g., makes an intuitive judgment) about the probability of some event, such as the probability of supply disruptions associated with various suppliers, without collecting and utilizing performance data.”

Example case: The issue of having multiple suppliers

After the attacks on the World Trade Center in September 2001 there was an increase in the average number of suppliers deployed and similar effects can be noticed in other large disruptions, especially if the media coverage is large.

Thus, unless we actually collect the data and calculate the frequencies, the odds are good that we will confound the two terms in our algebra of the supply risk associated with major events, since as a short cut to understanding the risk, we substitute the extent of damage for how representative such an event is of daily experience.

First, there are rational reasons to increase the supply base to reduce / diversify the risks across multiple (hopefully, independent) suppliers.
So, after an event like this we think of the decision problem in a certain way, and decide to do a risk benefit analysis and
bq. we might decide that the seemingly small risk of more relationship issues is more than offset by the benefits as we add more suppliers. Note that this is a risk versus benefit comparison. By contrasting risk compared with benefits, we have framed the decision in one way.

But one could also reframe the problem to a risk-risk-analysis comparing on the one hand the risk of being subject to disruptions and on the other hand risks of inadequate coordination, due to too many suppliers.

This example should show two things. First, we generally add suppliers to reduce risk, but we trade reductions of risk that is relatively rarely experienced (but catastrophic when it is observed) for risk that is relatively frequently experienced. Second, there is tremendous value to actually knowing how your suppliers are performing in the form of how frequently you experience disruptions that require intervention, because this is the only way that you can really understand what you are trading off.

Management implications

Smith draws several management implications from his analysis:

“The first step in addressing any potential challenge is awareness”, not only an objectified view of the risks around us, but especially of the decision processes which lead to certain decisions for or against lean supply chains or backup suppliers.
Group decisions might be a way to improve this decision making, if common problems with it (e.g. group think are prohibited.
“We have already seen that statistical thinking and training can be valuable in overcoming the shortcuts that we can end up taking in the face of difficult decisions. In supply risk management it is important that the probability of potentially negative events be determined as accurately as reasonably possible, so that both the extent of the risks, and the potential for avoiding the negative consequences of the realized risk can be soundly assessed.”
“Finally, as was illustrated here, reframing represents a valuable approach to overcoming some of the biases associated with decision making under uncertainty.”

Conclusion

Smith highlights an often neglected aspect when talking about supply chain risk management. But this aspect is not only neglected by researchers, but also managers alike.
Psychological issues like those mentioned above have been discussed for decades, but only few businesses employ them or even think about employing them.
So on the one hand side one should ask: Do we need more risk mitigation measures to ensure sustainable profits?
But, also: How do our cost reduction measures affect our risk-exposure?

Reference:

Smith, M.E. (2009). Psychological Foundations of Supply Chain Risk Management Supply chain risk - A Handbook of Assessment, Management, and Performance, 219-233 DOI: 10.1007/978-0-387-79934-6_14

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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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Supplier Risk Monitoring for the Automotive Industry

Daniel Dumke — Wed, 23 Nov 2011 10:22:00 +0000

A typical supply chain risk management process consists of four steps: risk identification, assessment, management and monitoring. From those steps, one of the most neglected step is the risk monitoring.
Risk monitoring implies two different actions: Continuous risk assessment and actions, as soon as pre-defined limits are reached.

So this article sheds light on the risk monitoring, from an article by Blackhurst, Scheibe and Johnson (“Supplier risk assessment and monitoring for the automotive industry”).

Literature

After the analysis of the current literature the authors come to the conclusion: “Of the two methodologies we did find to monitor risks, neither addresses measuring, assessing and monitoring supplier risk over time. In addition, research that has examined supplier evaluation models has indicated that most methods are too mathematically complex to implement and understand, require excessive amounts of data, or are too subjective.”

So the authors want to close this gap, by developing a risk assessment and monitoring methodology for a US automotive manufacturer.

Risk assessment and monitoring methodology

Core of the risk monitoring methodology is the risk assessment, which is done using several categories of risk. The authors extend the framework of Chopra and Sodhi (2004), which contains nine risk categories: disruptions, delays, systems, forecast, intellectual property, procurement, receivables, inventory, and capacity.
To adapt this framework to the specific needs of the automotive industry.

For example, in the Chopra and Sodhi framework there was a risk category called delays. While this is a reasonable category in general terms to describe delays in material flows, it is insufficient to capture the necessary detail of risk elicited through our interviews with the auto manufacturer. Consequently, we expanded delays into logistics, supplier dependence, and quality.

Figure 1 shows the selected risk categories divided into internal (controllable) and external (uncontrollable) risks.

Figure 1: Supply Chain Risk Categories (Blackhurst et al., 2008)

Each of these categories are then weighed, so that the sum of all weights adds up to 100%. The subcategories also have to be weighed to show their relative importance within the risk category. In the example of the automotive manufacturer the focus was on quality and disruptions only, with 40 and 60 percent respectively.

Using the example of a disc brake assembly, the next step is to assess the risks of the individual parts of the supply chain product. In this case: caliper, hub and rotor. The results are shown in figure 2 and aggregated in a heat map (figure 3).

Figure 2: Risk Evaluation Sheet (Blackhurst et al., 2008; click to enlarge)

Figure 3: Heat Map for Product Parts (Blackhurst et al., 2008; click to enlarge)

The results can also be interpreted towards the individual suppliers (figure 4).

Figure 4: Heat Map for Suppliers (Blackhurst et al., 2008; click to enlarge)

Continuous process

The authors elaborate extensively on the requirements of a risk assessment and monitoring methodology:

Any risk assessment methodology should be able to develop a list of rank ordered critical parts (whether pre-specified or determined by the assessment tool); with critical risk parts at the top and low-risk parts at the bottom.

Furthermore the main purpose of an implementation of a risk monitoring is to switch from a reactive supply chain risk management to a continuous and proactive one.

To do this, risk ratings and/or risk indices must be tracked over time and trends monitored to determine if they are reaching unacceptable levels.

For this the risk scores developed above have to be updated regularly and can lead to a time dependent analysis shown in figure 6.

Figure 6: Trends in Supplier and Part Risks (Blackhurst et al., 2008)

Operational issues

The authors see four operational issues:

The number of categories and subcategories used becomes a balancing act. As more subcategories are added to a particular category, the relative impact each subcategory has on the score of the overall category declines. Similarly, as more categories are added to the risk assessment and monitoring tool, the relative weight each category contributes to the overall risk index of a supplier or part declines.

A higher weight on a particular factor will cause that factor to have more impact on the calculated risk index. As previously mentioned, the weights can be based on the probability of each category of disruption occurring, the relative impact each category of disruption has on supply, or any other factor considered important to the company.

Each subcategory must be rated. Some factors such as war and terrorism, political issues/unrest, information infrastructure breakdown, level of system integration, etc. are quite subjective and ratings on these factors should be made by higher level managers familiar with assessing these factors.

In order to use the methodology in a proactive manner, the ratings on each subcategory must be updated on a periodic basis and the data analyzed for patterns, high-risk levels, or trends that indicate potential problems. […] Therefore, the time interval between updates will also vary from subcategory to subcategory.

Conclusion

Overall the automobile manufacturer from the case study was reportedly very pleased with the resulting process and its relative ease of use and implementation: “it is too early to use overly sophisticated and brittle methods. We need an easily employable and understandable method such as this.”
I think the authors not only did a good job in summarizing the process, but also reflecting on its implications and shortcomings.

Reference:

Blackhurst, J., Scheibe, K., & Johnson, D. (2008). Supplier risk assessment and monitoring for the automotive industry International Journal of Physical Distribution & Logistics Management, 38 (2), 143-165 DOI: 10.1108/09600030810861215

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How Risky is your Company?

Daniel Dumke — Mon, 05 Sep 2011 14:36:00 +0000

Today I picked a special article on corporate risks. “How Risky is your Company?” by Robert Simons of the Harvard Business School. Its a more business oriented view on how companies should handle risks, internally. But since internal risk management can be seen as a part of supply chain risk management, I also include it here.

Risk exposure

This article is about how risky one company is. About the internal risk. And by these risks, the author does not so much refer to the production processes, but the softer risks of managing a company.

The key contribution of Simons is the Risk Exposure Calculator. Of course not all risks are unintended. Most people would argue that risk is part of doing business, so a good risk calculator would only include the unwanted and unrecognized risks.

It is also not the goal to find one specific number to pinpoint the risks of a company, but to give hints for improvement.

Risk Exposure Calculator

The key elements of Simons calculator are three internal pressures: due to growth, culture and information management. A version of the calculator can be found in figure 1.

Figure 1: Tool to Calculate the Risk Exposure of a Company (Simons, 1999)

There are three pressure points in each category:

Growth

Pressure for performance
Setting challenging goals can have stimulating effects on employees, but also increase risks when employees try everything to achieve those goals: “They may, for example, accept customers with poor credit ratings or cut quality to accelerate operations.”

Rate of expansion in operation
If companies are expanding at a faster rate than the knowledge of their employees, it can also lead to additional risks: “Managers should ask themselves: Are operations expanding faster than our capacity to invest in more people and technology?”

Inexperience of key employees
This can also be a problem in times of growth, when key managers are employed without sufficient training.

Culture

Entrepreneurial risk taking
Risk taking is part of daily business and also a reason why a profit can be reaped from those deals. But of course, the more risk a company takes the more it has to take care of.

Bad news
This point measures how information, especially the bad news, travel upwards to the management. So the questions to ask here are: “How much bad news do I actually hear? Have I surrounded myself with ‘yes men’?”

Internal competition
For some conglomerates this last cultural pressure point is just the way of making business. But internal competition can also have negative effects like decreased information sharing within the whole company, and thus increasing risks.

Information Management

Transaction complexity and velocity
“To determine the score on this point, managers should do a rough calculation in their heads. What were the complexity, volume, and velocity of information a year ago? Have they risen, and by how much?”

Diagnostic performance measures
Simons argues that performance measures are often neglected in times of growth. This point should have a high score, if there is: “a feeling of frustration – a sense that it’s hard to get the right data at the right time. When there are gaps in diagnostic performance measures, managers end up getting the information they need by making phone calls and walking around. In short, they spend a lot of time doing the work a computer system should be doing.”

Decentralized decision making
The last point here is decentralized decision making, which can be used to improve motivation and performance. But also local managers often do not fully follow the guidelines set by the company and also information flow might be disturbed.

Overall the risk index can be calculated by adding up the individual scores. But the insights you gain are by answering the questions and not by the resulting number alone.

Controlling internal risks

Simons suggests ways to manage the above mentioned risk by focussing on five questions (figure 2).

Figure 1: Ways to improve Control and Reduce Risks within a Company (Simons, 1999)

Conclusion

This article after all was more inward facing than I thought, but nonetheless operational risks are an important part of every-day business and of course strategic management. Operational risks as described here probably became a little outdated due to some unpopular initiatives as the Sarbanes-Oxley Act (USA) or BilMoG (Germany), but there is a lot of potential for improvement and Simons describes several good cases supporting his points.

The major conclusion from his research is the contradiction between risks in times of high growth. All the risks Simons presented are usually growing during the “good times” when the business is growing. And many cases show that those risks can be the reason why those companies went out of business after the growth period was over.

This research can give you clues where improvements are necessary and also shows levers to improve control within the company again.

Reference:

Simons, R. (1999). How Risky is your Company? Harvard Business Review, 85-94

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Let me help you with... Time-Based Risk Management

Daniel Dumke — Wed, 31 Aug 2011 13:56:00 +0000

Today I present you only one chapter of a great book by Wu and Blackhurst: “Managing Supply Chain Risk and Vulnerability” (which can be bought on amazon.com here).

I especially like the third chapter by Professors Sodhi and Tang on a new approach to managing disruptions: Time-based Risk Management.
The whole book features a more practitioner oriented approach so don’t expect too much focus on the methodological parts.

Reason for risk management

Supply chain disruptions should have arrived at the board rooms due to

“short-term effects (negative publicity, low consumer confidence, market share loss, etc.) and
long-term effects (stock prices and equity risk).”

So there should be enough drive to pay for even some of the more expensive risk management initiatives. Nonetheless a really robust supply chain strategy has to mitigate supply chain risks and increase profits at the same time.

Time-based risk management

The time-based risk management approach aims to travel on this thin line and deliver a strategy to mitigate disruption risk without compromising profits.
It consists of three time frames which should be in focus of the risk manager:

time to detect a disruption (D1),
time to design a solution (D2),
time to deploy (D3),
time to response is set to the sum of D1 to D3 (R1), and
time to recover (R2)

The authors argue, that prior work has pretty much focussed on the generation and selection of recovery plans, but only after the event has occurred.
The time-based risk management now tries to reduce the time needed for the other elements, since “just as 80% of the total cost of a product is determined during the product design phase, the activities that take place for designing response can have significant effect on the overall impact of a disruption.”

Argumentation

Furthermore, based on three case studies (e.g. the Nokia / Ericsson Case) the authors argue that a longer response time can also lead to hugely increased recovery times. “This is mainly because the magnitude of the problem triggered by the event escalated exponential over time.”

This argument is illustrated in figure 1 using a exponential epidemic model.

Figure 1: Effects of Response Time Reduction (Sodhi and Tang, 2009)

Activities to reduce response time

The authors name five time-based disruption management strategies to reduce the response time (R1) and therefore also the recovery time (R2).

Work with suppliers and customers to map risks
Define roles and responsibilities
Develop monitoring/advance warning systems for detection
Design recovery plans
Develop scenario plans and conduct stress tests

Risk and reward

As argued in the beginning robust strategies should not only support the company during times of disruption, but also increase competitiveness.
Using the case of Zara (fashion industry) the authors outline the advantages of time-based risk management. By managing their design, manufacture and deliver operations very closely and centralized with a already very low lead time Zara can detect disruptions almost in real time (low D1). New clothes and therefore a market adaptation can be designed very quickly, so D2 is quite low as well. Since suppliers and distribution are usually in close proximity to the factories deployment of new strategies is fast as well (low D3).

However these low lead times have not (primarily) been established for risk management purposes, but they are part of Zara’s business strategy and help them achieving continuous high growth rates as well.

Conclusion

Sodhi and Tang use very illustrative cases to make their points for a time-based risk management approach. This approach represents a corporate strategy which of course has to include a very broad definition of supply chain management. So not only the logistics and manufacturing parts are included, but also product design, finance, …

Reference:

Sodhi, M.S., & Tang, C.S. (2009). Managing Supply Chain Disruptions via Time-Based Risk Management Managing Supply Chain Risk and Vulnerability: Tools and Methods for Supply Chain Decision Makers., 29-40

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Simulation as a Tool for Supply Chain Optimization

Daniel Dumke — Wed, 27 Jul 2011 13:54:00 +0000

Sometimes I am really amazed by the research topics of others. Even though I already read much about simulation and its potential benefits, up to now I have never seen a analysis of supply chain simulation performance on a larger sample. So I would like to share those insights here.

Simulation method

In this paper the authors (Manzini et al.) used a visual interactive simulation (VSI) approach for five case studies. In this case VSI refers to a simulation model that can be directly manipulated by the user of the model during simulation. So for example the effects of a change in inventory levels could be viewed directly after the fact.

This approach therefore allows for quick testing of different scenarios and possible strategies for optimization of the supply chain.

Case example

The authors elaborate five different cases where they employed the above mentioned methodology. I will just highlight one case example here: A make-to-order producer of packaging systems for the pharmaceutical industry. The produced machines consist of a large number of different components, which are supplied by external suppliers. The authors build a model containing the 18 major suppliers, which supply overall 53 modules for the final product.

Figure 1: Case Study: Comparison of Module Lead Times (Manzini et al., 2005)

Based on the simulation model different measures have been developed to reduce lead times from the suppliers. Figure 1 shows the effects of those measures for each of the 53 components. The lead times could be reduced in nearly all areas.

Case analysis

As in any multi-case study after the description of the different examples a cross case analysis and synthesis is necessary to compare the results and draw further conclusions. In this case the author calculate five indicators for each case study which describes the effort for the simulation study and the resulting benefits:

General indicators of model complexity:

NE index is the number of entities used in the simulative model so as to describe the real situations that can occur; and

PT takes into account the programming time in terms of man-hours dedicated by the programmer to develop the model.

General indicators of model effectiveness:

SI is the saving index, defined as the ratio between savings forecast and costs of simulation approach; this is introduced to show the economic feasibility of the simulative model;

SCI, simulation costs index, is the ratio between simulation costs and plant modification costs required for the supply chain optimisation so it deals with the simulation’s economic weight in a complete project; and

RI, readiness index, is the ratio between the lead-time of the simulative approach and real project time extension; it measures the capability of the dynamic model to give rapid solutions to a problem.

Result

The above mentioned indices are then used to compare the different case studies concerning the effective and efficient use of the VSI simulation method within each of the settings.

Figure 2 shows the savings index of the companies which compares the savings the company could achieve using the selected measures versus the cost of the simulation (more is better).

Figure 2: Comparison of the Cases: Saving Index (Manzini et al., 2005)

Figure 3 shows the simulation cost index which refers to the cost of simulation versus the overall project costs for implementing the measures within the respective supply chain (less is better)

Figure 3: Comparison of the Cases: Simulation Cost Index (Manzini et al., 2005)

Figure 4 displays the readiness index. This describes the amount of time it took to execute the simulation versus the total project time necessary to make the changes (less is better).

Figure 4: Comparison of the Cases: Readiness Index (Manzini et al., 2005)

The authors conclude based on these results that the VSI method is very suited for deployment in a business setting.

Conclusion

The authors make a strong case for “their” simulation approach: visual interactive simulation and claim that it is especially useful from a cost / benefit point of view. Also the results in the cases are delivered quickly.

I would love to see another comparison including other simulation approaches, since at least from a theoretical point of view, the VSI is a very simplistic simulation method. There is no optimization selecting the best strategy from several alternatives, but the simulation modeler has to select and evaluate the “best” strategy by himself.

Nonetheless a simple approach has advantages from the complexity perspective. As always: Case studies are not representative, but should give a hint for further research, so go ahead and do some research!

Reference:

Manzini, R., Ferrari, E., Gamberi, M., Persona, A., & Regattieri, A. (2005). Simulation performance in the optimisation of the supply chain Journal of Manufacturing Technology Management, 16 (2), 127-144 DOI: 10.1108/17410380510576796

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Follow Up: Systematic Review

Daniel Dumke — Wed, 29 Jun 2011 12:48:00 +0000

Already some time ago I wrote about Systematic Review, a literature analysis approach that should lead to a well founded overview of a specific research field. Since I left out the history and some insights last time I wanted to extend on my last article here.

History and background

Systematic Review developed from a need in medical research to aggregate multiple studies on one research topic into a comprehensive picture on the current state of the research. Meta research can improve the overall validity of the research by increasing the sample size and viewing a research problem from different angles. But this also comes with a disadvantage as well: Small differences in the methodology of the original studies have to be acknowledged by the researcher and incorporated within meta analysis. Larger differences can even lead to the invalidity of the whole meta study.

But meta studies still is one of the best evidence there is (figure 1).

Figure 1: Hierarchy of Evidence (Tranfield et al., 2003)

So to overcome the difficulties the Systematic Review process was developed, with the goal to formalize the process of meta analysis

Transfer

Tranfield (2003) pioneered the transfer of the methodology to the management field with only few adjustments. To justify the transfer he compared the research methodology in management and medicine (figure 2).

Figure 2: Comparison of Management and Medical Research (click to enlarge; Tranfield et al., 2003)

Conclusion

In my own research the Systematic Review approach proves very helpful in organizing the vast amount of research available in supply chain risk management.

Figure 3: Overview Systematic Review Approach (Tranfield et al., 2003)

Reference:

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review British Journal of Management, 14 (3), 207-222 DOI: 10.1111/1467-8551.00375

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Avoiding Supply Chain Breakdown

Daniel Dumke — Mon, 20 Jun 2011 14:00:00 +0000

This article presents a comprehensive practice oriented framework for managing supply chain disruptions by Sunil Chopra and ManMohan S. Sodhi. The article has been published in the MIT Sloan Management Review in 2004. The framework covers everything from risk analysis to the selection of the risk mitigation strategy.

Risk Categories

Chopra and Sodhi find nine categories for risk in the supply chain context. In figure 1 they contrast the risks with the corresponding risk drivers.

Figure 1: Risk Categories (Chopra and Sodhi, 2004)

Risk mitigation

The above mentioned risks have to be analyzed and if necessary mitigated. Risk mitigation as a process of managerial decision making can be quite cumbersome, since there is no catch-all strategy and some strategies can even increase other risks as a side effect. Figure 2 highlights the effect of selected mitigation strategies on the risk categories.

Figure 2: Effect of Risk Mitigation Strategies (Chopra and Sodhi, 2004)

Cost of risk mitigation

Very often risk mitigation not only comprises a laborious decision process, but also the resulting strategy implementation can mean a huge financial expenditure. Some strategies on the other hand are not only capable of reducing risks but can also increase profitability. Figure 3 highlights the dilemma. The goal should be to jump from the current position “X” to a higher risk-return curve (eg. “A”), instead of moving within the existing decision space.

Figure 3: Trade-off between Risk Reduction and Cost/Reward (Chopra and Sodhi, 2004)

What-if analysis

The authors suggest to use a what-if scenario analysis to assess a companies / supply chains propensity towards certain risks. In the matrix displayed in figure 4 the risk categories are further separated into supplier-related, internal and customer-related parts.

Figure 4: Exploring Scenarios using What-If Analysis (click to enlarge; Chopra and Sodhi, 2004)

Risk and reward

To balance risk versus reward the authors recall three essential relationships:

The cost of risk reduction increase with the level of risk (the higher the risk the higher the cost to mitigate)
Risk pooling reduces the cost to mitigate risks (eg. pooling demands of several customers reduces the cost to mitigate the risk)
The pooling effect is more pronounced when the pooled risks are very high

Depending on the level of risk and the cost of a mitigation reserve the authors suggest four generic strategic approaches to how to mitigate risks under different circumstances (figure 5).

Figure 5: Risk Mitigation Strategies depending on the Level of Risk and the Cost of Risk Mitigation (Chopra and Sodhi, 2004)

Tailored strategies

Finally the authors show examples of risk mitigation strategies and when to use them. The results are shown in figure 6.

Figure 6: Strategies for Risk Mitigation (Chopra and Sodhi, 2004)

Conclusion

I was surprised by the number of topics the authored covered in a single paper. They really tried to cover the whole risk mitigation process, and they succeeded.

Of course, most of the presented results are not really new and not covered in depth, but they make a excellent basis for strategic, business oriented discussions on supply chain risk management.

One drawback though: I did not really understand why exactly those risk categories (figure 1) were chosen. From my point of view the those are not very intuitive since the categories are not exclusive and thus overlap (eg. a disruption can also lead to a delay. Or why is a information infrastructure breakdown a systems risk and not a disruption?). This makes it harder to use the risk categories efficiently. But the categories could easily be replaced by another framework (eg. by Christopher and Peck, 2004)

Reference:

Chopra, S., & Sodhi, M.S. (2004). Managing Risk To Avoid Supply-Chain Breakdown MIT Sloan Management Review, 46 (1), 53-61

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Alternatives for Developing Supply Chain Simulation Capabilities

Daniel Dumke — Mon, 13 Jun 2011 14:54:00 +0000

Today I would like to talk about a non-essential, but helpful part of supply chain management: Simulation.

Simulation can be used in a supply chain setting on many different levels. On a strategic level there are models to analyze scenarios for the optimal locations of one’s factory, on a tactical level inventory management and distribution policies are treated and on the operations side route-optimization is a generally used. Of course there are also non-simulation models for these tasks, but this article is not about the pros and cons of that.

The goal of this article is to give you an overview what general differences exist between the approaches to build a non-specialized capability for supply chain simulation.

Approaches for building simulation capabilities

Simulation tools can be categorized into the following scheme.

Data driven simulators, can be defined as a simulation model which can be parameterized by providing data (e.g. by using spreadsheets), it’s purpose is to model a specified set of systems.

Pros: Usually easy to configure for the user

Cons: Less flexibility, since the simulator can only interpret parameters which its programming allow; standard graphical representation, which fits for the set of systems may not be optimal for one special case.

Interactive simulator, builds on modules for supply chain elements. Those have to be arranged and parameterized by the user.

Pros: due to the modularity the visual representation can come very close to the real supply chain

The sub-model approach, is comparable to the module approach, but instead of providing closed modules, models for a sub-system are used.

Pros: The user can better specify the level of detail he needs and also have differing levels of detail for different supply chain tiers.

Cons: Increased requirements for the abilities of the user

Furthermore there are:

Simulator extensions, are extensions written for a general purpose simulation software, which enhance the capabilities to model a specific set of systems.

Object class libraries, are an even lower level where objects are predefined, but not bundled with a simulator, but built on a general programming language.

Comparison and trade-offs

After defining the different approaches the author then compares the different approaches (see figure 1 and 2).

Figure 1: Flexibility / Effort Trade-Off to develop a generic Simulation Capability (Jain, 2008)

Figure 2: Flexibility / Effort Trade-Off to develop a single Model (Jain, 2008)

There is no golden rule of choosing the right approach for building simulation capabilities, but there are some important criteria and trade-offs to select the best approach.

Figure 1 focusses on the development of a generic simulation capability, which could be used to simulate a wider range of problems. If the objective is to develop only one model for a specific purpose (figure 2) the major difference is, that the effort for using sub-models is there higher than using the interactive simulator, since the learning curve for the former is steeper.

Jain names the following criteria, which should be used to decide on the correct approach:

Effort for developing simulation model instance vs. effort for developing a generic simulation capability

Flexibility of the acquired knowledge

Base software to be used

Role of the user in the simulation process

Animation capabilities of the selected product

Utilization of standards (by the simulation software), e.g. in data import / export, model format

Maintenance efforts needed for the capability

Execution time of the simulator

Conclusion

I think Jain could have made a better job to make the difference clear between the decision which software tool to acquire and which capabilities are needed at a company.

These decisions are of course interrelated, but since especially for a larger company it may be also an option to buy capabilities externally or by employing specialists.

For a complete framework the goal orientation was missing as well. For me the first step in such a process would be to define the company’s goals which then lead to the needed capabilities.

But, the reason why I presented this article is that I still think it contains some good classifications to select a fitting approach for creating simulation capabilities.

Reference:

Jain, S. (2008). Tradeoffs in building a Generic Supply Chain Simulation Capability Proceedings of the 2008 Winter Simulation Conference, 1873-1881

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Advanced Concepts in Simulation

Daniel Dumke — Wed, 06 Apr 2011 19:00:00 +0000

Computer simulation has not been used on a professional scale until after World War II, and also then mostly for military uses like war games or simulations of atomic bomb explosions nowadays.

One of the first scientific papers on simulation has been published in the late 70s by Ören and Zeigler. They aggregate some fundamental knowledge about simulation and suggest an conceptual model for simulation, which I want to introduce today from the perspective of a supply chain

Components of Simulation

Simulation programs can be described completely using the following terms:

Model Structure, describes the static and dynamic aspects of the model, like components of the model, variables and rules for interaction (supply chain context: echelons and elements of the echelon)

Model Outputs, contain all variables and functions that can be observed during and after execution (eg. lead times, profit of the chain)

Input Scheduling, when is the model fed with what inputs (eg. how is demand generated)

Initialization of Simulation, what are the starting parameters of the model (eg. are queues already loaded?)

Termination of Simulation, what are the conditions for model termination, may be after specific time or specific model states

Data Collection, how and when is the data during simulation collected

Simulator, the simulator carries out the model’s instructions to generate the new model states (nowadays the Simulator is usually a software like Arena, Anylogic or similar)

All those elements can be found within modern simulation tools, since they do not only include the Simulator component, but also aids to model and control the supply chain.

Simulation Models can be further characterized by the “language” they are using. First, is the model using a continuous or discrete time scale. Second, orientation the model: block- or expression-oriented; and last, what is the world view expressed in the model by differential equations, events or process interactions.

Experiments

After modeling a supply chain you can do experiments using the model, but of course also in reality (eg. increasing buffers or changing inventory policies). This would allow the experimenter to validate and compare the models output with the real changes. Of course in a supply chain context experiments in reality are confined to a very small range of parameter variation without compromising the ongoing operations.

This discussion focusses on the object of the experiment. But there are other components of an experiment. The experimental frame is defined as

a limited set of circumstances under which the system is to be observed or subject to experimentation,

so the settings which are used for above mentioned components of a simulation.

Advanced Concepts and Conclusion

The authors suggest some concepts for advancing simulation which are standard in nowadays simulation software. For example they describe a closer linking of model and experiment or demand capabilities for easy design of models and experiments.

So after all a very insightful article in the historical development of simulation. This has not been discussed here, but of course there are pros and cons for simulation. There are strong opponents of using simulation in the supply chain context (eg. Sodhi and Tang, 2009), since at least when using optimization there will always be an optimality gap. Instead mathematical modeling should be used.

But the huge amount of literature using different kinds of simulation (eg. reviewed here) shows that supply chain management can benefit very much from simulation, since the method is probably more easily understood, than some mathematical models.

Reference:

Oren, T., & Zeigler, B. (1979). Concepts for advanced simulation methodologies SIMULATION, 32 (3), 69-82 DOI: 10.1177/003754977903200301

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Be prepared for the Unthinkable

Daniel Dumke — Wed, 30 Mar 2011 09:07:00 +0000

Today I just want to highlight a short article from “The Conversation” blog at the Harvard Business Review for you to read. The article, which can be found here, was written by Harold Sirkin, senior parter at the Boston Consulting Group.

Problem

Sirkin describes the problem of disruptive events referring to the black swan analogy. In hindsight most people can explain the cause and effect relations of the Japanese disaster with ease, but beforehand most companies belief that such events could not be predicted, or prepared for. Sirkin disagrees, and suggests the following measures to improve performance in extreme situations.

Measures

Preparation for the unlikely is key to lowering the impact on the own company, this includes a diversification of the supply base and a locking of supplies before shortages occur.

Companies should furthermore invest in flexibility, so producing more products locally and lower the fixed cost, eg. by outsourcing.

As you see it is a really short article…

Conclusion

Of course this is no research paper, with elaborate methodology. I see it more as a business insight, what are companies doing. Or in this case what are the current recommendations of the consultants.
For my critique I would like to make three points:
I found it rather confusing how to fill the gap between the companies which do not see themselves able to predict disruptive events and the consultant who recommends preparing for the unknown. I think what Sirkin implicitly means is that it is not about modeling specific scenarios (like the Japan earthquake) and assigning probabilities to them. But to think about more general ways to increase the resilience of the supply chain, which can be helpful for a large number of different scenarios.

Furthermore, the recommended measures miss the current trend for cost minimization and how to align both goals. I could well imaging another BCG consultant telling the same company to reduce supplier redundancy and operational flexibility to reduce cost. So perhaps risk mitigation is just not for all?

Most importantly, this article also follows the Black Swan, since it is only written in hindsight as well. As you can see from the other publications of Sirkin on the HBR, which are mostly about supply chain innovation and change management, consultants fall for the trending topics as well. This is a problem for disruptions because they usually do not happen cumulatively but with long intervals between them. So the right time for this article would have been half a year ago or one year in the future. But everybody is wiser after the fact, and forgets soon thereafter. (Since I did not do a full review of all publications of Sirkin, this conclusion may be skewed.)

Reference:

Sirkin, Harold L. (March 28, 2011 ). How to Prepare Your Supply Chain for the Unthinkable Harvard Business Review – Blogs Link: blogs.hbr.org/cs/2011/03/why_are_supply_chains_eternall.html

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