measuring

Measuring agile Capabilities in the Supply Chain

Daniel Dumke — Wed, 13 Apr 2011 12:22:00 +0000

Today I introduce you to the process of measuring agility in a supply chain. Agility is a major concept in the research of the last 10 years or so. I already have written some articles on this topic:

Consequently, the goal of today’s article is to expand on those and define what are the key factors of SC agility and how it could be measured.

Method

The paper presented is based on two sources: an empirical investigation of agile capabilities in Europe and a literature review of the current research available.

The concept developed is based on the literature review and it is then tested using the survey.

Agility Framework

Agility has already been described early in four basic dimensions (according to Goldman et al.,1995): 1) Enriching the customer, 2) cooperating to enhance competitiveness, 3) organizing to master change and uncertainty, 4) leveraging the impact of people and information

An extensive literature review leads to the agility framework shown in figure 1. The dimensions are described below (quoted).

Figure 1: Presentation of the Agility Framework (van Hoek et al., 2001)

Customer sensitivity
Customer centred versus product centred logistics policies (ten questions): assumes that “agile” policies emphasize customers and markets, while “lean” policies focus on the elimination of waste in products and processes.
Virtual integration
Immediate conversion of demand information into new products using knowledge-based methods versus multi-stage, multi-function methods (three questions): assumes that agile policies focus on instantaneous demand capture, interpretation and response while lean policies emphasize stable production periods and protecting the “operations core”.
Process integration
Self management versus work standardization (five questions): assumes that agile policies focus on operator self- management to maximize autonomy and immediate response, while lean policies emphasize work standardization to ensure conformance to quality and productivity standards.
Network integration
Fluid clusters v. long term supply chain partnerships (six questions): assumes that “agile” policies emphasize fluid clusters of network associates, while lean policies focus on a more fixed set of long-term stable partnerships.
Measurement
Capabilities versus “world class” measures of performance (seven questions): assumes that agile policies are based on broad-based measures that underpin capabilities, while lean policies emphasize “hard” measures such as quality and productivity only.

The authors go further and analyze how these dimensions are represented other common supply chain concepts like just in time or lean production (figure 2).

Figure 2: Representation of Agile Dimensions in common SC Concepts (van Hoek et al., 2001)

Changed Approach

The “agile approach” changes supply chain management as a whole. The authors compare the traditional vs. agile approach very extensively. A summary can be found in figure 3.

Figure 3: Approaches to Agility in Supply Chain Management (click to enlarge; van Hoek et al., 2001)

Conclusion

Overall this article is a good starting point to answer the question “what is agility?”. For business it is important to transfer this framework into their measurement systems to get a benchmark how well they perform, since “you cannot manage what you don’t measure”.

Reference:

Hoek, R., Harrison, A., & Christopher, M. (2001). Measuring agile capabilities in the supply chain International Journal of Operations & Production Management, 21 ½, 126-148 DOI: 10.1108/01443570110358495

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Definition of Risk

Daniel Dumke — Wed, 05 Jan 2011 12:59:00 +0000

What is risk anyway?
I read this paper already some time ago. It is very important to have a clear definition of the terms used in research. But from my previous experience I know that also in business a clear understanding of the different aspects of risks is important to stay consistent.

Case Study

At a client I was involved in a company wide risk assessment. The participating middle managers were required to list and assess relevant risks using an Excel sheet. There were predefined categories for impact (eg. less than €1m, between €1m and €10m and above €10m) and probability (eg. p 50%).
The survey was therefore aimed at high impact, low probability events. One of the biggest tasks during this assignment was to train the managers on how these categories should be interpreted. But honestly, this categorization does not make this task very easy. Suppose the following disruption: missing employees due to an outbreak of the swine flu. The line in Excel might look like this:

Swine Flu: p But if you think about it, this task is not very straight forward, the following questions remain:

There is of course also a more remote chance that the outbreak cannot be contained so quickly as assumed here and the loss might be larger than € 1m. How should you account for this? Add another line, with p Risks can be differentiated into different kinds like business risk, social risk, economic risk, safety risk

Risk and uncertainty
There is a difference between risk and uncertainty, where risk is when you know about the probabilities and uncertainties when you don’t. So most real life situations (beside the Casino) you are in the realm of uncertainty. If you are using uncertainty, then you have to think about the probabilities of the probability, how likely is it that your estimated probabilities are correct?

Relativity of risks
There is usually no objective measure for risk, only perceived risks.
The notion of absolute risk always ends up being somebody else’s perceived risk quantitative definition of risk

Risk should be view using the following triplet
Scenario, Likelihood and Consequence

There are different types of damage
There is no continuous measure which you can use for loss of life vs. loss of property

Conclusion

Kaplan shows several important factors of risks that are relevant when applying the concept of risk in a business setting. Several other researchers have built on his work, and for example extended the definition of the consequence of risk, to not only contain a single digit for the impact but also aspects as duration, etc.

Reference:

Kaplan, S., & Garrick, B. (1981). On The Quantitative Definition of Risk Risk Analysis, 1 (1), 11-27 DOI: 10.1111/j.1539-6924.1981.tb01350.x

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Measuring Supply Chain Performance

Daniel Dumke — Mon, 22 Nov 2010 12:47:00 +0000

Last week I conducted another Interview for the empirical part of my research. And we also discussed how to measure performance within the SC. As it turns out, multiple measures, namely service, cost, working capital are used. Sadly in literature many authors still focus on a single measure only and I wanted to know more about it. So I read an article by B. Beamon (Measuring Supply Chain Performance) to get an overview over performance measures used and how to select the right one(s).

History of Supply Chain Performance Measures

Supply chains consist of many different companies and within those, various functions. So first measures have relied on characterizing individual systems like production, distribution, or inventory management. In literature the measures have often been categorized into aspects of quality, time, flexibility, and cost.

But most often cost is used as an only measure or cost and another measure for customer responsiveness is applied. Other more qualitative measures, like customer satisfaction or concerning risk management, are seldom used due to their restricted applicability in quantitative models.

Measures are generally thought of being effective if the following characteristics are fulfilled:

inclusiveness (measurement of all pertinent aspects)
universality (allow for comparison under various operating conditions)
measurability (data required are measurable) and
consistency (measures consistent with organization goals)

Performance Management System

The last bullet point highlights another topic: It is also important not only to focus on the measures used, but also to reflect on the performance measurement system as a whole. Especially, it is important to consider the effects of a specific corporate strategy on the performance measures used. Thus there are several caveats when evaluating options for performance measures:

If using only one performance measure make sure that it can adequately describe the performance of the system in focus.
When using cost as (only) measure, be careful to include all necessary costs in the calculation. Pitfalls include cost of obsolescence or rework due to engineering changes
Align performance measures with the strategic goals

Framework

Individual performance measures are usually non-inclusive so the goal for a performance management system should be to include more aspects. Beamon suggest to focus on measures for resources ®, output (O) and flexibility (F) (see figure 1 and 2).

Figure 1: Framework for Performance Measurement (Beamon 1999)

Figure 2: Framework Supply Chain Measurement System (Beamon 1999)

Examples for resource measures are: total cost, distribution cost, inventory cost.

Output can be measured quantitatively as number of items produced or number of on-time deliveries or profit; alternatively qualitative measures like customer satisfaction or quality can be used.

Flexibility is harder to measure, Beamon details on a quantitative approach for measuring it, focussing on volume, delivery, mix and new product flexibility.

Different Aspects of Flexibility (Beamon 1999)

Conclusion

The use of simple performance measures is tempting, since simple measures are more easily implemented into numerical models; however, by limiting the scope of the performance measurement, these models ignore important performance trade- offs.

So it is obviously important to focus on meaningful measures. While reading the article I didn’t quite understand the authors relation to profit as a performance measure. Return on Investment for example is listed under the category cost-measures, profit itself is listed under output. Neither is really correct if you define profit as revenue minus cost since, profit affects both sides, resources and output.

Reference:

Beamon, B. (1999). Measuring supply chain performance International Journal of Operations & Production Management, 19 (3), 275-292 DOI: 10.1108/01443579910249714

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Managing Information Risks

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

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

Topic / Methodology

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

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

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

Framework

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

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

Risk Measurement

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

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

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

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

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

Conclusion

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

Reference:

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

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Risk from the Managers Perspective (Part 2)

Daniel Dumke — Fri, 12 Nov 2010 11:37:00 +0000

Today I will write about the implications of the risk understanding by managers covered in Part 1 of this series. After observing the mentioned factors on how managers perceive risks the authors categorize their conclusions in three areas.

Insensitivity of Risk Taking to Probability Estimates

[Decision makers] do not trust, do not understand, or simply do not much use precise probability estimates.

This quote summarizes one of the major findings. Probabilities – not risk at large – are not well understood and handled by managers. This is especially fatal when considering disruptions, events with low probabilities but high adverse effects. Because these events are then ignored since managers are overwhelmed by their sheer number. And of course this leads to organizations which are always surprised by events that they did not think of.

In some of my interviews during my research with business executives I spotted the same behavior: supply chain managers that never have thought of the possibility of a strike, a fire or another catastrophe. But the number of managers on the other end – so really managing these risks – seems to be even lower.

The Importance of Attention Factors for Risk Taking

Empirical research – as for example in Part 1 – indicate that risk preference varies with the context in which the decision has to be made. This includes everything from individual feelings of the manager, how and by whom the data is presented, the company context, etc.

In human decision making there is always the tendency to focus on a few key aspects of a problem at a time. Therefore it is of the utmost importance to define and calculate the key metrics correctly, but often not only the measures may pose a problem but also the target levels to assess performance (eg. break even) and survival levels. These two reference points partition possible states into three: success, failure, and extinction; and with that massively influence decision making in each of the three categories

Risk Taking, Gambling, and Managerial Conceit

Managers do care a lot about their reputations for risk taking, on the other hand are eager talk about how they assess the qualities of others in this regard and on the inadequacy of organizational incentives for making risky decisions intelligently. So risk taking seems to be a very important part of their definition of life as a manager. It is important to take risks, but not to “gamble”.

Hence, it is amazing to see that managers are still rewarded for good outcomes rather than good decisions (making).
The authors see this point very critical

Society values risk taking but not gambling, and what is meant by gambling is risk taking that turns out badly. Thus, risky choices that tum out badly are seen, after the fact, to have been mistakes. The warning signs that were ignored seem clearer than they were; the courses that were followed seem unambiguously misguided.

Conclusion

The authors conclude with the following recommendations for the proceeding:

Short run: Measures have to be designed which accommodate for these findings, but more importantly there has to be a concept on how to steer risk taking by managers.
Long run: These findings might have implications for the education of managers to improve the individual risk taking.

We may prefer to have managers imagine (sometimes falsely) that they can control their fates, rather than suffer the consequences of their imagining (sometimes falsely) that they cannot.

This article has been written in 1987, nearly one generation ago, but to be honest I believe most of the results are still true nowadays (to my knowledge there has not been another similar study, yet). Sadly, I do not think that neither companies have improved decision making nor education has picked up much. In my point of view the handling of decision making under uncertainty should be supported by IT and the organization.

Reference:

March, J., & Shapira, Z. (1987). Managerial Perspectives on Risk and Risk Taking Management Science, 33 (11), 1404-1418 DOI: 10.1287/mnsc.33.11.1404

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Risk from the Managers Perspective (Part 1)

Daniel Dumke — Wed, 10 Nov 2010 08:43:00 +0000

Today I finally read one of the most cited articles on subjective risk in general. In 1987 March and Shapira set out to shake up the existing theories on the perception and processing of risks by managers. Accordingly, they aggregated the information from various surveys on this topic.

The article is called “Managerial Perspectives on Risk and Risk Taking” and it can be downloaded here as PDF and I really recommend reading it.
In the first part I will analyze major empirical findings on how managers perceive risks, the second part will cover the implications from that and will be published this Friday.

Definition of Risk

Usually risk can be defined as a combination of an outcome and uncertainty / probability component. And most people would agree: An alternative with higher outcome is better and one with more uncertainty is worse. Decision theory therefore used the concept of the variance of the outcome distribution to describe risks and many models are based on this. But does this reflect how managers defines risks?

Probably not, the authors show that…

Most managers do not care about uncertainty of the positive outcomes: Eighty percent of the executives said they considered the negative ones only
For managers risk is not so much about probability, but much more about the magnitude of possible bad outcomes, so managers do not seem to be risk averse in general but more loss averse (with a clear focus on the amount not the probability)
Although many managers use numbers for quantifying risks and 42% argued that there was no way to translate a multidimensional phenomenon into one number. Interestingly 24% of the same managers felt it could be done and with additional probing said that actually it should be.

The last point is supported by a common quote:

A senior vice-president observed: “You don’t quantify the risk, but you have to be able to feel it.”

Attitudes toward Risk

It has been acknowledged for some time now that decision makers do not necessarily act purely rational, so several concepts of risk aversion have been developed from a theoretical point of view. But how do managers think about risks.

There are individual differences on the degree of risk aversion, but these differences are less influential than other differences in “incentives and normative definitions of proper managerial behavior”
Some managers show a very negative attitude towards individual risk taking (vs. group decision or “gambling”), this behavior is particularly characteristic of managers who see risk as single construct of the magnitude of the expected loss (ignoring the probability)
Managers tend to believe they are greater risk takers than they are in reality
Motivations to take risks

Risk taking is essential to success in decision making
Risk taking is an expectation on their job
Emotional pleasures of risk taking

Most managers would take more risks when faced with a failure to meet targets than when targets were secure. Furthermore, managers would take riskier actions when their own positions or jobs are threatened then when they are safe

Dealing with Risk

In theory dealing with risks is easy since there are alternatives to choose from and depending on the individual definition of risk and the risk preferences (see above) the correct decision is made. But the behavior of the decision makers shows distinctive features as well.
Very often manager focus on ways to reduce the negative outcomes while retaining the gain of a given alternative. The easiest way to do this is to reject the estimates which form the basis of the proposed alternative. And even after doubting the odds executives still feel that they will be better of since they expect being able to avoid the risks using their experiences.

Go on reading Part 2.

Reference:

March, J., & Shapira, Z. (1987). Managerial Perspectives on Risk and Risk Taking Management Science, 33 (11), 1404-1418 DOI: 10.1287/mnsc.33.11.1404

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Measuring the Bullwhip Effect in Supply Chains

Daniel Dumke — Mon, 23 Aug 2010 06:46:00 +0000

The bullwhip effect in supply chains has been around for some time now. The term “bullwhip effect” originated at Procter & Gamble, and is defined as: demand amplification across echelons within a supply chain. This describes the effect that end customer demand may be very static (as for “Pampers” by Procter & Gamble), but the demand experienced by the manufacturer or supplier shows amplified demand variations. (Fransoo and Wouters (2000))

Causes of the Bullwhip Effect

Lee et al. (1997) first analyzed the causes of the bullwhip effect:

Demand forecast updating
Demand forecast usually are based on the orders of the preceding echelon and not on the actual customer demand
Order batching
Orders are usually aggregated to batches to save cost.
Price fluctuations
Promotions and other effects can lead to price fluctuations of the product. When the price is perceived to be lower, orders go up and vis-versa.
Rationing and shortage gaming
For some products (eg. new iPhone) which may be short in supply, game theory suggests that it might be rational to order more than actually needed, since the number of delivered products usually is a percentage of the number of products ordered.

Measuring the Bullwhip Effect

The bullwhip effect is measured per echelon by the following formula:

Measuring the Bullwhip Effect (Source: Fransoo and Wouters (2000))

So first we standardize the standard deviation for incoming and outgoing demand. The bullwhip effect is measured as the quotient of these numbers.

Issues

The following issues arise when measuring the bullwhip effect.

Aggregation
To measure the bullwhip effect correctly one has to be aware of different data aggregation levels one could choose; one can aggregate demand by echelon, outlet, product, time. Each aggregation will give different insights and different levels of the bullwhip effect.
Incomplete data
There may be a conceptual imbalance between incoming demand and outgoing demand, furthermore the information on those values may be incomplete.
Filtering for causes
To analyze and act on the bullwhip effect, one has to understand the causes of the demand variations at hand.

Findings from case studies

Fransoo and Wouters (2000) find in their specific case study that,

filtering for causes of the bullwhip effect can be quite challenging.
the bullwhip effect caused by price fluctuations was negligible.
shortage gaming did occur and posed a major problem for the supply chain.

Reference:

Fransoo, J., & Wouters, M. (2000). Measuring the bullwhip effect in the supply chain Supply Chain Management: An International Journal, 5 (2), 78-89 DOI: 10.1108/13598540010319993

Lee, H., Padmanabhan, V., & Whang, S. (1997). Information Distortion in a Supply Chain: The Bullwhip Effect Management Science, 43 (4), 546-558 DOI: 10.1287/mnsc.43.4.546

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