Samir Dani

Fragile Food Supply Chains: Reacting to Risks

Daniel Dumke — Mon, 05 Mar 2012 12:22:00 +0000

The foods supply chain satisfies one of the most basic Maslowian needs.
Interruptions can quickly become major crisis. Assessment and reactions to risks therefore seems to be a vital point.

This article presents a framework by Dani and Deep on how specific food supply chain risks can be analyzed and how reactions can be tailored.

Status quo

The authors start by sketching the status quo of a foods supply chain and the trends affecting it.

Figure 1: Food Supply Chain Schematic (Dani and Deep, 2010)

A typical foods supply chain consists of six echelons starting at the farmer. The second stage is usually an aggregator/marketer who provides the input for the processing facilities. The distribution stage starts with the wholesaler which delivers the product to the customer usually via a retail stage.

Several trends affect the supply, processing and demand for food products:

Consumer: In the past decades demand shifts have been observed, leading for parts to more meat based diets (e.g. China) or to a more health-concious consumption.
Food standards: Internal (company-made) and external (government-made) standards seem to be on the rise to provide a high degree of food safety.
Technology: Advances especially in IT technology and electronics have made it possible to provide uninterrupted tracking within the foods supply chain to improve safety and responsibility further.

Research goal and method

The aim of the presented paper is to answer the following questions:

Question 1: If a risk were to materialise, what can be done to minimise its impact?

Question 2: What are the reactive risk management peculiarities of the food supply chain?

The paper is build as a meta-study using secondary sources: “research publications, journal papers, newspaper reports, articles from business magazines, websites and government reports”

From these sources thoughts and ideas were extracted and then clustered with the goal to distill common themes which can be used for the general framework.

Results

The following themes were identified:

Speed of response: Speed of response is often cited as the most crucial variable in controlling the effects of risk. […] A quick response to food supply chain incidents may include: 1) Deploying a crisis management team, 2) Scope and extent definition of the problem, 3) Identifying affected areas and their impact on critical activities, 4) Recall procedures.

Communication: Communication and information sharing is often beneficial in controlling the damage caused by a risk. […] Crises are situations of extreme stress and therefore an organisation needs to ensure that they are able to provide coherent, precise and timely information to all concerned members.

Escalation: Escalation refers to the procedure for making information available to the most relevant authority.

Resource and fund availability

Multi-partner collaboration

Leadership: Leadership is an essential catalyst for the above factors to combine.

Two distinct risk types can be separated:

Type I: These are risks which are concerned with food safety, as well as maintaining a secure supply of food. These are differentiated on the basis of the responsibility and involvement of regulatory authorities. Food contamination is the most prominent of these risks and involves any incident which may constitute a public health emergency of domestic or international concern.

Type II: These are all other risks which affect the supply chain but do not have a direct impact on food safety. The involvement of these types of risks is primarily the organisation and its direct supply chain. These risks include transportation strikes, loss of power, flooding, etc.

Combining these aspects into a single conceptual model results in the following diagram.

Figure 2: Conceptual Model (Dani and Deep, 2010)

Case based validation

The author then use three case studies (summarized in figure 3) to post-hoc validate their model.

Figure 3: Case Studies for the Framework Validation (Dani and Deep, 2010)

This is the conclusion of the author of what went wrong in the first case (for the other cases I refer you to the original paper):

Case highlights:
One of the biggest cases of food product recall in US history is the most recent case of outbreak of illness caused by Salmonella typhimurium. The FDA and Centres for Disease Control and Prevention (CDC) identified the source as peanut butter and peanut butter paste in the processing plant of Peanut Corporation of America (PCA) at Blakely, Georgia.

Case analysis:
In this case, clearly the type of risk was type 1. Referring to the conceptual model depicted in Figure 2, it can be seen that although all entities were involved within the crisis management process, the speed of response was very slow causing failure of the PCA [Peanut Corporation of America]. The Peanut Corporation, despite being aware of a salmonella poisoning investigation at King Nut Corporation, did not initiate any damage control steps. The FDA inspection at the PCA started on 9 January, but the recall announcement did not happen until 13 January. The collaboration, communication and information between different members were possibly inadequate as the recall list grew from 21 products to all products manufactured at the location. The date of recall moved back to any product manufactured at the facility since 1 January 2007, which highlights gross inconsistencies in process controls within the organisation. There were no escalation procedures set up until the FDA took control over the crisis management process. Also, multi-partner collaboration was lacking as a second plant of PCA was also found contaminated with Salmonella 2 weeks after the initial inspection at PCA.
In the investigation (Weise and Schmit 2009), it was highlighted that the leadership of the company had the chance and information to intervene much earlier in the process to stop the con- taminated product reaching the customer. However, poor leadership and improper risk mitigation procedures led to the risk propagating widely and causing human fatalities. As per the model, even though the FDA intervened and took over the risk mitigation process, the speed of response was slow and there were no proactive measures instilled by PCA with regard to risk mitigation. Hence with reference to the model, although this is a type 1 risk and process A is involved, the success of the intervention is dependent upon the capability of process B.

The authors conclude that the framework is a good fit to analyze cases after the crisis happened. Furthermore they are confident that it should also be applicable for proactive and predictive analyses.

Conclusion

I think focussing the research questions to include only food supply chains in this case results in a very focussed model of the impact factors in food supply chain resilience.
On the other hand as often (this may be good or bad) I fail to see why this model should only be applicable to disruptions in foods supply chains. Leadership, communication, and collaboration are key factors in many (most?) supply chains and in times of disruptions even more so.

Reference:

Dani, S., & Deep, A. (2010). Fragile food supply chains: reacting to risks International Journal of Logistics Research and Applications, 13 (5), 395-410 DOI: 10.1080/13675567.2010.518564

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Predicting and Managing Supply Chain Risks

Daniel Dumke — Mon, 21 Nov 2011 15:40:00 +0000

I often recommend Supply Chain Risk: A Handbook of Assessment, Management, and Performance as a good overview of supply chain risk management literature. In this article I summarize chapter four of the book on the problem and processes to predict supply chain risks.

Structure and basics

In first section the author starts by defining the basics of risk and uncertainty. The next section briefly introduces different categories for supply chain risks:

Internal to the firm: Process, Control

External to the firm but Internal to the Supply network: Demand, Supply

External to the network: Environmental

These risk can then impact the different dimensions of a supply chain:

Physical movement of goods
Flow of information
Flow of money
Security of the firm’s internal information systems
Relationship between supply chain partners
Corporate social responsibility and the effect on a firm’s reputation.

The next section provides an overview on supply chain risk management, its steps and mitigation strategies.

Predicting Supply Chain Risks

One of the main requirements for an effective proactive risk management process is to obtain good estimates of the probability of the occurrence of any particular disruption and accurately measure the potential impact.

So the estimation of probabilities and impacts, depending on the characteristics of the supply chain and the environment it might be harder to identify all risks and assess them.

The author describes two tools for the estimation of risks: Data Mining and Failure Mode Effect Analysis (FMEA).

Data Mining

The role of data mining is to analyse historical data, to improve prediction capability. Some of the common analytic approaches used by data miners are:

Estimation of the parameters of past performance: Means, Standard deviations, Correlations, and Associations for hypothesis testing

Classification: Segmentation, or Clustering of data units to facilitate the modelling process

Construction of a functional relationship: or model between responses and explanatory variables.

The following tools should be used by a data miner:

Estimation: […] These tools will not lead to patterns but are more useful in analysing the data to identify the most relevant sets of data to concentrate further analysis.

Clustering/segmentation: This approach is used to logically group observations on the basis of similarity in their characteristics, reducing the level of heterogeneity in the data.

Classification/discrimination: The process of assigning observations to a predetermined number of classes.

Prediction: Formal mathematical models are built for the purpose of predicting the occurrence of the phenomenon.

Failure Mode Effect Analysis (FMEA)

The goal of the FMEA is to

Recognise and evaluate the potential failures of a product or process and the effects of that failure.

Identify actions, which could eliminate or reduce the chance of the potential failure occurring.

Document the entire process.

The author highlights ten steps of the process:

Define scope, functional requirements, design parameters and process steps.

Identify potential failure modes: Failure modes indicate the loss of at least one functional requirement. It is the manner in which a failure occurs. This step in the process takes into account a foresight view (based on past experience and any new information) of what could cause a failure to the system or process.

Potential failure effect: This step investigates the effect the failure will have on other entities or processes.

Severity: “How bad” or “serious” the effect of the failure mode is. Usually severity is rated on a discrete scale from 1 (no effect) to 10 (hazardous effect). Severity ratings of 9 and 10 indicate a potential effect of high importance and this could typically be a safety or government regulation issue. Critical effects need deeper study for all causes to the lowest level, using a method of Fault Tree Analysis.

Potential causes: These are the causes of the failure. In this step, all causes that can be attributed to the failure occurring are investigated.

Occurrence:This is the likelihood of the event happening (i.e. failure in the system) on the basis that “the cause occurs”. FMEA assumes that if the cause occurs, failure will occur too. The probability of occurrence is ranked from 1 to 10, where 1 signifies a remote probability of occurrence and 10 a very high probability of occurrence.

Current controls: The objective of the controls is to identify and detect the deficiencies and vulnerabilities as early as possible. This step looks at the current processes in place to mitigate the failures (if already known).

Detection: A subjective rating is assessed corresponding to the likelihood that the detection method will detect the first-level failure of a potential failure mode. This is ranked from 1 to 10, where 1 signifies that it is unlikely to detect and 10 signifies a very high detection potential.

Risk Priority Number (RPN): These are used to prioritise the potential failures and are calculated as “Severity Occurrence Detection ranking”.

Actions Recommended: The team should then select and manage subsequent actions needed to locate and control the situation.

Predictive – Proactive Methodology

Based on these methods the author suggests a predictive-proactive methodology (figure 1) to manage risks.
The two main steps are called predictive mode and proactive mode. In the predictive mode, the focus is on gathering of information on the potential risk sources. The proactive mode focusses on the identification of the risks within the supply chain and assessing the risk level and setting the strategic objectives of the company.

Figure 1: Schematic Diagram of the Predictive-Proactive Approach Supply Chain Risk Management Approach (Dani, 2006)

Conclusion

I read the chapter with a focus on the description of the prediction tools. The model on the other hand is a nice add-on which could be used as food for thought. But I find it a little bit too simplistic and a little bit confusing. It describes a basically linear process (from a risk to the disruption), but why then did the author choose not to represent this in a linear way, but in a diagram with two main parts/frames instead of a clear process and why are there two different types of frames (dashed and solid)?

Reference:

Dani, S. (2006). Predicting and Managing Supply Chain Risks Supply Chain Risk (Ed. Clare Brindley), 53-66 DOI: 10.1007/978-0-387-79934-6_4

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Agile Supply Chains and Uncertainty

Daniel Dumke — Wed, 27 Apr 2011 12:49:00 +0000

There are many definitions of agility. A supply chain can be defined as agile, when it is flexible and responds quickly to customer needs. Agility can also be seen as a measure to mitigate supply chain risks, building on this thought Dani and Ranganathan (2008) developed a model to mitigate risks using the concept of agility .

Concept for Agile Risk Mitigation

The authors develop the model based on two premises: (1) scenario planning is used to identify supply chain risks and (2) the system is designed to react fast and flexible to mitigate the risk.
Figure 1 shows the resulting concept.

Figure 1: Concept for agile Supply Chain Risk Mitigation (Dani and Ranganathan, 2008)

Two general mitigation “arms” are necessary. One for the foreseen risks which can be mitigated proactively and unforeseen risks which have to be reduced reactively.

Model Validation

This model constitutes a theory build by the authors on recent literature and own experience. To raise the credibility a theory has to be validated. In this case the authors decide to use the Ericsson case (if you are interested, have a look at my review of the case here).
After a breakdown of a Philips semiconductor plant in 2000, Nokia and Ericsson were competing over the remaining capacity. Nokia reacted fast and acquired spare capacity by Philips and other suppliers. Ericsson reacted more slowly and failed to obtain the necessary component and lost about 400 million USD.

When applying the above mentioned model, it is clear that both companies had not thought about this scenario, however Nokia already beforehand had established a fast communication structure which permitted it to react in a fast manner.

Conclusion

The article also contains a extended literature section defining the terms used, building from uncertainty and risk, to risks in supply chains, scenario analysis and the agility concept. Building on that the concept presented on less then one page seems to be quite underweighted. The main statement of the article is that: (1) planning of future scenarios is important to anticipate and mitigate possible risks, (2) depending on the planning mitigation of disruptions happening can be done proactively or reactively. (3) Especially for reactive mitigation the agility of the company plays a major role in the success.

Reference:

Dani, S., & Ranganathan, R. (2008). Agility and supply chain uncertainty: a scenario planning perspective International Journal of Agile Systems and Management, 3 ¾, 178-191

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