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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Strategies

Improving Performance in Food Supply Chains by Reducing Uncertainty

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

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

Methods

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

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

Results case studies

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

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

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

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

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

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

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

Results simulation

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

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

Further results were generated using the simulation model:

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

Conclusion

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

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