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A Closed-Loop Traceability System to Improve Logistics Decisions in Food Supply Chains
A Closed-Loop Traceability System to Improve Logistics Decisions in Food Supply Chains: A Case Study on Dairy Products
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R. Accorsi, E. Ferrari, M. Gamberi, R. Manzini, A. Regattieri Alma Mater Studiorum - University of Bologna, Bologna, Italy
1. Introduction The food industry has experienced profound transformation. Companies are increasingly facing new challenges and issues: on one hand, the access to new and attractive markets, and on the other, the increasing number of products, the competition in a global market, and the consumers’ requirements in terms of quality and environmental sustainability of the products (Li et al., 2014). Furthermore, the recent scandals and frauds affecting food safety requirements elicit the attention of consumers and compel politicians to assess the responsiveness of food supply chains and their tracking and monitoring systems. Because of the leading role of the food industry in the European manufacturing sector, the food companies are more and more resilient and strive to adapt quickly and responsively to the incoming issues and challenges. The food and drink industry turnover is 1048 billion euro in 2014, with an increasing value of both agro-food and beverage export and import respectively of 86.2 billion euro and 63.2 billion euro (World Bank, 2014). The demand of food specialties by foreign markets grows with the importance of logistics processes and operations in food supply chains. To meet the growing trends of food demand (Ahumada and Villalobos, 2009) food products intensively travel across continents and oceans, thereby shifting the focus from the management of local agriculture models to the optimization of the global food supply chain. These trends are increasing the relevance of considering the related functions of the food supply chain: not only cultivation and food processing but also the packaging, storage, distribution, and control and monitoring of the product quality across its entire life cycle. These processes result in more complex supply networks, which emphasize the distance between the grower and the consumer and affect consumer awareness of the supply chain stages from farm-to-fork. Beyond the appealing shelves of the grocery store, food is grown, harvested, handled, stored, and shipped, and each of step may affect the product’s quality and safety. In order to assess how food supply chains affect the safety and the quality of food products, the prediction of food degradation (Labuza, 1982; Man and Jones, 1994), combined with the tracking and the environmental conditions experienced by products from farm-to-fork are fundamental (Manzini and Accorsi, 2013). Typically, next to Advances in Food Traceability Techniques and Technologies. http://dx.doi.org/10.1016/B978-0-08-100310-7.00018-1 Copyright © 2016 Elsevier Ltd. All rights reserved.
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biological variations, food quality is determined by time and environmental conditions, which may be influenced by the type of packaging, way of loading, and the availability of temperature-controlled packages, vehicles, and warehouses and generally by the complete factors configuring the supply chain. To properly control the safety and quality of food products across processing, packaging, storage and distribution, the development of accurate traceability and monitoring systems is recommended, particularly when the supply chain in out of the control of the producer. Regattieri et al. (2007) defined traceability as “the history of a product in terms of the direct properties of that product and/or properties that are associated with that product once these products have been subject to particular value-adding processes using associated production means and in associated environmental conditions.” An effective food traceability system is an important tool not only to manage food quality and safety risks, but also to promote the development of effective supply chains. Two main categories of traceability technologies and devices can be distinguished: identification tags (ie, barcode, label, RFID tag) which address a product or a general item with a specific code for identification purposes and data loggers (sometimes called “black boxes”), whose aim is to trace and record the environmental conditions and profiles experienced by a product (Piazzi et al., 2011). In the food industry, radio-frequency identification (RFID) systems can also embed sensors and thereby working as an identification black box for traceability and logistics, as well as for anticounterfeit purposes (Barge et al., 2014). The development of a traceability system based on an RFID black box for a food supply chain is an effective way to realize the so-called real-time control of the state of the food products (de las Morenas et al., 2014), which goes in agreement with safety and quality standards and regulations and customer satisfaction. When gathering the conservation conditions of a product in real time, a set of operative logistic decisions can be handled in run time to correct, improve, or modify the observed process. This approach would suggest to monitor those parameters (eg, time, temperature, humidity) having an impact on the quality of products. Unfortunately, the costs of tags and the investment required to build up real-time traceability infrastructures over the entire supply chain are not revenue-generating for companies, because of the low market price of food products, the extension of global supply chains, and the cost of primary packaging is often not reusable (Accorsi et al., 2014b). The so-called ex-post monitoring systems are less expensive and are built up through proper black box or data logger devices, tracking the environmental profiles of storage and distribution activities. The ex-post monitoring system protocol is based on inserting data loggers within the product primary packaging, gathering the data loggers at the end of distribution, and collecting the distribution profile data. Finally, an ex-post detailed analysis of the collected data identifies the most critical steps of food distribution and aids operative decisions (eg, packaging improvements) to improve the control of the food safety and quality. The main difference between a real-time and an ex-post monitoring systems, both illustrated in Fig. 18.1, is based on the rapid response of the former system enabling operative real-time decisions in response to events or stresses in the supply chain
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operations. This undoubtedly results in making the supply chain more resilient and responsive. Conversely, the ex-post monitoring system tracks the supply chain steps and gives feedback to the involved actors about what happened along the product storage, transport, and delivery after the product reached the customer. Therefore any possible corrective actions and improvement of the supply chain operations are handled ex-post driven by the findings from the tracking system. Highlighting what are indeed the issues affecting the products, assessing them in an objective and quantitative
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Figure 18.1 Comparison between real-time versus ex-post monitoring system.
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manner helps in redesigning the packaging, the truck loads, the storage conditions in a warehouse, the shipping container (Accorsi et al., 2014a), up to choosing more careful carriers and logistics providers and renegotiating with them the transport standards and rules. This process can be particularly onerous for a single company. The Food Supply Chain project aims to bring together knowledge, experience, and tools of investigation to improve the distribution logistics of food products, thereby protecting a very important sector of the regional and national economy. The ex-post monitoring system can be even more exhaustive whether combined with a closed-loop protocol, which includes the reconstruction and simulation in laboratory of the monitored environmental conditions (eg, temperature, humidity, vibration) experienced by the products, and the organoleptic and chemical analysis of the simulated products to assess the effects on product quality due to distribution. The closed-loop protocol was first introduced by Bartholdi and Mac Cawley (2011), as a main result of the Wine Supply Chain Council (http://wscc.scl.gatech.edu/), to analyze the international shipments of bottled wine and was extended by Manzini and Accorsi (2013) to other supply chains and products, as extra-virgin olive oil (EVOO) (Valli et al., 2013; Manzini et al., 2014a), fine chocolate (Manzini et al., 2014b), kiwi fruits (Manzini et al., 2014c), and others within the Food Supply Chain Project. This international project put together a number of international research institutions, such as Georgia Institute of Technology (USA), San Francisco State University (USA), the Commonwealth Scientific and Industrial Research Organization (CSIRO) (Australia), the Pontificia Universidad Catolica de Cile (Chile), the Mendoza Universidad (Argentina), the Council for Scientific and Industrial Research (CSIR) (South Africa), and the University of Bologna (Italy), whose aim is to analyze how the variation of certain factors (eg, temperature, humidity, vibrations) can affect the products during storage and distribution phases. Through the direct involvement of companies of the food industry, this project aims to highlight the critical issues affecting the current global food supply chain and to assess possible improvements to meet food safety and quality standards. As a methodological finding of this project, the monitoring closed-loop protocol (Manzini and Accorsi, 2013), illustrated in Fig. 18.2, consists on coupling the ex-post monitoring system to track the profile experienced by the products, with the reproduction in the laboratory of such a profile and the assessment of the resulting effects on the product quality and safety. The reproduction of the thermal and humidity profiles experienced by the products has carried out through climate-controlled chambers, illustrated in Fig. 18.3, properly designed and controlled by a Proportional-Integral (PI) control (Accorsi et al., 2014c). The involved companies thereby have the opportunity to collect accurate data on the conditions experienced by their products when leave the production facility and are shipped to the consumer site and also to assess the effect of the supply chain on their products in terms of quality, taste, flavor, appearance, nutritional value, and safety. Given the flexibility of the applied technologies, including the traceability data loggers and the climate controlled chambers, the close-loop protocol can be widely applied to different products and supply chains, for example, meat supply chains or dairy products.
A Case Study on Dairy Products
“continuouse improvements” -new packaging solutions -facility locations -transportation modes -demand allocation Etc.
Loop 2
Set-points (targets & expected KPI) ADD NEW FSC configuration
FSC configuration i-th
process monitoring - processing/manufacturing - storage - Shipment...
Physical & environmental stresses
“sensed value” system output
Lab simulation
Loop 1 what-if multiscenario analysis
+ -
Feedback OK
FSC best configuration (TO-BE)
reference
Feedback NO OK
AS-IS starting FSC configuration
Figure 18.2 Closed-loop monitoring system. From Manzini, R., Accorsi, R., 2013. The new conceptual framework for food supply chain assessment. Journal of Food Engineering 115 (2), 251–263.
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Figure 18.3 Climate-controlled chamber and related control panel.
This chapter presents the proposed closed-loop monitoring system applied to a case study of the dairy supply chain. The aim of this chapter is to assess the environmental and physical conditions (ie, temperature) experienced by dairy products during the transport and storage activities, identify criticalities, and measure the effectiveness of logistics and material handling operations in addressing the quality and the shelf life of the product. The ex-post monitoring system is applied and discussed, while the reproduction of the distribution profile in the laboratory is postponed to further developments of this research. The remainder of the chapter is arranged as follows. Section 2 presents the adopted closed-loop monitoring methodology applied to the supply chain of the observed dairy products. Section 3 illustrates the case study of the observed dairy supply chain and resulting distribution profiles. Finally, Section 4 presents the conclusions and some notes and suggestions for further research.
2. Closed-Loop Traceability System: Methodology While Fig. 18.2 illustrates the entire closed-loop monitoring system, the proposed chapter deals with the monitoring campaign of the environmental conditions affecting the distribution activities of the observed dairy products. The dairy sector accounts for 14% of the whole European food industry, with more than 750,000 dairy farms across Europe and about 12,000 production sites. Furthermore, it contributes significantly with more than 350 POD cheeses and dairy products to the European nutritional and cultural heritage (EDA, 2014; F&D Europe, 2014). The dairy production is extremely diffused in Italy, and particularly in the Emilia-Romagna region, with renowned POD specialties as Parmigiano-Reggiano cheese. While the production phase within the producer facility is strictly controlled to comply
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with safety standards, this chapter highlights what could happen during product distribution. Given the fact that the Italian dairy products represent a significant opportunity for exporting, the attention to the maintenance of the optimal conservation conditions during the transport phase is crucial. For the Parmigiano-Reggiano cheese the quality of the raw materials (ie, milk) is certified by the consortium which puts together the cheese factories and regulates the brand as well as the rules against counterfeiting.
2.1 The Characteristics of Cheese Supply Chains This chapter collects the monitoring campaign of the national distribution of dairy products obtained by Parmigiano-Reggiano cheese (ie, butter, snack, cuts) through one the most critical steps of the supply chains: the fractionated transport from the vendor facility to the consumer points. The horizon of time of such shipments is usually about three to five days. When imposed by the customer, the shipments are realized via reefer container maintaining the shipment temperature between 4–8°C. Because of the less constraining requirements in fresh product distribution than for frozen, the carriers behave arbitrarily given the contract and the client. When products are shipped to the large-scale retailers, the carriers belong to the retailer fleet, and temperature control is guaranteed until the retailer warehouse or the sales point is achieved. In the case that clients are grocery shops, restaurants, or cafes, the order is generally small in quantity and not insufficient to fill the truck. Therefore the carrier shares the cargo with other food products, not necessarily dairy. Such fractioned cargo carries out a number of stops, as a delivery tour, where the truck doors are opened and the internal storage condition altered. During transportation, temperature should vary within the range of 4–8°C (DM 01/04/88 n. 178, Section C Part II DPR 327/80). While temperature up to 14°C are allowed for short periods during the fractioned transport (DM 01/04/88 n. 178, Section C Part II DPR 327/80), the taste, the flavor and the nutritional properties of the cheese products can be significantly compromised when the conservation temperature passes the threshold of 8°C. In particular, the most sensitive cheese varieties to the temperature are the soft cheeses (which are not treated in this study), butter, and cut or grated cheese.
2.2 The Production Company The enterprise observed in this study is a leading brand in the national and international distribution of Parmigiano-Reggiano cheese and related by-products (eg, cheese cuts, grated cheese, snacks). Belonging to the Parmigiano-Reggiano consortium, it distributes the products with three renowned brands and is the 3P producers for a popular Italian large scale retailer cooperative. The company owns nine cheese factories and two bug processing and warehousing facilities in Modena and Reggio-Emilia.
2.3 The Tracking Methodology When the products are packed, the warehouse operators at the production facility put the temperature data logger, illustrated in Fig. 18.5, within the secondary packaging, fill the tracking label of Fig. 18.4 and load the pallet into the cargo. Once received at destination, the product is unloaded, the label is filled by the client and the data logger
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Figure 18.4 The ex-post tracking label.
Figure 18.5 The tracking temperature data logger and reader.
retrieved and shipped back for data analysis and manipulation. Fig. 18.6 illustrates the four steps of the handling protocol which consists on sealing the secondary packaging with the sensor embedded, loading the carton in the pallet, and collecting the temperature sensor at the destination point.
3. Data Collection and Analysis Resulting from the transportation monitoring campaign, a set of different temperature profiles is illustrated. Different key performance indices (KPI) are introduced and evaluated to identify the aforementioned issues in the fractioned national distribution
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Figure 18.6 Enclosing and retrieving the sensor to/from the product packaging. Table 18.1
Tracked Shipments Client
Shipment 1 Shipment 2 Shipment 3 Shipment 4
Consorzio Europrogea Komedis Autogrill mag. Anagni Comby service
Expected Departure
Expected Arrival
Destination
Carrier
18/5
22/5
Rutigliano (Ba)
Carrier 1
18/5 18/5
23/5 22/5
Catania Anagni (Fr)
Carrier 2 Carrier 3
22/5
24/5
Roseto d.A. (Te)
Carrier 4
of dairy products and to aid improvements of the supply chain operations and packaging. Table 18.1 and Table 18.2 report the characteristics of the four milk-run shipping tours tracked with the introduced ex-post monitoring system. For each shipment, the temperature logger has been collected and data manipulated and analyzed. The following graphs and related statistics summarize the temperature profiles experienced by the dairy products during the fractioned transport from the producer facility to the client site. The graphs from Figs. 18.7–18.10 respond to the following legend: the green belt represents the optimal temperature condition for dairy conservation (ie, between 4–8°C);
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Table 18.2
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Ordered Dairy Products
Clients/Order Products
Lot
Expiring Date
07L12225
04/08/2012
27L12231 11L12223
15/10/2012 04/08/2012
L141112 11L12235
14/11/2012 18/08/2012
25L12229 25L12229 24L12235
21/07/2012 21/07/2012 15/10/2012
L12241 24L12230 24L12231
25/08/2012 15/10/2012 15/10/2012
Client 1 Parmigiano-Reggiano grated Unigrana g.100
Client 2 Parmigiano-Reggiano stick 30 m 100 g × 12 Parmigiano-Reggiano Cuor di mix grated g.70 doy × 20 Parmigiano-Reggiano slices 150 g × 18 Parmigiano-Reggiano grated 60 g × 20
Client 3 Cheese Alpina Cuor di Fette g.140 Scamorza Affumicata Cuor di Fette g.140 Parmigiano-Reggiano snack 20 g × 5 × 10
Client 4 Parmareggio butter POL.200 × 16 Parmigiano-Reggiano snack 20 g × 5 × 10 Parmigiano-Reggiano snack 20 g × 5 × 10
Figure 18.7 Temperature profile of shipment 1.
the temperatures below this belt are colored in blue, while the temperatures above are colored in red. The blue line represents the profile experienced by the dairy product along the transport, while the orange line indicates the external environmental temperature measured outside the truck (Table 18.3).
A Case Study on Dairy Products
Figure 18.8 Temperature profile of shipment 2.
Figure 18.9 Temperature profile of shipment 3.
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Figure 18.10 Temperature profile of shipment 4.
Shipment 1 Summary
Shipment Summary 18/05/2012 18/05/2012 08:00 18/05/2012 17:25 22/05/2012 08:00 85 h 35 min 5 min Samples 1040 Max temperature (°C) 9 Min temperature (°C) –2 Average temperature (°C) 2.5975 Samples above 8°C 5 out of 1040
3000 2500 Time (min)
Expected shipping date Packaging enclosure Shipping date Arrival date Transport duration Sampling time
2000 1500 1000 500 0
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9
Table 18.3
Temperature (°C)
The profile of Fig. 18.7 reveals how the temperature experienced by the daily products is constantly maintained below the critical threshold of 8°C. The average temperature is indeed around 2.5°C, even below the recommended range. For a part of the first samples, measured when the sensor was not yet coupled with the product, only two samples overcome the threshold, in compliance with the recommended conservation standards. In the final part of distribution, when the products are unloaded and put away at the client dock, the temperature falls further, tracking values even below 0°C with a minimum of −2°C (Table 18.4). The profile of Fig. 18.8 highlights an increasing trend of the temperature that rises up to 17.5°C until the products are delivered to the client warehouse on May 22nd and are thereby conserved within the optimal thermal belt. A number of samples are measured above the external environmental temperature, which is unusual and is probably
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Table 18.4
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Shipment 2 Summary
Shipment Summary Expected shipping date Packaging enclosure
18/05/2012 18/05/2012 08:00
Shipping date
18/05/2012 17:51
Arrival date
23/05/2012 08:00 1187
Travel distance (km) Transport duration
170 h 40 min
Sampling time
5 min
Samples
1912
Max temperature (°C)
17.5 5.5
Min temperature (°C) Average temperature (°C) Samples above 8°C
Table 18.5
9.57 1145 out of 1912 (59.88%)
Shipment 3 Summary
Shipment Summary
Table 18.6
18/05/2012 18/05/2012 08:15 18/05/2012 19:08 22/05/2012 06:00 82 h 52 min 5 min 995 12
2.5 6.61 350 out of 995 (35%)
700 600 500 Time (min)
Expected shipping date Packaging enclosure Shipping date Arrival date Transport duration Sampling time Samples Max temperature (°C) Min temperature (°C) Average temperature (°C) Samples above 8°C
400 300 200 100 0
2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 Temperature (°C)
Shipment 4 Summary
Shipment Summary 22/05/2012 22/05/2012 10:00 22/05/2012 18:24 24/05/2012 09:45 39 h 21 min 5 min 474 14 7.5 11.45 446 out of 474 (94%)
600 500 Time (min)
Expected shipping date Packaging enclosure Shipping date Arrival date Transport duration Sampling time Samples Max temperature (°C) Min temperature (°C) Average temperature (°C) Samples above 8°C
400 300 200 100 0
7.5
8
8.5
9
9.5
10 10.5 11 11.5 12 12.5 13 13.5 14 Temperature (°C)
due the temporary storage of the products within an intermediate warehouse that is not climate-controlled. On average, the products are conserved at the temperature of 9.67°C, but suffer numerous critical thermal stresses with about 60% of tracked time above the optimal temperature belt. The graph of Fig. 18.9 shows that the temperature is maintained within the optimal belt for a large part of the transportation phase. Along the transport time the temperature rises above the optimal threshold for two batches, for a total time of about 30 h, representing approximately 35% of the total travel time (Tables 18.5 and 18.6).
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More than in the other shipments, the tracked profile illustrated in Fig. 18.10 shows that the dairy products can significantly suffer thermal stresses during the transport phase. Indeed, while the average temperature is about 11°C, the dairy products stay above their optimal conservation threshold for a period corresponding to 94% of the total travel time. Although the regulation that defines the standards for dairy conservation allows a maximum temperature of 14°C during the fractioned transport (DM 01/04/88 n. 178, Section C Part II DPR 327/80), it also indicates that such temperature should be maintained for a short time. In order to study the impact of the tracked shipments on the quality and safety of dairy products, the closed-loop monitoring system should be implemented and the tracked profiles should be reproduced in a laboratory to assess whether and how they affect the products, in terms of both chemical and organoleptic properties.
4. Conclusions and Further Research This manuscript gives an overview of the differences between a real-time and an ex-post monitoring system and applies an ex-post monitoring protocol to a case study from the dairy industry. While real-time monitoring infrastructures are more effective and make the supply chain more resilient and controlled (de las Morenas et al., 2014; Barge et al., 2014), they require large investments and can be difficult to implement on a large or global scale. Conversely, the ex-post monitoring system is less expensive and can be easily applied to monitor the effect of transport and logistics activities on the stresses experienced by the agro-food products. The ex-post and closed-loop monitoring system can be applied finally to determine and identify the most sensitive and critical phase of the supply chain supporting oriented investment for the development of a real-time monitoring architecture to control a critical phase of the product distribution process. The aim of further developments is to assess the effect of the environmental stresses due to the logistics and the transport phase on the quality and the safety of the dairy products. The tracked profiles will be reproduced in a laboratory within properly controlled climate chambers (Accorsi et al., 2014c) able to reproduce the storage and shipping profiles in terms of experienced temperature and humidity. The aim of these simulations will be to assess how the quality and safety of dairy products can be affected by transportation and delivery processes and identify operative solutions in terms of packaging (Manzini et al., 2013), containment, and delivery routing, ensuring compliance with quality and safety standards.
References Accorsi, R., Manzini, R., Ferrari, E., 2014a. A comparison of shipping containers from technical, economic and environmental perspectives. Transportation Research, Part D, Transport and Environment 26, 52–59. Accorsi, R., Cascini, A., Cholette, S., Manzini, R., Mora, C., 2014b. Economic and environmental assessment of reusable plastic containers: a food catering supply chain case study. International Journal of Production Economics 152, 88–101.
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Accorsi, R., Bortolini, M., Fabri, F., Gamberi, M., Manzini, R., Pareschi, A., 2014c. Closedloop strategies to locally simulate food shipment conditions. In: Proceedings of the Third International Workshop on Food Supply Chain (IWFSC 2014). San Francisco State University. Ahumada, O., Villalobos, R., 2009. Application of planning models in the agro-food supply chain: a review. European Journal of Operational Research 195, 1–20. Barge, P., Gay, P., Merlino, V., Tortia, C., 2014. Item-level radio-frequency identification for the traceability of food products: application on a dairy product. Journal of Food Engineering 125 (2014), 119–130. Bartholdi, J.J., Mac Cawley, A., 2011. Tracking the temperatures of international wine shipments: processes, information handling, and temperature simulation. In: Proceedings of the First International Workshop on Food Supply Chain (IWFSC 2011), 28–40. University of Bologna, Bologna. ISBN: 9788890650000. European Dairy Association, EDA, 2014. Annual Report 2014. Avenue d’Auderghem, 22–28, 1040 Brussels. Food and Drink Europe (F&D), 2014. Data & Trends of the European Food and Drink Industry 2013–2014. Avenue des Nerviens, 9–31, 1040, Brussels. Li, D., Wang, X., Chan, H.K., Manzini, R., 2014. Sustainable food supply chain management. International Journal of Production Economics 152, 1–8. Labuza, T.P., 1982. Shelf-Life Dating of Foods. Food & Nutrition Press, Westport, CT, USA. de las Morenas, J., García, A., Blanco, J., 2014. Prototype traceability system for the dairy industry. Computers and Electronics in Agriculture 101 (2014), 34–41. Man, C.M.D., Jones, A.A., 1994. Shelf Life Evaluation of Foods. Blackie Academic & Professionals, Glasgow, UK. Manzini, R., Accorsi, R., 2013. The new conceptual framework for food supply chain assessment. Journal of Food Engineering 115 (2), 251–263. Manzini, R., Accorsi, R., Ayyad, Z., Bendini, A., Bortolini, M., Gamberi, M., Valli, E., Gallina Toschi, T., 2014a. Sustainability and quality in the food supply chain. A case study of shipment of edible oils. British Food Journal 16 (12), 2069–2090. Manzini, R., Accorsi, R., Bortolini, M., Ferrari, E., Gamberi, M., Trombini, M., 2014b. Quality assessment of fine Italian chocolate subject to time-varying stress of temperature during the logistic distribution. In: Proceedings of the Third International Workshop on Food Supply Chain (IWFSC 2014). San Francisco State University. Manzini, R., Accorsi, R., Bortolini, M., Tampieri, F., Garbellini, F., Evangelisti, F., Gamberi, M., Soli, S., August, 2014c. The cool chain under x-ray. Fresh Point Magazine 3 (8). Manzini, R., Accorsi, R., Ferrari, E., Mora, C., Regattieri, A., Santarelli, G., Versari, L., 2013. Accelerated life test analysis for packaging solutions. A case study of edible oils distribution. In: Proceedings of the Second International Workshop on Food Supply Chain (IWFSC 2013). Pontificia Universidad Catolica de Cile. Piazzi, P., Adami, S., Bortolini, M., Gamberi, M., Accorsi, R., Manzini, R., 2011. Design, development and test of a vibration monitoring embedded system. In: Proceedings of the First International Workshop on Food Supply Chain (IWFSC 2011), 1–12. University of Bologna, Bologna. ISBN: 9788890650000. Regattieri, A., Gamberi, M., Manzini, R., 2007. Traceability of food products: general framework and experimental evidence. Journal of Food Engineering 81, 347–356. Valli, E., Manzini, R., Accorsi, R., Bortolini, M., Gamberi, M., Bendini, A., Lercker, G., Gallina Toschi, T., 2013. Quality at destination: simulating shipment of three bottled edible oils from Italy to Taiwan. La Rivista Italiana delle Sostanze Grasse 90 (3), 163–169. World Bank, 2014. World Bank Data..
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R. Accorsi, E. Ferrari, M. Gamberi, R. Manzini, A. Regattieri Alma Mater Studiorum - University of Bologna, Bologna, Italy
1. Introduction The food industry has experienced profound transformation. Companies are increasingly facing new challenges and issues: on one hand, the access to new and attractive markets, and on the other, the increasing number of products, the competition in a global market, and the consumers’ requirements in terms of quality and environmental sustainability of the products (Li et al., 2014). Furthermore, the recent scandals and frauds affecting food safety requirements elicit the attention of consumers and compel politicians to assess the responsiveness of food supply chains and their tracking and monitoring systems. Because of the leading role of the food industry in the European manufacturing sector, the food companies are more and more resilient and strive to adapt quickly and responsively to the incoming issues and challenges. The food and drink industry turnover is 1048 billion euro in 2014, with an increasing value of both agro-food and beverage export and import respectively of 86.2 billion euro and 63.2 billion euro (World Bank, 2014). The demand of food specialties by foreign markets grows with the importance of logistics processes and operations in food supply chains. To meet the growing trends of food demand (Ahumada and Villalobos, 2009) food products intensively travel across continents and oceans, thereby shifting the focus from the management of local agriculture models to the optimization of the global food supply chain. These trends are increasing the relevance of considering the related functions of the food supply chain: not only cultivation and food processing but also the packaging, storage, distribution, and control and monitoring of the product quality across its entire life cycle. These processes result in more complex supply networks, which emphasize the distance between the grower and the consumer and affect consumer awareness of the supply chain stages from farm-to-fork. Beyond the appealing shelves of the grocery store, food is grown, harvested, handled, stored, and shipped, and each of step may affect the product’s quality and safety. In order to assess how food supply chains affect the safety and the quality of food products, the prediction of food degradation (Labuza, 1982; Man and Jones, 1994), combined with the tracking and the environmental conditions experienced by products from farm-to-fork are fundamental (Manzini and Accorsi, 2013). Typically, next to Advances in Food Traceability Techniques and Technologies. http://dx.doi.org/10.1016/B978-0-08-100310-7.00018-1 Copyright © 2016 Elsevier Ltd. All rights reserved.
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biological variations, food quality is determined by time and environmental conditions, which may be influenced by the type of packaging, way of loading, and the availability of temperature-controlled packages, vehicles, and warehouses and generally by the complete factors configuring the supply chain. To properly control the safety and quality of food products across processing, packaging, storage and distribution, the development of accurate traceability and monitoring systems is recommended, particularly when the supply chain in out of the control of the producer. Regattieri et al. (2007) defined traceability as “the history of a product in terms of the direct properties of that product and/or properties that are associated with that product once these products have been subject to particular value-adding processes using associated production means and in associated environmental conditions.” An effective food traceability system is an important tool not only to manage food quality and safety risks, but also to promote the development of effective supply chains. Two main categories of traceability technologies and devices can be distinguished: identification tags (ie, barcode, label, RFID tag) which address a product or a general item with a specific code for identification purposes and data loggers (sometimes called “black boxes”), whose aim is to trace and record the environmental conditions and profiles experienced by a product (Piazzi et al., 2011). In the food industry, radio-frequency identification (RFID) systems can also embed sensors and thereby working as an identification black box for traceability and logistics, as well as for anticounterfeit purposes (Barge et al., 2014). The development of a traceability system based on an RFID black box for a food supply chain is an effective way to realize the so-called real-time control of the state of the food products (de las Morenas et al., 2014), which goes in agreement with safety and quality standards and regulations and customer satisfaction. When gathering the conservation conditions of a product in real time, a set of operative logistic decisions can be handled in run time to correct, improve, or modify the observed process. This approach would suggest to monitor those parameters (eg, time, temperature, humidity) having an impact on the quality of products. Unfortunately, the costs of tags and the investment required to build up real-time traceability infrastructures over the entire supply chain are not revenue-generating for companies, because of the low market price of food products, the extension of global supply chains, and the cost of primary packaging is often not reusable (Accorsi et al., 2014b). The so-called ex-post monitoring systems are less expensive and are built up through proper black box or data logger devices, tracking the environmental profiles of storage and distribution activities. The ex-post monitoring system protocol is based on inserting data loggers within the product primary packaging, gathering the data loggers at the end of distribution, and collecting the distribution profile data. Finally, an ex-post detailed analysis of the collected data identifies the most critical steps of food distribution and aids operative decisions (eg, packaging improvements) to improve the control of the food safety and quality. The main difference between a real-time and an ex-post monitoring systems, both illustrated in Fig. 18.1, is based on the rapid response of the former system enabling operative real-time decisions in response to events or stresses in the supply chain
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operations. This undoubtedly results in making the supply chain more resilient and responsive. Conversely, the ex-post monitoring system tracks the supply chain steps and gives feedback to the involved actors about what happened along the product storage, transport, and delivery after the product reached the customer. Therefore any possible corrective actions and improvement of the supply chain operations are handled ex-post driven by the findings from the tracking system. Highlighting what are indeed the issues affecting the products, assessing them in an objective and quantitative
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manner helps in redesigning the packaging, the truck loads, the storage conditions in a warehouse, the shipping container (Accorsi et al., 2014a), up to choosing more careful carriers and logistics providers and renegotiating with them the transport standards and rules. This process can be particularly onerous for a single company. The Food Supply Chain project aims to bring together knowledge, experience, and tools of investigation to improve the distribution logistics of food products, thereby protecting a very important sector of the regional and national economy. The ex-post monitoring system can be even more exhaustive whether combined with a closed-loop protocol, which includes the reconstruction and simulation in laboratory of the monitored environmental conditions (eg, temperature, humidity, vibration) experienced by the products, and the organoleptic and chemical analysis of the simulated products to assess the effects on product quality due to distribution. The closed-loop protocol was first introduced by Bartholdi and Mac Cawley (2011), as a main result of the Wine Supply Chain Council (http://wscc.scl.gatech.edu/), to analyze the international shipments of bottled wine and was extended by Manzini and Accorsi (2013) to other supply chains and products, as extra-virgin olive oil (EVOO) (Valli et al., 2013; Manzini et al., 2014a), fine chocolate (Manzini et al., 2014b), kiwi fruits (Manzini et al., 2014c), and others within the Food Supply Chain Project. This international project put together a number of international research institutions, such as Georgia Institute of Technology (USA), San Francisco State University (USA), the Commonwealth Scientific and Industrial Research Organization (CSIRO) (Australia), the Pontificia Universidad Catolica de Cile (Chile), the Mendoza Universidad (Argentina), the Council for Scientific and Industrial Research (CSIR) (South Africa), and the University of Bologna (Italy), whose aim is to analyze how the variation of certain factors (eg, temperature, humidity, vibrations) can affect the products during storage and distribution phases. Through the direct involvement of companies of the food industry, this project aims to highlight the critical issues affecting the current global food supply chain and to assess possible improvements to meet food safety and quality standards. As a methodological finding of this project, the monitoring closed-loop protocol (Manzini and Accorsi, 2013), illustrated in Fig. 18.2, consists on coupling the ex-post monitoring system to track the profile experienced by the products, with the reproduction in the laboratory of such a profile and the assessment of the resulting effects on the product quality and safety. The reproduction of the thermal and humidity profiles experienced by the products has carried out through climate-controlled chambers, illustrated in Fig. 18.3, properly designed and controlled by a Proportional-Integral (PI) control (Accorsi et al., 2014c). The involved companies thereby have the opportunity to collect accurate data on the conditions experienced by their products when leave the production facility and are shipped to the consumer site and also to assess the effect of the supply chain on their products in terms of quality, taste, flavor, appearance, nutritional value, and safety. Given the flexibility of the applied technologies, including the traceability data loggers and the climate controlled chambers, the close-loop protocol can be widely applied to different products and supply chains, for example, meat supply chains or dairy products.
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“continuouse improvements” -new packaging solutions -facility locations -transportation modes -demand allocation Etc.
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Set-points (targets & expected KPI) ADD NEW FSC configuration
FSC configuration i-th
process monitoring - processing/manufacturing - storage - Shipment...
Physical & environmental stresses
“sensed value” system output
Lab simulation
Loop 1 what-if multiscenario analysis
+ -
Feedback OK
FSC best configuration (TO-BE)
reference
Feedback NO OK
AS-IS starting FSC configuration
Figure 18.2 Closed-loop monitoring system. From Manzini, R., Accorsi, R., 2013. The new conceptual framework for food supply chain assessment. Journal of Food Engineering 115 (2), 251–263.
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Figure 18.3 Climate-controlled chamber and related control panel.
This chapter presents the proposed closed-loop monitoring system applied to a case study of the dairy supply chain. The aim of this chapter is to assess the environmental and physical conditions (ie, temperature) experienced by dairy products during the transport and storage activities, identify criticalities, and measure the effectiveness of logistics and material handling operations in addressing the quality and the shelf life of the product. The ex-post monitoring system is applied and discussed, while the reproduction of the distribution profile in the laboratory is postponed to further developments of this research. The remainder of the chapter is arranged as follows. Section 2 presents the adopted closed-loop monitoring methodology applied to the supply chain of the observed dairy products. Section 3 illustrates the case study of the observed dairy supply chain and resulting distribution profiles. Finally, Section 4 presents the conclusions and some notes and suggestions for further research.
2. Closed-Loop Traceability System: Methodology While Fig. 18.2 illustrates the entire closed-loop monitoring system, the proposed chapter deals with the monitoring campaign of the environmental conditions affecting the distribution activities of the observed dairy products. The dairy sector accounts for 14% of the whole European food industry, with more than 750,000 dairy farms across Europe and about 12,000 production sites. Furthermore, it contributes significantly with more than 350 POD cheeses and dairy products to the European nutritional and cultural heritage (EDA, 2014; F&D Europe, 2014). The dairy production is extremely diffused in Italy, and particularly in the Emilia-Romagna region, with renowned POD specialties as Parmigiano-Reggiano cheese. While the production phase within the producer facility is strictly controlled to comply
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with safety standards, this chapter highlights what could happen during product distribution. Given the fact that the Italian dairy products represent a significant opportunity for exporting, the attention to the maintenance of the optimal conservation conditions during the transport phase is crucial. For the Parmigiano-Reggiano cheese the quality of the raw materials (ie, milk) is certified by the consortium which puts together the cheese factories and regulates the brand as well as the rules against counterfeiting.
2.1 The Characteristics of Cheese Supply Chains This chapter collects the monitoring campaign of the national distribution of dairy products obtained by Parmigiano-Reggiano cheese (ie, butter, snack, cuts) through one the most critical steps of the supply chains: the fractionated transport from the vendor facility to the consumer points. The horizon of time of such shipments is usually about three to five days. When imposed by the customer, the shipments are realized via reefer container maintaining the shipment temperature between 4–8°C. Because of the less constraining requirements in fresh product distribution than for frozen, the carriers behave arbitrarily given the contract and the client. When products are shipped to the large-scale retailers, the carriers belong to the retailer fleet, and temperature control is guaranteed until the retailer warehouse or the sales point is achieved. In the case that clients are grocery shops, restaurants, or cafes, the order is generally small in quantity and not insufficient to fill the truck. Therefore the carrier shares the cargo with other food products, not necessarily dairy. Such fractioned cargo carries out a number of stops, as a delivery tour, where the truck doors are opened and the internal storage condition altered. During transportation, temperature should vary within the range of 4–8°C (DM 01/04/88 n. 178, Section C Part II DPR 327/80). While temperature up to 14°C are allowed for short periods during the fractioned transport (DM 01/04/88 n. 178, Section C Part II DPR 327/80), the taste, the flavor and the nutritional properties of the cheese products can be significantly compromised when the conservation temperature passes the threshold of 8°C. In particular, the most sensitive cheese varieties to the temperature are the soft cheeses (which are not treated in this study), butter, and cut or grated cheese.
2.2 The Production Company The enterprise observed in this study is a leading brand in the national and international distribution of Parmigiano-Reggiano cheese and related by-products (eg, cheese cuts, grated cheese, snacks). Belonging to the Parmigiano-Reggiano consortium, it distributes the products with three renowned brands and is the 3P producers for a popular Italian large scale retailer cooperative. The company owns nine cheese factories and two bug processing and warehousing facilities in Modena and Reggio-Emilia.
2.3 The Tracking Methodology When the products are packed, the warehouse operators at the production facility put the temperature data logger, illustrated in Fig. 18.5, within the secondary packaging, fill the tracking label of Fig. 18.4 and load the pallet into the cargo. Once received at destination, the product is unloaded, the label is filled by the client and the data logger
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Figure 18.4 The ex-post tracking label.
Figure 18.5 The tracking temperature data logger and reader.
retrieved and shipped back for data analysis and manipulation. Fig. 18.6 illustrates the four steps of the handling protocol which consists on sealing the secondary packaging with the sensor embedded, loading the carton in the pallet, and collecting the temperature sensor at the destination point.
3. Data Collection and Analysis Resulting from the transportation monitoring campaign, a set of different temperature profiles is illustrated. Different key performance indices (KPI) are introduced and evaluated to identify the aforementioned issues in the fractioned national distribution
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Figure 18.6 Enclosing and retrieving the sensor to/from the product packaging. Table 18.1
Tracked Shipments Client
Shipment 1 Shipment 2 Shipment 3 Shipment 4
Consorzio Europrogea Komedis Autogrill mag. Anagni Comby service
Expected Departure
Expected Arrival
Destination
Carrier
18/5
22/5
Rutigliano (Ba)
Carrier 1
18/5 18/5
23/5 22/5
Catania Anagni (Fr)
Carrier 2 Carrier 3
22/5
24/5
Roseto d.A. (Te)
Carrier 4
of dairy products and to aid improvements of the supply chain operations and packaging. Table 18.1 and Table 18.2 report the characteristics of the four milk-run shipping tours tracked with the introduced ex-post monitoring system. For each shipment, the temperature logger has been collected and data manipulated and analyzed. The following graphs and related statistics summarize the temperature profiles experienced by the dairy products during the fractioned transport from the producer facility to the client site. The graphs from Figs. 18.7–18.10 respond to the following legend: the green belt represents the optimal temperature condition for dairy conservation (ie, between 4–8°C);
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Table 18.2
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Ordered Dairy Products
Clients/Order Products
Lot
Expiring Date
07L12225
04/08/2012
27L12231 11L12223
15/10/2012 04/08/2012
L141112 11L12235
14/11/2012 18/08/2012
25L12229 25L12229 24L12235
21/07/2012 21/07/2012 15/10/2012
L12241 24L12230 24L12231
25/08/2012 15/10/2012 15/10/2012
Client 1 Parmigiano-Reggiano grated Unigrana g.100
Client 2 Parmigiano-Reggiano stick 30 m 100 g × 12 Parmigiano-Reggiano Cuor di mix grated g.70 doy × 20 Parmigiano-Reggiano slices 150 g × 18 Parmigiano-Reggiano grated 60 g × 20
Client 3 Cheese Alpina Cuor di Fette g.140 Scamorza Affumicata Cuor di Fette g.140 Parmigiano-Reggiano snack 20 g × 5 × 10
Client 4 Parmareggio butter POL.200 × 16 Parmigiano-Reggiano snack 20 g × 5 × 10 Parmigiano-Reggiano snack 20 g × 5 × 10
Figure 18.7 Temperature profile of shipment 1.
the temperatures below this belt are colored in blue, while the temperatures above are colored in red. The blue line represents the profile experienced by the dairy product along the transport, while the orange line indicates the external environmental temperature measured outside the truck (Table 18.3).
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Figure 18.8 Temperature profile of shipment 2.
Figure 18.9 Temperature profile of shipment 3.
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Figure 18.10 Temperature profile of shipment 4.
Shipment 1 Summary
Shipment Summary 18/05/2012 18/05/2012 08:00 18/05/2012 17:25 22/05/2012 08:00 85 h 35 min 5 min Samples 1040 Max temperature (°C) 9 Min temperature (°C) –2 Average temperature (°C) 2.5975 Samples above 8°C 5 out of 1040
3000 2500 Time (min)
Expected shipping date Packaging enclosure Shipping date Arrival date Transport duration Sampling time
2000 1500 1000 500 0
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9
Table 18.3
Temperature (°C)
The profile of Fig. 18.7 reveals how the temperature experienced by the daily products is constantly maintained below the critical threshold of 8°C. The average temperature is indeed around 2.5°C, even below the recommended range. For a part of the first samples, measured when the sensor was not yet coupled with the product, only two samples overcome the threshold, in compliance with the recommended conservation standards. In the final part of distribution, when the products are unloaded and put away at the client dock, the temperature falls further, tracking values even below 0°C with a minimum of −2°C (Table 18.4). The profile of Fig. 18.8 highlights an increasing trend of the temperature that rises up to 17.5°C until the products are delivered to the client warehouse on May 22nd and are thereby conserved within the optimal thermal belt. A number of samples are measured above the external environmental temperature, which is unusual and is probably
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Table 18.4
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Shipment 2 Summary
Shipment Summary Expected shipping date Packaging enclosure
18/05/2012 18/05/2012 08:00
Shipping date
18/05/2012 17:51
Arrival date
23/05/2012 08:00 1187
Travel distance (km) Transport duration
170 h 40 min
Sampling time
5 min
Samples
1912
Max temperature (°C)
17.5 5.5
Min temperature (°C) Average temperature (°C) Samples above 8°C
Table 18.5
9.57 1145 out of 1912 (59.88%)
Shipment 3 Summary
Shipment Summary
Table 18.6
18/05/2012 18/05/2012 08:15 18/05/2012 19:08 22/05/2012 06:00 82 h 52 min 5 min 995 12
2.5 6.61 350 out of 995 (35%)
700 600 500 Time (min)
Expected shipping date Packaging enclosure Shipping date Arrival date Transport duration Sampling time Samples Max temperature (°C) Min temperature (°C) Average temperature (°C) Samples above 8°C
400 300 200 100 0
2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 Temperature (°C)
Shipment 4 Summary
Shipment Summary 22/05/2012 22/05/2012 10:00 22/05/2012 18:24 24/05/2012 09:45 39 h 21 min 5 min 474 14 7.5 11.45 446 out of 474 (94%)
600 500 Time (min)
Expected shipping date Packaging enclosure Shipping date Arrival date Transport duration Sampling time Samples Max temperature (°C) Min temperature (°C) Average temperature (°C) Samples above 8°C
400 300 200 100 0
7.5
8
8.5
9
9.5
10 10.5 11 11.5 12 12.5 13 13.5 14 Temperature (°C)
due the temporary storage of the products within an intermediate warehouse that is not climate-controlled. On average, the products are conserved at the temperature of 9.67°C, but suffer numerous critical thermal stresses with about 60% of tracked time above the optimal temperature belt. The graph of Fig. 18.9 shows that the temperature is maintained within the optimal belt for a large part of the transportation phase. Along the transport time the temperature rises above the optimal threshold for two batches, for a total time of about 30 h, representing approximately 35% of the total travel time (Tables 18.5 and 18.6).
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More than in the other shipments, the tracked profile illustrated in Fig. 18.10 shows that the dairy products can significantly suffer thermal stresses during the transport phase. Indeed, while the average temperature is about 11°C, the dairy products stay above their optimal conservation threshold for a period corresponding to 94% of the total travel time. Although the regulation that defines the standards for dairy conservation allows a maximum temperature of 14°C during the fractioned transport (DM 01/04/88 n. 178, Section C Part II DPR 327/80), it also indicates that such temperature should be maintained for a short time. In order to study the impact of the tracked shipments on the quality and safety of dairy products, the closed-loop monitoring system should be implemented and the tracked profiles should be reproduced in a laboratory to assess whether and how they affect the products, in terms of both chemical and organoleptic properties.
4. Conclusions and Further Research This manuscript gives an overview of the differences between a real-time and an ex-post monitoring system and applies an ex-post monitoring protocol to a case study from the dairy industry. While real-time monitoring infrastructures are more effective and make the supply chain more resilient and controlled (de las Morenas et al., 2014; Barge et al., 2014), they require large investments and can be difficult to implement on a large or global scale. Conversely, the ex-post monitoring system is less expensive and can be easily applied to monitor the effect of transport and logistics activities on the stresses experienced by the agro-food products. The ex-post and closed-loop monitoring system can be applied finally to determine and identify the most sensitive and critical phase of the supply chain supporting oriented investment for the development of a real-time monitoring architecture to control a critical phase of the product distribution process. The aim of further developments is to assess the effect of the environmental stresses due to the logistics and the transport phase on the quality and the safety of the dairy products. The tracked profiles will be reproduced in a laboratory within properly controlled climate chambers (Accorsi et al., 2014c) able to reproduce the storage and shipping profiles in terms of experienced temperature and humidity. The aim of these simulations will be to assess how the quality and safety of dairy products can be affected by transportation and delivery processes and identify operative solutions in terms of packaging (Manzini et al., 2013), containment, and delivery routing, ensuring compliance with quality and safety standards.
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Accorsi, R., Bortolini, M., Fabri, F., Gamberi, M., Manzini, R., Pareschi, A., 2014c. Closedloop strategies to locally simulate food shipment conditions. In: Proceedings of the Third International Workshop on Food Supply Chain (IWFSC 2014). San Francisco State University. Ahumada, O., Villalobos, R., 2009. Application of planning models in the agro-food supply chain: a review. European Journal of Operational Research 195, 1–20. Barge, P., Gay, P., Merlino, V., Tortia, C., 2014. Item-level radio-frequency identification for the traceability of food products: application on a dairy product. Journal of Food Engineering 125 (2014), 119–130. Bartholdi, J.J., Mac Cawley, A., 2011. Tracking the temperatures of international wine shipments: processes, information handling, and temperature simulation. In: Proceedings of the First International Workshop on Food Supply Chain (IWFSC 2011), 28–40. University of Bologna, Bologna. ISBN: 9788890650000. European Dairy Association, EDA, 2014. Annual Report 2014. Avenue d’Auderghem, 22–28, 1040 Brussels. Food and Drink Europe (F&D), 2014. Data & Trends of the European Food and Drink Industry 2013–2014. Avenue des Nerviens, 9–31, 1040, Brussels. Li, D., Wang, X., Chan, H.K., Manzini, R., 2014. Sustainable food supply chain management. International Journal of Production Economics 152, 1–8. Labuza, T.P., 1982. Shelf-Life Dating of Foods. Food & Nutrition Press, Westport, CT, USA. de las Morenas, J., García, A., Blanco, J., 2014. Prototype traceability system for the dairy industry. Computers and Electronics in Agriculture 101 (2014), 34–41. Man, C.M.D., Jones, A.A., 1994. Shelf Life Evaluation of Foods. Blackie Academic & Professionals, Glasgow, UK. Manzini, R., Accorsi, R., 2013. The new conceptual framework for food supply chain assessment. Journal of Food Engineering 115 (2), 251–263. Manzini, R., Accorsi, R., Ayyad, Z., Bendini, A., Bortolini, M., Gamberi, M., Valli, E., Gallina Toschi, T., 2014a. Sustainability and quality in the food supply chain. A case study of shipment of edible oils. British Food Journal 16 (12), 2069–2090. Manzini, R., Accorsi, R., Bortolini, M., Ferrari, E., Gamberi, M., Trombini, M., 2014b. Quality assessment of fine Italian chocolate subject to time-varying stress of temperature during the logistic distribution. In: Proceedings of the Third International Workshop on Food Supply Chain (IWFSC 2014). San Francisco State University. Manzini, R., Accorsi, R., Bortolini, M., Tampieri, F., Garbellini, F., Evangelisti, F., Gamberi, M., Soli, S., August, 2014c. The cool chain under x-ray. Fresh Point Magazine 3 (8). Manzini, R., Accorsi, R., Ferrari, E., Mora, C., Regattieri, A., Santarelli, G., Versari, L., 2013. Accelerated life test analysis for packaging solutions. A case study of edible oils distribution. In: Proceedings of the Second International Workshop on Food Supply Chain (IWFSC 2013). Pontificia Universidad Catolica de Cile. Piazzi, P., Adami, S., Bortolini, M., Gamberi, M., Accorsi, R., Manzini, R., 2011. Design, development and test of a vibration monitoring embedded system. In: Proceedings of the First International Workshop on Food Supply Chain (IWFSC 2011), 1–12. University of Bologna, Bologna. ISBN: 9788890650000. Regattieri, A., Gamberi, M., Manzini, R., 2007. Traceability of food products: general framework and experimental evidence. Journal of Food Engineering 81, 347–356. Valli, E., Manzini, R., Accorsi, R., Bortolini, M., Gamberi, M., Bendini, A., Lercker, G., Gallina Toschi, T., 2013. Quality at destination: simulating shipment of three bottled edible oils from Italy to Taiwan. La Rivista Italiana delle Sostanze Grasse 90 (3), 163–169. World Bank, 2014. World Bank Data.