Maintaining and controlling quality across any industry has become a compulsory norm and because the customer has become more aware and attentive, the industries are spending a lot of time, money, and effort to always maintain quality and thereby maintain their customer base. The food supply chain is one such area where the implementation of quality is must and sure and in case of any slight discrepancy, there will be a lot of product damage and revenue losses to be incurred. There are no issues with the methods to ensure product quality. However, the approach to do that has to be changed in the current age of fast evolution. One of the current major advancements that are spreading across the world is the Internet of Things (IoT) which consists of software and various tech devices such as sensors, computing devices, etc. Using IoT, the efficiency to control and maintain the quality will be improved. Data mining and data analysis will also be used to handle the large data collected by IoT. The data will be always monitored to check for the quality issues in real-time and respond to any failures in a very short span of time. Further, the collected data can be used to model and predict any future failures that can be caused. Quality Controlled Logistics (QCL), which has proven to be efficient to control quality from the point of origin of supply chain and till the end of it, is also in use to control the quality. The thesis report talks about Integrating IoT with QCL which will help to better the process of inspecting every part of the distribution networks and thus meeting customer deadlines and requirements. There are a few challenges associated with the integration and implementation, however, overcoming them will be very useful and will have long-term benefits.
Key Words: Internet of Things (IoT), Quality Controlled Logistics (QCL), Supply Chain Management, Quality Control, logistics, sensors.
Currently, there are numerous methods to implement quality measures in logistics and supply chain. All of these methods use traditional approaches, which are cumbersome, time-consuming, and require a lot of worker power. Also, from the research that was conducted, it is seen that there is only a little attention that was provided to quality issues in supply chain. My idea is to use the current advancements in technology and blend those with quality measures and create an advanced approach of quality assurance in the area of supply chain. The quality control and assurance in the concept of the supply chain are mainly dealt with Quality Controlled Logistics (QCL). In this paper, we will talk about how the quality control measures have changed over time and how the current advancements such as the internet of things, data mining, and data analysis, which are a part of Internet 4.0 will be useful to implement in Quality Controlled Logistics (QCL). Further, we will discuss how to implement these advancements in QCL on Food Supply Chain.
Quality control, a subset of quality assurance, often focuses on identifying the defective products from the actual products produced. Food supply chain works in a domino-like motion where food moves systematically from producers to consumers. The processes include production, processing, distribution, consumption, and disposal ways. It is considered as a domino-like process because if one of the subparts is affected, all the next parts are spoiled. Food being very delicate compared to other products in terms of the expiry and quality aspects, the distribution of the food materials is always a tough job which requires proper planning and exact scheduled distributions to maintain the food quality. Sometimes, a variety of foods such as refrigerated foods, allergic foods, etc. have to be distributed in a controlled environment. All of these factors contribute to the difficulties associated with the transportation and distribution of food products. Hence, controlling the quality in the food supply chain is one of the very prominent steps to have a smooth run of the food industry.
Some of the old methods are traditional Quality Management System (QMS) which uses other ideas like Total Quality Management (TQM), Six Sigma, etc. The paper  talks about the importance of the use of traditional quality management approaches. However, to implement this approaches, there is a lot of information and data to be collected which can be used by various tools like PDCA cycle, can be used in various quality methods such as DMAIC and can be used to prepare various charts and other graphs such as control charts, Ishikawa’s diagram, etc. This data collection is a tiresome job and hence, some processes can be identified and be replaced with modern technology which we will talk further.
Six Sigma is used to reduce the defect rate in a manufacturing process and other operations to a value of 3.4 defects per million parts produced. This low value is achieved by improving the engineering practices in product production. The concepts can also be used in many other operations such as supply chain and other areas. Six Sigma uses the standard approach of the DMAIC cycle (Define, Measure, Analyze, Improve, and Control) to have improved outcomes such as an increase in profits, an increase in efficiency, worker involvement, and reduced costs .
In the world of increased use of technology, every business aspect has to be integrated with the use of technology to cope up with the competitors. One such application of the technology is the Internet of Things (IoT) and it is a network of interrelated things such as computing devices, machines, sensors, electronic appliances, software, etc. which enables them to send and receive data. This data is very useful and can be used in various applications and other improvement objectives. A typical IoT network includes a sensing layer, a networking layer, a service layer, and an interface layer. The paper  mentions how IoT can be integrated with Supply Chain Management (SCM) which aids in cost-savings, product tracking, and inventory accuracy. The paper also tries to identify how important IoT can be to SCM. According to , IoT in SCM terms is defined as a network of physical objects that are digitally connected to sense, monitor and interact within a company and between the company and its supply chain enabling agility, visibility, tracking and information sharing to facilitate timely planning, control and coordination of the supply chain processes. The IoT, Industrial Internet of Things (IIoT), cloud computing, and various such things come under the latest industry development, called Industry 4.0, which is a trend towards automation and data exchange.
Quality Controlled Logistics:
Quality Controlled Logistics (QCL) makes use of variation in product quality, developments in technology, heterogeneous needs of customers, and the possibilities to manage product quality development in the distribution chain. Using the definition of logistics management of the Council of Supply Chain Management Professionals (CSCMP), we define QCL as follows: Quality Controlled Logistics is that part of supply chain management that plans, implements, and controls the efficient, effective flow and storage of food products, services and related information between the point of origin and the point of consumption in order to meet customers' requirements with respect to the availability of specific product qualities in time by using time-dependent product quality information in the logistics decision process .
There are various challenges associated in implementing IoT and other new technologies in the existing QMS and QCL. Some of them are
- The integration of the tech items with the logistics and the management systems and people to get habituated to this new working system
- Handling of the huge amount of data and constant maintaining of this data
- While the organization collects a large and sensitive data to make the supply chain efficient and transparent, protecting that huge data is very challenging and an important issue to be taken care of, and in the case of failing to accomplish that, it could affect the whole organization.
- An initial investment of time and money to widespread the tech across the whole supply chain and logistics.
REVIEW OF IDENTIFIED MATERIALS:
From the review conducted, there are some offerings/insights that identify the use of IoT in the network of supply chains.
- The paper  talks about the use of IoT in agricultural food supply chain and the ways to track and maintain the quality of food. There is a mention that the integration of IoT with the supply chain network was helpful and it became easy to track and trace the supply and distribution. It also helped in providing an intuitive view for customers buying these agricultural food/meats where the customers can inspect the quality of the food. One of the prominent concepts from this paper is that they have used RFID to tag the cattle/poultry for food safety reasons.
-  identified ways to control temperatures and thereby quality in cold chain management by using wireless sensors that collect temperature data and implements a control point criterion throughout the distribution process. Initially, sensors collect the data, and using the collected data, the organization will create some X-R control charts to identify the variance in the temperature data. This will help in controlling the quality by setting the temperatures according to the food that is to be distributed. This will reduce the costs incurred for the distribution and thereby increase the distribution channels which will show a positive influence on the revenues generated. Further, this approach can be used in other business models and distributions too.
- Using IoT such as sensors, we can create a system to immediately detect any issues in the distribution and logistics operations.  proposed a pre-warning system that uses data mining and IoT to timely monitor the detected the supply and logistics data and pre-warn the management or the concerned people. This will help to maintain the quality and safety of the food all through the supply chain network. This data will also help to identify the previous major failures which were occurred and help the organization to implement some preventive measures to prevent those failures to occur again. Apriori algorithm was used to mine the data and to speed up the process of it. The system segregated the whole logistics into 4 parts, sourcing, production, quality control and delivery operations to get fast information.
- Nowadays customers have also become very specific in terms of quality and because of this, companies are compulsorily considering implementing quality norms throughout their organization.  also focuses on developing time-temperature indicators to individually monitor temperature conditions for the food supply chain. It talks about how the food supply chain network works and how it is organized. Further, it focuses on temperature-controlled food supply chain networks and managing food quality by controlling the temperature in the supply network. This collected temperature data is then used to predict the product quality throughout the chain.
- Further, the advancement of new technology and its usage is not only limited to the production and distribution, but it can also be used in the packaging of the food products produced. In the paper , it is described how intelligent packing can be used in supply chain distribution based on QCL to reduce food wastage. The food quality is monitored by intelligent packing sensors where the information of the variation in the food quality is collected and used to print Dynamic Expiration Date (IP-DED) on a food package. By using a Fixed Expiry Date (FED), there is a tendency where the older products will not be purchased if any new batch arrives. To avoid this, IP-DED is used. IP-DED works in such a way that the food quality is continuously monitored and according to the remaining shelf life, the price of the food product changes. Initially, simulations were performed, the information was collected to test the data sets and the simulated results were implemented to print IP-DED. Models for the variation of food quality over time were created and were integrated with mixed-integer Linear Programming  to plan the distribution and simulate the food wastage in the food supply chain.
Some of the underexplored areas which are relevant to the integration of IoT and QCL are
- The sophistication of the current quality standards and practices to gain a potential market advantage over the competitors
- Employee peer involvement within the organization using business models
- Contemporary lean techniques and continuous improvement strategies in manufacturing and services
- Business tactics and Philosophies that extend beyond the concepts of Total Quality Management and Six Sigma
- The role of product design, product innovations, and Research and Development in quality management
- Worker involvement and leadership emphasis
The use of IoT will surely reduce a lot of manual and monotonous work. However, it will create a vast number of intellectual employment opportunities in the area of modeling, IoT designs, IoT production, data analysis, data mining, etc. Hence, the integration of IoT with QCL will be helpful to the organization in terms of a better quality of the food materials which produces better revenue. It is helpful for the customers because they can find, track, and have quality products. It is also useful for society as it is creating many technical and intellectual jobs.
It can be concluded that to have better quality control and assurance, we need to improve the quality management methods by changing the approaches. Using IoT will enhance the quality management aspects and tracking the food products at every point of the product distribution will help to act immediately in case of a major failure. Further, it is to be taken care that the errors are minimized completely by collecting the data and using the same data to simulate models, which will then be used to predict the future trend and failure modes in the supply chain network.
 dos Reis, J. G. M., Machado, S. T., Neto, P. L. D. O. C., Monteiro, R., Sacomano, J. B. (2014, September). Supply chain quality management in agribusiness: an approach of quality management systems in food supply chains. In IFIP International Conference on Advances in Production Management Systems (pp. 497-504). Springer, Berlin, Heidelberg.
 Jayaram, A. (2016, December). Lean six sigma approach for global supply chain management using industry 4.0 and IIoT. In 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I) (pp. 89-94). IEEE.
 Ben-Daya, M., Hassini, E., Bahroun, Z. (2019). Internet of things and supply chain management: a literature review. International Journal of Production Research, 57(15-16), 4719-4742.
 van der Vorst, J. G., van Kooten, O., Marcelis, W. J., Luning, P. A., Beulens, A. J. (2007). Quality controlled logistics in food supply chain networks: integrated decision-making on quality and logistics to meet advanced customer demands.
 Liu, Y., Han, W., Zhang, Y., Li, L., Wang, J., Zheng, L. (2016). An Internet-of-Things solution for food safety and quality control: A pilot project in China. Journal of Industrial Information Integration, 3, 1-7.
 Shih, C. W., Wang, C. H. (2016). Integrating wireless sensor networks with statistical quality control to develop a cold chain system in food industries. Computer Standards Interfaces, 45, 62-78.
 Wang, J., Yue, H. (2017). Food safety pre-warning system based on data mining for a sustainable food supply chain. Food Control, 73, 223-229.
 van der Vorst, J. G., van Kooten, O., Luning, P. A. (2011). Towards a diagnostic instrument to identify improvement opportunities for quality-controlled logistics in agrifood supply chain networks. International journal on food system dynamics, 2(1), 94-105.
 Heising, J. K., Claassen, G. D. H., Dekker, M. (2017). Options for reducing food waste by quality-controlled logistics using intelligent packaging along the supply chain. Food Additives Contaminants: Part A, 34(10), 1672-1680.
 Rong, A., Akkerman, R., Grunow, M. (2011). An optimization approach for managing fresh food quality throughout the supply chain. International Journal of Production Economics, 131(1), 421-429.