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The purpose of the paper is to identify the multiple types of data that can be collected and analyzed by practitioners across the cold chain, the ICT infrastructure required to enable data capture and how to utilize the data for decision making in cold chain logistics.

Content analysis based literature review of 38 selected research articles, published between 2000 and 2016, was used to create an overview of data capture, technologies used for collection and sharing of data, and decision making that can be supported by the data, across the cold chain and for different types of perishable food products.

There is a need to understand how continuous monitoring of conditions such as temperature, humidity, and vibration can be translated to support real-time assessment of quality, determination of actual remaining shelf life of products and use of those for decision making in cold chains. Firms across the cold chain need to adopt appropriate technologies suited to the specific contexts to capture data across the cold chain. Analysis of such data over longer periods can also unearth patterns of product deterioration under different transportation conditions, which can lead to redesigning the transportation network to minimize quality loss or to take precautions to avoid the adverse transportation conditions.

The findings need to be validated through further empirical research and modeling. There are opportunities to identify all relevant parameters to capture product condition as well as transaction data across the cold chain processes for fish, meat and dairy products. Such data can then be used for supply chain (SC) planning and pricing products in the retail stores based on product conditions and traceability information. Addressing some of the above research gaps will call for multi-disciplinary research involving food science and engineering, information technologies, computer science and logistics and SC management scholars.

The findings of this research can be beneficial for multiple players involved in the cold chain like food processing companies, logistics service providers, ports and wholesalers and retailers to understand how data can be effectively used for better decision making in cold chain and to invest in the specific technologies, which will suit the purpose. To ensure adoption of data analytics across the cold chain, it is also important to identify the player in the cold chain, which will drive and coordinate the effort.

This paper is one of the earliest to recognize the need for a comprehensive assessment for adoption and application of data analytics in cold chain management and provides directions for future research.

The purpose of this paper is to explore tactical planning in grocery retailing and propose how process and integration mechanisms from sales and operations planning (S&OP) can enhance retail tactical planning.

This work follows an explorative design with case studies from the grocery retailing industry in Finland, Norway, and the UK.

The tactical planning process focuses on demand management and securing product availability from suppliers in order to reach sales targets. Less attention is directed toward balancing supply and demand or toward providing a single plan to guide company operations. Planning appeared to be functionally oriented with limited coordination between functional plans, but it did include external integration that improved forecast accuracy.

The study involves grocery retailer cases with variable levels of S&OP maturity. The propositions need to be investigated further through action research or additional case studies to confirm their validity.

The study proposes a design of an S&OP process in retailing and propositions for improving tactical planning integration.

The study complements research on retail tactical planning by taking planning process and integration viewpoints. The research suggests that retailers would benefit from a formal and company-wide S&OP process to unify different market-oriented plans to a single set of numbers, thus better balancing supply and demand without sacrificing the emphasis on demand planning.

The aim of this empirical paper is to study information sharing in fresh food supply chains, with a specific goal of reducing waste and facilitating sustainable performance. The study focuses on material and information flow issues, specifically on sharing demand and shelf‐life data.

This work has been designed as an exploratory case study in three fresh food supply chains, milk, fresh fish, and fresh poultry, in the Nordic countries. The cases are based on interviews and data from the databases of the companies involved. Each case focuses on analyzing information flow, particularly the current order patterns and forecasting and planning process, and material flow, focusing on the supply chain structure. In two cases significant changes have been made to forecasting processes and material flow, while the third case intends to identify the most beneficial uses of shared information to create a sustainable fresh food supply chain.

The performance of the perishable food chain can be improved by more efficient information sharing. The key to improved operations is how and for which purposes the shared data should be used. In addition, changes in the supply chain structure were needed to speed up the deliveries and ensure shelf availability. The cross‐case analysis revealed that improved performance was obtained with parallel changes in information sharing and usage and in material flow.

Few studies approach the problem of waste and sustainability from an integrated supply chain perspective. This paper links data sharing with the sustainability performance of the supply chain as a whole.

The purpose of this paper is to understand how companies design sales and operations planning (S & OP) contingent on the planning environment (PE).

On the basis of the literature, the paper creates an analytical framework identifying the main constructs related to the PE and S & OP design, which is the basis for gathering and analysing qualitative data from eight cases in the food industry. The findings highlight the relations between S & OP and the PE, and are used for generating three propositions.

Responding to the complex and uncertain PE, the companies set up S & OP on a stock-keeping unit (SKU) level, with the possibility of re-planning and a flexible planning horizon, thus differing from what has generally been suggested in the literature. In addition, the companies are aligning the inputs, activities, and outcomes of the S & OP process to the PE. Particularly important environmental contingencies are uncertainty connected to demand and supply, frequent product launches, and production network complexity. Product-related variables have a lower impact on the S & OP design.

The present study is limited to one industry only and a comparison between industries with larger data sets would be valuable in future studies. The study selected cases based on their S & OP maturity; further studies need to explore the effect of the alignment of S & OP and the PE on the planning performance.

In the literature, S & OP is presented as a generic process with a strict formal design that is equal for all companies. The study provides insights into how companies adjust S & OP according to the PE.