Supply chain forecasting when information is not shared

The operations management literature is abundant in discussions on the benefits of information sharing in supply chains. However, there are many supply chains where information may not be shared due to constraints such as compatibility of information systems, information quality, trust and confidentiality. Furthermore, a steady stream of papers has explored a phenomenon known as Downstream Demand Inference (DDI) where the upstream member in a supply chain can infer the downstream demand without the need for a formal information sharing mechanism. Recent research has shown that, under more realistic circumstances, DDI is not possible with optimal forecasting methods or Single Exponential Smoothing but is possible when supply chains use a Simple Moving Average (SMA) method. In this paper, we evaluate a simple DDI strategy based on SMA for supply chains where information cannot be shared. This strategy allows the upstream member in the supply chain to infer the consumer demand mathematically rather than it being shared. We compare the DDI strategy with the No Information Sharing (NIS) strategy and an optimal Forecast Information Sharing (FIS) strategy in the supply chain. The comparison is made analytically and by experimentation on real sales data from a major European supermarket located in Germany. We show that using the DDI strategy improves on NIS by reducing the Mean Square Error (MSE) of the forecasts, and cutting inventory costs in the supply chain

Reduction of the value of information sharing as demand becomes strongly auto-correlated

Information sharing has been identified, in the academic literature, as one of the most important levers to mitigate the bullwhip effect in supply chains. A highly-cited article on the bullwhip effect has claimed that the percentage inventory reduction resulting from information sharing in a two level supply chain, when the downstream demand is autoregressive of order one, is an increasing function of the autoregressive parameter of the demand. In this paper we show that this is true only for a certain range of the autoregressive parameter and there is a maximum value beyond which the bullwhip ratio at the upstream stage is reduced and the percentage inventory reduction resulting from information sharing decreases towards zero. We also show that this maximum value of the autoregressive parameter can be as high as 0.7 which represents a common value that may be encountered in many practical contexts. This means that large benefits of information sharing cannot be assumed for those Stock Keeping Units (SKUs) with highly positively auto-correlated demand. Instead, equally careful analysis is needed for these items as for those SKUs with less strongly auto-correlated demand

Pass-through of unfair trading practices in EU food supply chains

This report presents the results of the research project “Pass-Through of Unfair Trading Practices in EU Food Supply Chains: Methodology and Empirical Application”. The purpose of the project is to design and test a monitoring system of unfair trading practices (UTP) along the agri-food supply chain. The investigation has special focus on assessment of the “pass-through effect”, defined as the consequences for the entire supply chain of UTPs adopted in a specific transaction. The report includes: (i) a review of the economic literature for a better understanding of the economic principles of UTPs; (ii) a review of available data sources and past experiences in UTP monitoring; (iii) the illustration of two alternative approaches for UTP monitoring: B-SEA (broad-scope empirical analysis) and IDEA (in-depth analysis); (iv) a test application of the two approaches to the EU fresh fruit sector; (v) a comparative analysis of the IDEA and B-SEA results and (vi) a discussion of the implications of our research.JRC.D.4-Economics of Agricultur

Revisiting the value of information sharing in two-stage supply chains

There is a substantive amount of literature showing that demand information sharing can lead to considerable reduction of the bullwhip effect/inventory costs. The core argument/analysis underlying these results is that the downstream supply-chain member (the retailer) quickly adapts its inventory position to an updated end-customer demand forecast. However, in many real-life situations, retailers adapt slowly rather than quickly to changes in customer demand as they cannot be sure that any change is structural. In this paper, we show that the adaption speed and underlying (unknown) demand process crucially effect the value of information sharing. For the situation with a single upstream supply-chain member (manufacturer) and a single retailer, we consider two demand processes: stationary or random walk. These represent two extremes where a change in customer demand is never or always structural, respectively. The retailer and manufacturer both forecast demand using a moving average, where the manufacturer bases its forecast on retailer demand without information sharing, but on end-customer demand with information sharing. In line with existing results, the value of information turns out to be positive under stationary demand. One contribution, though, is showing that some of the existing papers have overestimated this value by making an unfair comparison. Our most striking and insightful finding is that the value of information is negative when demand follows a random walk and the retailer is slow to react. Slow adaptation is the norm in real-life situations and deserves more attention in future research - exploring when information sharing indeed pays off

A knowledge chain framework for construction supply chains

Construction is a project-based industry and construction supply chains generally work with a unique product in every project. Commonly, project organizations are reconfigured for each project. This means that construction supply chains are characterised by various practices and disjointed relationships, with the result that construction supply chain actors generally have transient relationships rather than long term risk sharing partnerships. A consequence of this is the lack of trust between construction clients, designers, main contractors and suppliers. Because the construction supply chain works as a disparate collection of separate organisations rather than as a unified team, the supply chain suffers from lack of integration. Knowledge flow in construction supply chains are hindered due to the reasons such as inadequate adaptation to collaborative procurement type projects, inadequate collaboration between the downstream and upstream supply chain, lack of interoperability of the design tools, lack of well structured SCM process and lack of well developed knowledge management applications. These characteristics of the construction supply chains are the main reasons for its low efficiency and productivity in project delivery. There is a need for the development of appropriate systems to ensure the effective diffusion of knowledge such that each actor of the supply chain adds value to the project delivery process. This is expected to result in the creation of knowledge chains in construction. It is believed that construction supply chain management (SCM), when integrated with knowledge management (KM), can successfully address the major problems of the industry The main aim of this research was to develop a framework to transform construction supply chains into knowledge chains . To reach this aim, the research first provided an overview of practices and issues in SCM across a range of industry sectors including construction, aerospace, and automotive industries. It discusses research and developments in the field of SCM and KM in construction industry, the key SCM issues with a knowledge flow focus, and the best practices from other industries to improve the construction supply chains. Furthermore, the results of the company specific and project specific case studies conducted in aerospace and construction industry supply chains are presented. These results include the key SC problems, key issues related to knowledge flow and the presentation of knowledge requirements of each supply chain actor. Following the data analysis process, a framework to transform the construction supply chain into a knowledge chain taking full cognisance of both the technical and social aspects of KM was presented. The main purpose of the knowledge chain framework was to enable construction bid managers/project managers to plan and manage the project knowledge flow in the supply chain and organise activities, meetings and tasks to improve SCM and KM throughout the supply chain in an integrated procurement type (PFI) project life cycle. The knowledge chain framework was intended to depict the knowledge flow in the construction supply chain specifically, and to offer guidance for specific business processes to transform the supply chains into knowledge chains. Finally, this research focused on the evaluation of the framework through industry practitioners and researchers. An evaluation of the Framework was conducted via workshop followed by a questionnaire comprising industry experts. The findings indicated that adoption of the Framework in construction project lifecycle could contribute towards more efficient and effective management of knowledge flow, standardisation and integration of SCM and KM processes, better coordination and integration of the SC, improved consistency and visibility of the processes, and successful delivery of strategic projects. The overall research process contributed the construction research in many perspectives such as introduction of knowledge chain concept for construction supply chains; comparative analysis of the SCM practices in different industry sectors, identification of best practices for construction supply chains, better demonstration of the maturity level and critical factors of the SCM within the construction industry, demonstration of the KC framework which integrates the supply chain process and knowledge sharing within a single framework which covers all the recent trends in the construction industry like collaborative procurement route projects, creation of better integrated SCs, applications like off site construction and BIM where all supply chain management and knowledge management should take place

A sustainable supply chain study of the Indian bioenergy sector

In India, more than one third of the population do not currently have access to modern energy services. Biomass to energy, known as bioenergy, has immense potential for addressing India’s energy poverty. Small scale decentralised bioenergy systems require low investment compared to other renewable technologies and have environmental and social benefits over fossil fuels. Though they have historically been promoted in India through favourable policies, many studies argue that the sector’s potential is underutilised due to sustainable supply chain barriers. Moreover, a significant research gap exists. This research addresses the gap by analysing the potential sustainable supply chain risks of decentralised small scale bioenergy projects. This was achieved through four research objectives, using various research methods along with multiple data collection techniques. Firstly, a conceptual framework was developed to identify and analyse these risks. The framework is founded on existing literature and gathered inputs from practitioners and experts. Following this, sustainability and supply chain issues within the sector were explored. Sustainability issues were collated into 27 objectives, and supply chain issues were categorised according to related processes. Finally, the framework was validated against an actual bioenergy development in Jodhpur, India. Applying the framework to the action research project had some significant impacts upon the project’s design. These include the development of water conservation arrangements, the insertion of auxiliary arrangements, measures to increase upstream supply chain resilience, and the development of a first aid action plan. More widely, the developed framework and identified issues will help practitioners to take necessary precautionary measures and address them quickly and cost effectively. The framework contributes to the bioenergy decision support system literature and the sustainable supply chain management field by incorporating risk analysis and introducing the concept of global and organisational sustainability in supply chains. The sustainability issues identified contribute to existing knowledge through the exploration of a small scale and developing country context. The analysis gives new insights into potential risks affecting the whole bioenergy supply chain

Spatial-Intelligent Decision Support System for Sustainable Downstream Palm Oil Based Agroindustry within the Supply Chain Network: A Systematic Literature Review and Future Research

Oil palm plantations as one of the sexiest commodities; produce a high yield of oil and fat that can be used in various sectors. The prospect of oil palm and its derivative products is good, but there are obstacles and problems faced that are mainly related to sustainability issues in oil palm plantations and its downstream process. Therefore, it is important to study the decision-making process that are needed to develop sustainable palm oil agroindustry. This paper aims at providing a comprehensive literature review for decision support system for sustainable agroindustry. Totally, 186 scientific publication articles from 2005 to 2019 were reviewed and synthesized. The reviewed articles were categorize based on the keywords of palm oil sustainability, geographic information system (GIS), and decision support system (DSS). The research gap and pointers for future research that are identified is the lack of sustainability aspect inclusion on decision-making process. We also identified the lack discussion of integrated spatial and intelligent tools through DSS for better, faster, and smarter decision-making process. In the end part of the paper, a pointer for possible future research was develop in terms of combination through spatial-intelligent system applying business analytics for sustainable agroindustry

Improving Outcomes for Shell and Shucking By-Products in Australian Abalone Fisheries – A Supply Chain Perspective

This research responds to global targets to halve food waste by 2030 and the Australian abalone industry’s need to maximise returns on catch by utilising shucking by-products. Both these exigencies are addressed by quantifying food waste and understanding its drivers. Analysis revealed that Australia’s wild-harvest abalone industry faces several barriers to recovering and valorising commercially-viable volumes of waste arising from heavily-regulated supply and vast geographical distances. Supply chain collaboration is necessary to overcome these challenges