Reducing the impact of demand fluctuations through supply chain collaboration in the Finnish retail grocery sector

Paper delivered at the 21st Logistics Research Network annual conference 2016, 7th-9th September 2016, Hull. Abstract Purpose: The purpose of the paper is to explore how a collaborative approach to supply chain management can be used to enhance supply chain performance when demand is fluctuating and uncertain. Enablers and barriers of collaboration will be assessed to provide insights into optimal methods of collaborating between supply chain partners. Research Approach: The study is a qualitative two-echelon case study of a grocery retail supply chain, focussing on a retail grocery wholesaler in Finland and its tier 1 small retail customers. An a priori conceptual framework for collaboration implementation that also details its impact on supply chain performance during periods of fluctuating and uncertain demand is developed through insights from the literature. The validity of the framework is explored through interviews conducted with key respondents at both echelon levels, which were analysed to evaluate and refine this framework. Findings and Originality: The paper demonstrates that collaboration can be a useful and successful technique to reduce costs and improve performance across the supply chain, particularly when demand is volatile and uncertain. This paper also provides insight into one alternative for implementing supply chain integration across several echelons and improving performance in the whole supply chain as a result. Research Impact: The paper provides a list of enablers and barriers for supply chain collaboration, discusses the importance of several key factors, and offers suggestions and guidelines for further research to generalise the findings. Practical Impact: The paper provides insight into the challenges and benefits of increased collaboration for grocery retail supply chain actors. It will be especially useful for those firms in the retail sector and other industries where demand is characterized by demand uncertainty and volatility

Forecasting Hands and Objects in Future Frames

This paper presents an approach to forecast future presence and location of human hands and objects. Given an image frame, the goal is to predict what objects will appear in the future frame (e.g., 5 seconds later) and where they will be located at, even when they are not visible in the current frame. The key idea is that (1) an intermediate representation of a convolutional object recognition model abstracts scene information in its frame and that (2) we can predict (i.e., regress) such representations corresponding to the future frames based on that of the current frame. We design a new two-stream convolutional neural network (CNN) architecture for videos by extending the state-of-the-art convolutional object detection network, and present a new fully convolutional regression network for predicting future scene representations. Our experiments confirm that combining the regressed future representation with our detection network allows reliable estimation of future hands and objects in videos. We obtain much higher accuracy compared to the state-of-the-art future object presence forecast method on a public dataset

Demand uncertainty and lot sizing in manufacturing systems: the effects of forecasting errors and mis-specification

This paper proposes a methodology for examining the effect of demand uncertainty and forecast error on lot sizing methods, unit costs and customer service levels in MRP type manufacturing systems. A number of cost structures were considered which depend on the expected time between orders. A simple two-level MRP system where the product is manufactured for stock was then simulated. Stochastic demand for the final product was generated by two commonly occurring processes and with different variances. Various lot sizing rules were then used to determine the amount of product made and the amount of materials bought in. The results confirm earlier research that the behaviour of lot sizing rules is quite different when there is uncertainty in demand compared to the situation of perfect foresight of demand. The best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. In addition the choice of lot sizing rule between ‘good’ rules such as the EOQ turns out to be relatively less important in reducing unit cost compared to improving forecasting accuracy whatever the cost structure. The effect of demand uncertainty on unit cost for a given service level increases exponentially as the uncertainty in the demand data increases. The paper also shows how the value of improved forecasting can be analysed by examining the effects of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high forecast error variance, improved forecast accuracy should lead to substantial percentage improvements in unit costs

Development and Validation of a Rule-based Time Series Complexity Scoring Technique to Support Design of Adaptive Forecasting DSS

Evidence from forecasting research gives reason to believe that understanding time series complexity can enable design of adaptive forecasting decision support systems (FDSSs) to positively support forecasting behaviors and accuracy of outcomes. Yet, such FDSS design capabilities have not been formally explored because there exists no systematic approach to identifying series complexity. This study describes the development and validation of a rule-based complexity scoring technique (CST) that generates a complexity score for time series using 12 rules that rely on 14 features of series. The rule-based schema was developed on 74 series and validated on 52 holdback series using well-accepted forecasting methods as benchmarks. A supporting experimental validation was conducted with 14 participants who generated 336 structured judgmental forecasts for sets of series classified as simple or complex by the CST. Benchmark comparisons validated the CST by confirming, as hypothesized, that forecasting accuracy was lower for series scored by the technique as complex when compared to the accuracy of those scored as simple. The study concludes with a comprehensive framework for design of FDSS that can integrate the CST to adaptively support forecasters under varied conditions of series complexity. The framework is founded on the concepts of restrictiveness and guidance and offers specific recommendations on how these elements can be built in FDSS to support complexity

Performance of supply chain collaboration – A simulation study

In the past few decades several supply chain management initiatives such as Vendor Managed Inventory, Continuous Replenishment and Collaborative Planning Forecasting and Replenishment (CPFR) have been proposed in literature to improve the performance of supply chains. But, identifying the benefits of collaboration is still a big challenge for many supply chains. Confusion around the optimum number of partners, investment in collaboration and duration of partnership are some of the barriers of healthy collaborative arrangements. To evolve competitive supply chain collaboration (SCC), all SC processes need to be assessed from time to time for evaluating the performance. In a growing field, performance measurement is highly indispensable in order to make continuous improvement; in a new field, it is equally important to check the performance to test conduciveness of SCC. In this research, collaborative performance measurement will act as a testing tool to identify conducive environment to collaborate, by the way of pinpointing areas requiring improvements before initializing collaboration. We use actual industrial data and simulation to help managerial decision-making on the number of collaborating partners, the level of investments and the involvement in supply chain processes. This approach will help the supply chains to obtain maximum benefit of collaborative relationships. The use of simulation for understanding the performance of SCC is relatively a new approach and this can be used by companies that are interested in collaboration without having to invest a huge sum of money in establishing the actual collaboration

Coordination, Cooperation, and Collaboration: Defining the C3 Framework

The term C3 refers to the framework of coordinative, cooperative and collaborative relationships within the realm of external supply chain partnerships. Each unique partnership offers both benefits and challenges within a supply chain and must be aligned with company and supply chain strategy in order to achieve maximum effectiveness. This paper aims to fill the current void in supply chain literature concerning C3 by defining each term based upon current supply chain research as well as give the most prevalent characteristics and differences between each “C” in this phase model. This research is then compared to the industry through a case study of a major international retailer. Finally, we propose a set of propositions that organizations can use to assess at what level their external relationships reside within the phase model as well as how companies move and evolve their relationships between the levels and what the trigger mechanisms are in this evolution

How Smart Operations Help Better Planning and Replenishment?

This chapter discusses various roles of smart information in Supply Chains (SC) of digital age and tries to answer an important question - What types of collaborative arrangements facilitate smart operations to improve planning, production and timely replenishment? We have conducted longitudinal case studies with firms practicing SC collaborations and also using smart information for operations. Based on the case analysis, the companies are further classified as 'smart planning' and 'traditional planning'. Research findings show the importance of aligning SC partnerships based on smart information requirements. These findings are based on case studies of Indian firms with global SC collaboration. We also discuss the role of Big Data for the companies using smart planning