Forecasting and inventory management optimization in Stokke AS

Confidential until 1 June 201

Commentary on the Makridakis Time Series Competition (M- Competition)

In 1982, the Journal of Forecasting published the results of a forecasting competition organized by Spyros Makridakis (Makridakis et al., 1982). In this, the ex ante forecast errors of 21 methods were compared for forecasts of a variety of economic time series, generally using 1001 time series. Only extrapolative methods were used, as no data were available on causal variables. The accuracies of methods were compared using a variety of accuracy measures for different types of data and for varying forecast horizons. The original paper did not contain much interpretation or discussion. Partly this was by design, to be unbiased in the presentation. A more important factor, however, was the difficulty in gaining consensus on interpretation and presentation among the diverse group of authors, many of whom have a vested interest in certain methods. In the belief that this study was of major importance, we decided to obtain a more complete discussion of the results. We do not believe that the data speak for themselves.Makridakis, commentary, time series competition, m competition

FORECASTING CRITICAL AIRCRAFT LAUNCH AND RECOVERY EQUIPMENT (ALRE) COMPONENTS' DEMAND

Demand signals across the Navy’s NIMITZ Class Carrier (CVN) Aircraft Launch and Recovery Equipment (ALRE) market-basket are highly erratic and do not fit neatly into the traditional demand-based sparing construct. This causes the Naval Supply Systems Command Weapons Systems Support (NAVSUP WSS) planning efforts to continually lag behind requirements, with material often arriving late-to-need. This project attempts to develop a comprehensive and more reliable ALRE material requirement forecast model. To accomplish this effectively, a comprehensive list of historical CVN ALRE demand data were analyzed in order to identify any correlation between ALRE demand and a ship’s operating phase status, and to identify whether that correlation directly drives ALRE demand. The analysis begins by collecting historical CVN ALRE demand data and identifying the improvements for the current forecasting model. After a complete analysis of the current forecasting model, we utilized multiple linear regression and evaluated various forecasting methods as the best available methods for developing/discovering an optimized and robust forecasting method. In conclusion, the extremely low demand quantities of critical ALRE components continue to make forecasting extremely unreliable, but we believe NAVSUP can improve the accuracy of ALRE demand forecast by adapting a flexible forecasting system.NAVSUPhttp://archive.org/details/forecastingcriti1094561212Lieutenant Commander, United States NavyLieutenant, United States NavyLieutenant Commander, United States NavyApproved for public release; distribution is unlimited

Commentary on the Makridakis Time Series Competition (M-Competition)

In 1982, the Journal of Forecasting published the results of a forecasting competition organized by Spyros Makridakis (Makridakis et al., 1982). In this, the ex ante forecast errors of 21 methods were compared for forecasts of a variety of economic time series, generally using 1001 time series. Only extrapolative methods were used, as no data were available on causal variables. The accuracies of methods were compared using a variety of accuracy measures for different types of data and for varying forecast horizons. The original paper did not contain much interpretation or discussion. Partly this was by design, to be unbiased in the presentation. A more important factor, however, was the difficulty in gaining consensus on interpretation and presentation among the diverse group of authors, many of whom have a vested interest in certain methods. In the belief that this study was of major importance, we decided to obtain a more complete discussion of the results. We do not believe that “the data speak for themselves.

The Accuracy of Alternative Extrapolation Models: Analysis of a Forecasting Competition Through Open Peer Review

In 1982, the Journal of Forecasting published the results of a forecasting competition organized by Spyros Makridakis (Makridakis et al., 1982). In this, the ex ante forecast errors of 21 methods were compared for forecasts of a variety of economic time series, generally using 1001 time series. Only extrapolative methods were used, as no data were available on causal variables. The accuracies of methods were compared using a variety of accuracy measures for different types of data and for varying forecast horizons. The original paper did not contain much interpretation or discussion. Partly this was by design, to be unbiased in the presentation. A more important factor, however, was the difficulty in gaining consensus on interpretation and presentation among the diverse group of authors, many of whom have a vested interest in certain methods. In the belief that this study was of major importance, we decided to obtain a more complete discussion of the results. We do not believe that the data speak for themselves

Advanced Methods of Power Load Forecasting

This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load

Artificial Intelligence Methods in Spare Parts Demand Forecasting

The paper discusses the problem of forecasting lumpy demand which is typical for spare parts. Several prediction methods are presented in the article – traditional techniques based on time series and advanced methods that use Artificial Intelligence tools. The research conducted in the paper focuses on comparison of eight forecasting methods, including classical, hybrid and based on artificial neural networks. The aim of the paper is to assess the efficiency of lumpy demand forecasting methods that apply AI tools. The assessment is conducted by a comparison with traditional methods and it is based on Root Mean Square Errors (RMSE) and relative forecast errors (ex post) values. The article presents also a new approach to the lumpy demand forecasting issue – a method which combines regression modelling, information criteria and artificial neural networks

Complementing South African inflation surveys: A suitable forecasting tool

Central banks currently perform inflation expectation surveys in order to better align their inflation expectations with that of the general public. However, surveys are time-consuming, complicated, expensive and not always accurate, thus compromising the credibility of these expectations. The complexity of inflation targeting and the difficulty of forecasting in real time can also cause policymakers to consider more basic models, which can lead to inexact forecasts. This article employs less complicated models, such as the seasonally adjusted autoregressive integrated moving average and Holt-Winters exponential smoothing models, to provide equally reliable forecasts. A more complex approach in the form of a non-linear autoregressive neural network process was also employed to model the strategic and rational manner in which the general public formulates their expectations. Overall, the forecast estimates provided by these models were superior when compared with the inflation expectations provided by the International Monetary Fund, South African Reserve Bank and Bureau for Economic Research

Advanced predictive-analysis-based decision support for collaborative logistics networks

Purpose – The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution. Design/methodology/approach – The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments. Findings – Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes. Practical implications – The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept. Originality/value – The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application