Artificial intelligence-based hybrid forecasting models for manufacturing systems

In many enterprises the issue of making accurate business decisions often depends on the quality of demand forecasts for manufactured products. “Demand forecasting is crucial for decision making and operations in organisations” [38]. In the era of globalization, market uncertainty, and growing supply chain complexity the need for integrated and efficient planning increases [55]. Predicting future demand values provides the basis not only for proper production planning, but also for preparing precise material, financial and employee demand schedules. The proper resources management is a challenging task in every manufacturing company [25]. The accurate demand forecasting for the manufactured products allows reducing inventory and improving order indicators, whereas “inaccurate forecasts can be costly for company operations, in terms of stock-outs and lost sales, or over-stocking, while not meeting service level targets” [39]. Forecasting is widely used not only in production planning, but also in maintenance – it enables the companies to predict failures or demand for spare parts (examples of forecasting applications in maintenance-related problems include for instance time-based machine failure prediction in multi-machine manufacturing systems [75] or lifetime prediction of bearings or bearing-based systems [4]). In manufacturing processes the quality of the end product is in general defined by multiple critical outputs or responses and therefore, the efficient forecasting of quality is both critical and challenging for practitioners [72]. In fact in every single area of activity of a manufacturing company, for which it is possible to collect the appropriate dataset and it is necessary to make effective decisions regarding future operations, accurate prediction techniques should be implemented. In [29] Hall discusses a number of cases presenting how improvement of forecasting influences profitability of companies – for example Hyundai Motors has reduced delivery time by 20% and increased inventory turns from 3 to 3.4, whereas Reynolds Aluminum has reduced forecasting errors by 2%, which in turn caused a reduction of 1 million pounds in inventory. Moreover, Unilever has reduced forecasting errors from 40% to 25%, which has brought multi-million dollar savings. SCI Systems on the other hand has reduced on-hand inventory by 15%, which resulted in annual savings of 180 million dollars. It is also worth mentioning that Virgin Atlantic Cargo – being one of the largest air freight operators in the world – has identified forecasting accuracy as of strategic importance to its operational efficiency, due to the reason that efficient predictions ensure to have the right resources available at the right place and time [37]. Another important area of forecasting implementation in manufacturing companies is spare parts management. According to Suomala et al. the impact of the spare parts business is significant in terms of The paper addresses the problem of forecasting in manufacturing systems. The main aim of the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model and Hybrid extreme learning machine model. The study focuses on solving the problem of limited access to independent variables. Empirical verification of the proposed models is built upon real data from the three manufacturing system areas – production planning, maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting accuracy assessment and optimal method selection for industrial companies is introduced. It can serve not only as an efficient and costless tool for advanced manufacturing companies willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0 implementation. Highlights Abstract