Measuring the variability in supply chains with the peakedness

This paper introduces a novel way to measure the variability of order flows in supply chains, the peakedness. The peakedness can be used to measure the variability assuming the order flow is a general point pro- cess. We show basic properties of the peakedness, and demonstrate its computation from real-time continuous demand processes, and cumulative demand collected at fixed time intervals as well. We also show that the peakedness can be used to characterize demand, forecast, and inventory variables, to effectively manage the variability. Our results hold for both single stage and multistage inventory systems, and can further be extended to a tree-structured supply chain with a single supplier and multiple retailers. Furthermore, the peakedness can be applied to study traditional inventory problems such as quantifying bullwhip effects and determining safety stock levels. Finally, a numerical study based on real life Belgian supermarket data verifies the effectiveness of the peakedness for measuring the order flow variability, as well as estimating the bullwhip effects.variability, peakedness, supply chain

Forecasting Modelling For Oil Country Tubular Goods (OCTG)

Dalam teori, parameter dan distribusi permintaan diketahui, tetapi dalam praktiknya, berbagai ketidakpastian membuatnya sulit untuk menentukan faktor-faktor ini, terutama karena permintaan yang sporadis dan tidak terduga. Permintaan untuk produk oil country tubular goods (OCTG) bersifat fluktuatif dan tidak teratur, dan persediaan keselamatan adalah strategi umum untuk mengelola ketidakpastian pasokan dan permintaan. Ada beberapa metode yang tersedia untuk meramalkan permintaan yang tidak teratur, seperti model statistik, deret waktu, Croston, dan metode deep learning. Penelitian ini menggunakan metode long short-term memory (LSTM) untuk meramalkan permintaan OCTG dan membandingkannya dengan metode autoregressive integrated moving average (ARIMA), dengan menggunakan data penjualan dari periode tertentu. Untuk menilai akurasi ramalan, tingkat kesalahan dihitung, termasuk mean square error (MSE), root mean square error (RMSE), dan mean absolute error (MAE). Meskipun baik metode LSTM maupun ARIMA tidak memberikan hasil yang memuaskan dengan menggunakan data penjualan harian, data penjualan bulanan menghasilkan hasil yang lebih baik. Dengan menggunakan data 6 bulan, baik LSTM maupun ARIMA menghasilkan hasil yang relatif baik, dengan LSTM menunjukkan kesalahan yang lebih kecil daripada ARIMA. Mengingat waktu pembelian pipa hijau dari pemasok sekitar 5 bulan, data kumulatif terbatas pada 6 bulan. Berdasarkan hasil penelitian, metode LSTM dapat digunakan untuk meramalkan permintaan produk OCTG dan menentukan tingkat persediaan keselamatan yang diperlukan

Demand forecasting by temporal aggregation:Using optimal or multiple aggregation levels?

Recent advances have demonstrated the benefits of temporal aggregation for demand forecasting, including increased accuracy, improved stock control and reduced modelling uncertainty. With temporal aggregation a series is transformed, strengthening or attenuating different elements and thereby enabling better identification of the time series structure. Two different schools of thought have emerged. The first focuses on identifying a single optimal temporal aggregation level at which a forecasting model maximises its accuracy. In contrast, the second approach fits multiple models at multiple levels, each capable of capturing different features of the data. Both approaches have their merits, but so far they have been investigated in isolation. We compare and contrast them from a theoretical and an empirical perspective, discussing the merits of each, comparing the realised accuracy gains under different experimental setups, as well as the implications for business practice. We provide suggestions when to use each for maximising demand forecasting gains

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

Comparative analysis of short-term demand predicting models using ARIMA and deep learning

The forecasting consists of taking historical data as inputs then using them to predict future observations, thus determining future trends. Demand prediction is a crucial component in the supply chain’s process that allows each member to enhance its performance and its profit. Nevertheless, because of demand uncertainty supply chains usually suffer from many problems such as the bullwhip effect. As a solution to those logistics issues, this paper presents a comparative analysis of four time series demand forecasting models; namely, the autoregressive integrated moving Average (ARIMA) a statistical model, the multi-layer perceptron (MLP) a feedforward neural network, the long short-term memory model (LSTM) a recurrent neural network and the convolutional neural network (CNN or ConvNet) a deep learning model. The experimentations are carried out using a real-life dataset provided by a supermarket in Morocco. The results clearly show that the convolutional neural network gives slightly better forecasting results than the Long short-term memory network

Measuring Asymmetric Price and Volatility Spillover in the South African Broiler Market

This study investigated asymmetric price and volatility spillover in the broiler value chain. The data used for the study includes farm and retail broiler monthly prices dated from January 2000 to August 2008. The threshold autoregressive (TAR) and momentum threshold autoregressive (M-TAR) models were used to investigate asymmetry in farm-retail market prices, whereas the exponential generalised autoregressive conditional heteroskedasticity (EGARCH) model was used to measure price volatility and the volatility spillover effect between retail and farm prices. Price asymmetry was found between farm and retail prices with retail prices responding more rapidly (with a lag) to negative than positive changes in farm price. The results indicate that within one month, the retail prices adjust so as to eliminate approximate 2.8 % of a unit-negative change in the deviation from the equilibrium relationship caused by changes in producer prices. This implies that the retailers must increase their marketing margin by 2.8% in order to response completely to a unit-negative change in farm prices. The results from the volatility model show that the magnitude of volatility in the retail and farm prices for the periods 2000M1 to 2008M8 is 1.8% and 2.8%, respectively, with significant asymmetric volatility spillover from the farm to retail level of the value chain. This implies that the response to positive shock at any production and marketing stage differs from the response to a negative shock.Livestock Production/Industries,