ai based forecasting perishable food
195998386
Data Mining of Perishable Food Safety Sampling based on Voting
Different models had been made, which by selecting the neural network algorithm, classification and regression tree algorithm and Bayesian network algorithm in the data mining software. Then the three concrete models were combined and the conditional statements were derived from the derived nodes. According to the principle that the minority is subordinate to the majority, an accurate and credible forecasting model had been built by the way of "vote for" The prediction model can predict the condition of perishable food, which innovatively guiding the safety inspection work; by choosing the safety rapid detection for typical effective sample of perishable food, effectively improve the efficiency and effectiveness of the inspection work. The prediction model avoids the deterioration of perishable food flowing into the market, and ensures the safe transportation of perishable food.
A Q Hu Tongjuan Liu
29095203
Cluster based hierarchical demand forecasting for perishable goods
Cluster analysis of articles based on intra day sales pattern.Exploiting of article clusters and organizational structure for demand forecasting.DSS for ordering fast moving substitutable perishable goods.Evaluation based on real data of an industrialized bakery. Demand forecasting is of particular importance for retailers in the context of supply chains of perishable goods and fresh food. Such goods are daily produced and delivered as they need to be provided as fresh as possible and quickly deteriorate. Demand underestimation and overestimation negatively affect the revenues of the retailer. Stock outs have an undesired impact on consumers while unsold items need to be discarded at the end of the day. We propose a DSS that supports day to operations by providing hierarchical forecasts at different organizational levels based on most recent point of sales data. It identifies article clusters that are used to extend the hierarchy based on intra day sales pattern. We apply multivariate ARIMA models to forecast the daily demand to support operational decisions. We evaluate the approach with point of sales data of an industrialized bakery chain and show that it is possible to increase the availability while limiting the loss at the same time. The cluster analysis reveals that substitutable items have similar intra day sales pattern which makes it reasonable to forecast the demand at an aggregated level. The accuracy of top down forecasts is comparable to direct forecasts which allows reducing the computational costs.
Expert Syst. Appl.
Jakob Huber Alexander Gossmann Heiner Stuckenschmidt
9609451
A Neural Network Based Forecasting Method for Ordering Perishable Food in Convenience Stores
In managing convenience stores, placing a balanced order is a critical daily job especially in perishable goods. Making the right decisions in ordering appropriate lot size can maintain customers' satisfaction; increase store profits reduce the scrap of the perishable food. Neural networks have been proved as an effective pattern recognition and forecasting time series events method. However, existing neural network models need improvements before they can be successfully applied to forecast cold perishable food demand in convenience stores. Sudden changes such as weather may affect the sales volume. This research proposes a neural network model that integrates a dynamic factor to forecast perishable product sales. The experimental results show that this approach is more accurate than conventional time series forecasting models such as the moving average model and autoregressive integrated moving average (ARIMA) model in forecasting perishable food.
2008 Fourth International Conference on Natural Computation
F L Chen T Y Ou
231645121
Traceability system using IoT and forecasting model for food supply chain
Nowadays, customer's health awareness is of extreme significance. Food can become contaminated at any point during production, preparation and distribution. Therefore, it is of key importance for the perishable food supply chain to monitor the food quality and safety. Traceability system offers complete food information and therefore, it guarantees food quality and safety. The current study proposes IoT based traceability system that utilized RFID and raspberry pi based sensors. The RFID reader is utilized to track and trace the product, while the raspberry pi is used to measure temperature and humidity during storage and transportation. In addition, the machine learning based forecasting model is utilized to predict future temperature, so that early warning can be presented by system if the predicted temperature exceeding the normal range. The results displayed that compared to the traditional methods, the proposed system is capable of tracking products as well as predicting sensor data accurately and effectively.
2020 International Conference on Decision Aid Sciences and Application (DASA)
Ganjar Alfian Muhammad Syafrudin Norma Latif Fitriyani Jongtae Rhee Muhammad Rifqi Ma'arif Imam Riadi
158311593
Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand
Abstract Brazilian agribusiness is responsible for almost 25% of the country gross domestic product, and companies from this economic sector may have strategies to control their actions in a competitive market. In this way, models to properly predict variations in the price of products and services could be one of the keys to the success in agribusiness. Consistent models are being adopted by companies as part of a decision making process when important choices are based on short or long term forecasting. This work aims to evaluate Wavelet Neural Networks (WNNs) performance combined with five optimization techniques in order to obtain the best time series forecasting by considering two case studies in the agribusiness sector. The first one adopts the soybean sack price and the second deals with the demand problem of a distinct groups of products from a food company, where nonlinear trends are the main characteristic on both time series. The optimization techniques adopted in this work are: Differential Evolution, Artificial Bee Colony, Glowworm Swarm Optimization, Gravitational Search Algorithm, and Imperialist Competitive Algorithm. Those were evaluated by considering short term and long term forecasting, and a prediction horizon of 30 days ahead was considered for the soybean sack price case, while 12 months ahead was selected for the products demand case. The performance of the optimization techniques in training the WNN were compared to the well established Backpropagation algorithm and Extreme Learning Machine (ELM) assuming accuracy measures. In long term forecasting, which is considered more difficult than the short term case due to the error accumulation, the best combinations in terms of precision was reached by distinct methods according to each case, showing the importance of testing different training strategies. This work also showed that the prediction horizon significantly affected the performance of each optimization method in different ways, and the potential of assuming optimization in WNN learning process.
Weslly Puchalsky Gabriel Trierweiler Ribeiro Claudimar Pereira da Veiga Roberto Zanetti Freire Leandro dos Santos Coelho
168273165
Demand Forecasting for Perishable Commodities: A Case Study of Inflight Food Demand for Low Cost Airline
The MIT Global Supply Chain and Logistics Excellence (SCALE) Network is an international alliance of leading edge research and education centers, dedicated to the development and dissemination of global innovation in supply chain and logistics. The Global SCALE Network allows faculty, researchers, students, and affiliated companies from all six centers around the world to pool their expertise and collaborate on projects that will create supply chain and logistics innovations with global applications. This reprint is intended to communicate research results of innovative supply chain research completed by faculty, researchers, and students of the Global SCALE Network, thereby contributing to the greater public knowledge about supply chains. Location: Building E40, Room 267 1 Amherst St. Summary: Inflight food sales have become an important revenue source for low cost airlines where demand forecasting is play a critical role in profitability. By creating a new and improved forecasting method, the research is aiming for reduced waste and lost sales, thus will increase the business profitability. Daniel holds a Bachelors in Ocean engineering from Institut Teknologi Bandung and MSCM from Malaysia Institute for Supply Chain Innovation, part of MIT global Scale Network. Prior to joining MISI, Daniel work as an field engineer for Hafar Daya Konstruksi, where he handle engineering issue for pipeline construction and rectification. KEY INSIGHTS 1. The two channels of food service, Pre Book Meal and Inflight sales, have different demand pool. Thus capturing demand from both channels will greatly increase the revenue. 2. The demand of inflight perishable goods is normally distributed and affected by seasonality. 3. News vendor model provide a more robust solution with significantly less waste. Introduction Background " Low cost Airlines " or " Budget Airlines " is a term used to address Airlines that generally have lower fares in exchange with less comfort services. This business model start at 1971 when Southwest airlines, an America based airlines, introduce a concept that eliminate some of the extra services in order to reduce cost and then pass the saving to the customers. A business model that targeting customers from middle and lower class, boosting their demand by reducing the overall price, where they only need to pay for what they need. This model is a perfect picture of " lean " that only accommodate the need of transportation and forsake the other additional services. A business model that has been proven throughout the globe, Started by Ryanair in
Daniel F Liman
70347295
Modelling Multi Regression with Particle Swarm Optimization Method to Food Production Forecasting
Tempe was one of the perishable foods with a durability of 2 to 3 days. Tempe home based industry must take into account its production in order to avoid losses. Suitable planning and forecasting can determine the ways for the production process is implemented. Previously, regression analysis was used as the method to improve the process. This research proposed the particle swarm optimisation (PSO) and multiple regression for production forecasting. PSO is used for optimisation value of regression variable of the tempe productions while multiple regression is used to determine the best coefficients for forecasting. The result of the research showed that the combination of multiple regression and particle swarm optimisation method performed quite well, indicated by the RMSE value of 2.081641.
Adyan Nur Alfiyatin Wayan Firdaus Mahmudy Yusuf Priyo Anggodo
73626566
Forecasting of Food Refrigerated Transport Power System Based on Type 2 Fuzzy Construction Phase Space
The most critical aspect of the cold chain is refrigerated transport, appropriate transport conditions can guarantee the quality of perishable foods. This study introduces the interval type 2 fuzzy logic method to reduce the prediction error, presents an interval type 2 fuzzy logic model for the time series of one hour of power load forecasting and adopted the first modeling process model structure and then use back propagation algorithm to adjust the model parameters are determined by simulation. In view of the two type of fuzzy measure the importance of system identification in the field of fuzzy in type 2, the study of type 2 fuzzy measure, proposed the axiomatic definition of the ordinary type 2 fuzzy similarity and fuzzy inclusion degree, which is intuitive understanding of people axioms.
Zhu Lin
216432941
Group pooling for deep tourism demand forecasting
Abstract Advances in tourism demand forecasting immensely benefit tourism and other sectors, such as economic and resource management studies. However, even for novel AI based methodologies, the challenge of limited available data causing model overfitting and high complexity in forecasting models remains a major problem. This study proposes a novel group pooling based deep learning model (GP DLM) to address these problems and improve model accuracy. Specifically, with our group pooling method, we advance the tourism forecasting literature with the following findings. First, GP DLM provides superior accuracy in comparison with benchmark models. Second, we define the novel dynamic time warping (DTW) clustering quantitative approach. Third, we reveal cross region factors that influence travel demands of the studied regions, including "travel blog," "best food," and "Air Asia."
Yishuo Zhang Gang Li Birgit Muskat Robert Law Yating Yang
169493489
Data driven retail food waste reduction a comparison of demand forecasting techniques and dynamic pricing strategies
Every year one third of all food that is produced is wasted and one of the UN sustainable development goals is to cut food waste in half by 2030. This thesis focuses on two data driven strategies to reduce perishable food waste at retailers: demand forecasting and dynamic pricing. Improved demand forecasting techniques can prevent excess inventory by better supporting replenishment decisions, whereas dynamic pricing can reduce excess inventory once it exists by stimulating customers to buy older products at a discount. The performance of both traditional and promising new demand forecasting techniques is compared and implementation guidelines are provided for retailers. In addition, simulations were conducted to investigate the performance of different (dynamic) pricing strategies in terms of revenue, waste and stock outs. For retailers, reducing perishable food waste results in financial and sustainability benefits. Demand Forecasting: Demand forecasting techniques that were included in the performance evaluation are: naive (where the forecast equals sales from last period) exponential smoothing (ES) moving average (MA) linear regression (LINREG) auto regressive integrated moving average (ARIMA) support vector regression (SVR) multi layer perceptron (MLP) long short term memory network (LSTM) and adaptive boost (ADA) These techniques were evaluated based on their performance in forecasting sales for 986 perishable food products from an Ecuadorian supermarket. Performance was measured using the relative root mean squared error measure (RelRMSE) which is a robust measure that indicates how well a certain technique performs relative to the naive forecast. Performance was compared across different forecasting scenarios, which differed in terms of their time detail level, location detail level and horizon. In addition, it was investigated what the influence is of using external factors such as the weather in addition to historical sales data to produce forecasts. Results show that there is no such thing as the ultimate demand forecasting: technique and that performance greatly varies across products and forecasting scenarios. By far the best demand forecasting performance overall can be obtained by automatically selecting the best forecasting technique for each individual product, resulting in (depending on the forecasting scenario) a 6% to 32% RelRMSE improvement compared to always using the naive forecast and a 2% to 10% improvement compared to always using the best individual demand forecasting technique. Adding external factors from the weather, promotion, economic and holiday categories has shown to be valuable for forecasting scenarios that have a daily time detail level, enabling an additional 3% to 9% RelRMSE improvement compared to using historical sales data only. For each forecasting scenario, detailed results and an overview of the top 3 best performing individual techniques are provided in this thesis to help retailers select the appropriate demand forecasting technique(s) in their situation. In addition, a process is provided with step by step guidelines for retailers on how to improve the wider demand forecasting process. It not only considers how to select the right demand forecasting technique(s) which form the quantitative core of the demand forecasting process, but also discusses how to assess forecasting capabilities and which qualitative factors should be taken into account, such as implementation in decision support systems, adoption factors and organizational factors. Dynamic Pricing: A simulation study was conducted to determine which (dynamic) pricing strategy for perishable products performs best in terms of total revenue, waste and stock outs. The simulation considers a monopolist grocer selling a single perishable product with a fixed shelf life. The product can be replenished each day based on the demand forecast and a safety factor. Customers have different characteristics: some customers are regular customers that only pay attention to price, whereas others are date checking customers that also pay attention to remaining shelf life and aim to choose a product that maximizes their value for money. Four main pricing strategies for marking down perishable products that approach their expiry date were compared. Strategy 1 applies no price changes at all and serves as a baseline. Strategy 2 applies a fixed discount D at the last day before expiry, while strategy 3 spreads that discount over the last S days before expiry. Strategy 4 dynamically determines discount percentages based on the demand forecast and the remaining inventory. Almost all strategies that applied a discount resulted in a waste reduction, but they regularly resulted in significantly lower revenues. Multiple experiments were conducted to investigate the effects of varying assumptions for simulation settings. Discounting was most beneficial when product demand was more elastic and when more customers checked expiry dates, because that resulted in higher waste reductions and less negative (or even positive) changes to revenue at the same time. The fixed pricing strategy that most frequently performed best was strategy 2 with a fixed discount of 20% on the last day before the expiration date. Surprisingly, the dynamic pricing strategies did not always perform better than a fixed price strategy, which could be due to the fact that these strategies relied upon imperfect demand forecasts and hence their estimated optimal discount percentage might have been off base. A dynamic pricing strategy already outperformed the best fixed strategy when initial waste levels for a product were high or when a large percentage of customers were regular customers. Conclusion and Discussion: Both demand forecasting process improvement and dynamic pricing strategies for marking down products that near their expiry dates have a positive impact on waste reduction. Grocers are advised to initially focus on improving the demand forecasting process, since that reduces waste, but does not have the negative impact on revenue that discounting strategies frequently have. Preventing inventory excesses from occurring through improved demand forecasting is better than trying to resolve such excesses by discounting products that approach their expiration dates. Grocers are advised to follow the demand forecasting improvement process, to use this study as a benchmark for forecasting technique selection, to implement multiple forecasting techniques and to automatically select the best forecasting technique for each product. To resolve any excesses that do occur in stores, grocers are advised to use the pricing strategy that showed best performance in similar situations in the simulations. The best fixed strategy in general is applying a 20% fixed discount on the last day before expiry. A dynamic pricing strategy only outperformed a fixed strategy in a few situations. This study makes several contributions to both retail practice and the scientific fields of demand forecasting and dynamic pricing. First of all, this study provides a robust and objective comparison of forecasting techniques in different scenarios within a food retail context. To the best of our knowledge, it is the first study that conducts such a comparison. Results show the impact of automatically selecting the best forecasting techniques for each individual product and for including external factors and can be used by retailers as a benchmark for forecasting technique selection. In addition, this study proposes a process for demand forecasting improvement and forecasting technique selection, which can guide retailers in their demand forecasting improvement efforts. This study also provides a robust performance comparison of different pricing strategies that mark down perishable products, giving retailers insight into the impact on revenue, waste and stock outs. In addition, tools were developed in the form of an algorithm for automatic best DF technique selection (and configuration) and a pricing simulation that can be reused in future work.
P Felix