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Markets continue to change, and the speed of such change is becoming faster than ever. In order to adapt to the changing markets, corporations have placed a greater emphasis on supply chain management (SCM). Demand forecasting, being the cause of all factors that constitute SCM, is the most crucial factor. Therefore, it is essential to have a dynamic demand measuring method, so that companies could to adapt to the continuous market changes, and carry out market and demand predictions. Predicting the future based on a vast amount of information on diverse areas is one of the unique means for forecasting. Moreover, it has been proven that the forecasts are more accurate when there is more information with greater diversity. The advent of the IoT (Internet of Things) technology and the era of big data have provided humans with more information on diverse areas. This research aims to utilize the IoT technology and big data for an accurate forecast of the intermittent demand, which has been a difficult area for prediction until now. Moreover, the paper presents a platform that can contribute to effective inventory management and production planning through intermittent demand forecasting.
IoT, demand forecasting, artificial neural network, failure forecasting, recurrent artificial neural network, changing need, intermittent demand
With the intense competition in today’s society, companies are putting a greater focus on dynamically reacting to the needs of customers through supply chain management (SCM) . As a factor that directly affects SCM, demand prediction is the cause of all activities that occur within the management system . While accurate demand forecasting requires the reflection of the complex managerial environment, it is difficult to consider the entire managerial environment as of now. The non-linear managerial environment is considered as linear environment; therefore, it is difficult to accurately forecast the demand.
Even though intermittent demand forecasting, an area that has been difficult so far, can be performed by using exponential smoothing and Weibull distribution to extract the expected values, it is impossible to accurately predict the demand at a certain point in time. However, as there are frequent requirements for the information on demand at particular points in time, predicting the intermittent demand is a crucial aspect of SCM.
This research proposes a new method of demand prediction, which utilizes IoT data to predict the intermittent demand. Intermittent demand prediction, coupled with IoT technology can accurately forecast the changing needs at specific points in time. It can also accurately predict irregular demands, which has been difficult to do so until now, thereby reducing the safety stock.
2 Theoretical Background
2.1 Features of Intermittent Demand
The changing needs are sporadic and irregular in nature. Intermittent demand exists during the period when there is no demand, and thus identifying its period is impossible. In other words, the conventional researches that predicts the intermittent demand by analyzing only the amount of demand, results in a wide margin of error. However, the IoT technology can be used to collect and analyze information directly related to the life span of an individual product, thereby identifying the change in requirements of an individual product at a certain point in time. These products reflect the demand, and therefore, will be identified as the demand.
2.2 Current Method of Predicting Changing Need
Weibull distribution is the most widely used technique for predicting product failure. This method calculates the expected value of demand from the expected life span of products; therefore, it can be applied for calculating the overall changing needs. However, this method did not prove successful in predicting the sporadic changing needs . Moreover, the time series analysis, used in the earlier research stages, did not fully reflect the sporadic nature of demand . While other methods like exponential smoothing and bootstrapping have been proposed, they were limited to the collection of general information on the effective inventory level instead of demand prediction at specific times . This paper suggests a new method for predicting the intermittent demand of a type of battery that is sensitive to changing needs, using IoT data for the measurement and collection of information, and artificial neural network to carry out the analysis and predictions.
2.3 Selection of Research Subject
Product failure can be divided into three major categories. The products that need to be changed for production are limited to “parts,” which are categorized into the following three groups.
Functional parts are those parts, which are changed following an accident, shock, or abrasion. These parts form the subject of this research. The other two groups are consumable parts that need to be changed periodically with time and accidental parts that are changed after the occurrence of accidents .
By using IoT technology, this research aims to collect real-time product status information, which keeps changing through abrasions, and analyze the accumulated data. Batteries were selected as the subject of research, as they are products that experience shortened life span due to usage. Therefore, this research aims to predict the changing needs of batteries by collecting their real-time status information and analyzing them.
2.4 Artificial Neural Network
An artificial neural network imitates the process of a human brain by delivering and processing information. Inside a human brain, there are innumerous neurons, which deliver and process information by transmitting weak electric signals to each other. When the total number of signals received by a neuron surpasses its maximum capacity, it enters an excited state (firing stage) and sends the electric signals to other neurons . Artificial neural networks, which imitate this information delivering process using digital neurons, are widely recognized for their outstanding forecasting capability in non-linear relations, when compared to regression analysis . The recurrent neural network model, which can arouse past memories through feedback coupling, is equipped with a system that is capable of self-studying, and can be adjusted to the changing environments. Since the late 1990s, the recurrent neural network model has been researched as a method for problem solving .
2.5 Recurrent Artificial Neural Network
The recurrent artificial neural network is a model that is appropriate for dealing with the concept of "time" . As the results are saved on the network as feedback, it can remember the past and can be adapted to the changing environment. This network is classified into the following two types: Elman network and Jordan network.
The Jordan network is a model suitable for the control and learning of mobile robots. It analyzes the output information as input feedback in order to remember a robot's previous location.
The Elman network was developed to interpret the sentences in natural language, and it remembers the past by transforming the activated values of the hidden layer into input feedback. In other words, it remembers the threshold value of the hidden layer for a specific input, and when new information is inputted, it produces new results based on the past values.
In the Elman network, the hidden layers form a perfect circulated connection and include active neurons, whose states are determined by the neurons of the input and hidden layers .
In this research, the Elman network model was used to collect the real-time current data of the batteries and determine the next changing stage. By circulating only the accurate data, the network is able to remember the hidden layer threshold value for the next input.
3 Research Method
3.1 Measuring Battery Current
Fig. 1 NI 9203 Connection Diagram
Fig. 2 Excel Spreadsheet
An artificial neural network model was established in order to predict the intermittent demand based on the analysis of the collected real-time current data. The artificial neural network model deducts specific values from the various input variables, and the information that is inputted as the activation function can be formulated by the following equation:
Sum of Inputs =
n = Number of battery state information
xn = nth state information of battery
wn = Weight of nth state information
The sum of the input values is transformed into the output values by a non-linear function known as the activation function. When the final value obtained after subtracting the threshold value from the input sum is a positive number, it produces a binary output of 1. Alternatively, it produces an output of 0, when the final value is a negative number .
Therefore, "y" simultaneously shows whether a specific battery is nearing its time of change and the changing need. In short, the total sum of "y" can be shown as the overall changing need, "D."