These organizations work with the probability of equipment failure, maintenance and user requirements of spare parts. All of these elements increase uncertainty in this environment. Besides, it is difficult to integrate and process information to maintain good inventory control. This high uncertainty and lack of integration of information cause spare parts inventory excesses and shortages.
This research proposes a new model based on information processing theories to connect the lateral elements of the supply chain, increase vertical information and transform the MSC into a system to decrease shortages and excesses of inventory. This research incorporates a simulation to compare the new model with traditional models of inventory control.
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This study claims that when using the new model with different demands of maintenance, inventory cost is lower than with traditional models of inventory control. The research uses information processing theory as the framework to decrease uncertainty, and consequently decrease excesses and shortages of spare parts in MSC. This paper considers postal logistics, more precisely, the distribution networks for letter mail and parcel mail. The main service provided by postal companies is letter mail and parcel mail transportation and delivery. In this market segment there have been two key efforts during the last few years: reduction in transportation and delivery time service quality and minimization of costs.
Both efforts—reduction of service time and minimization of costs for providing the promised services—have a strong impact on the quality of the strategic planning phases of the respective distribution networks. In this article we introduce the structure of a typical distribution network for letter mail and for parcel mail, and we describe the main subnetworks. Furthermore, this paper deals with two selected projects on optimization of such networks.
Each of the projects covers system analysis, modeling, and for the second project, also the development of an optimization algorithm. This paper considers the postal logistics area, more precisely, the distribution networks for letter mail. A main service provided by postal companies is letter mail transportation and delivery. In this market segment there have been two key efforts during the last few years: reduction in transportation and delivery time service quality and minimization of costs under service quality constraints.
Both efforts—reduction of service time and minimization of costs for providing the promised services—have a strong impact on the quality of the strategic and the tactical planning phases of the respective distribution networks. The Operations Research type of analytical models used in the strategic and tactical planning phases of distribution networks in postal organizations are: facility location, location routing, service networks design, and vehicle routing and scheduling models.
This paper is also concerned with projects on optimization of such subnetworks. Therefore, we have selected three projects dealing with different subsystems and covering the strategic and the tactical planning phases as well. The projects are in the areas of collecting mail from mailboxes vehicle routing , replanning of delivery station locations facility location combined with vehicle routing , and reducing deadheading in the last mile facility location combined with vehicle routing.
Each of the projects covers system analysis, modeling, development of optimization algorithms, and a software prototype. Due to increasing costs, intense competition and increasingly demanding customer expectations regarding delivery lead times parcel delivery services need to continuously improve their logistics networks and processes. This paper analyses the potential to improve both costs and delivery lead times by introducing alternative, direct transportation routes. Here, direct transports avoid hubs within the distribution network and, therefore, reduce transportation distance and time as well as sorting time.
To examine the optimization potential of these new transportation routes, we consider a large-scale distribution network, where distinguishable items have to be transported from sources through certain hubs to predetermined sinks. We introduce a service network design model, which minimizes total long-haul transportation costs within the delivery network while meeting predetermined service levels.
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The model identifies the optimal transportation plan and decides if a service is offered at a specific time period or not. Hub location is a strategic management decision with far-reaching consequences for a company. Designing the hub network and the positions of the locations are critical components according the delivery of the logistic system. Many of the extant research deals with this issue. Yet, even if there is a network, it needs to be continuously adapted to the different conditions.
Quantitative methods in supply chain network design and green supply chain management
On the other hand, quantitative forecasting technique deals with numerical data focus on projection of trends on the basis of historical figures of the business. This method of forecasting is consistent and useful for long term scenario planning of the company. The opinion of the experts in the company helps to forecast the internal parameter of an organization, whereas quantitative data in the form of customer surveys are used for reflecting the sales forecast Frechtling, Small scale companies prefer qualitative forecasting method as it is simple in nature and cost-effective.
On the other hand, lean manufacturing and large scale organizations are proficient in using quantitative forecasting. There are mainly two types of forecasting techniques; time series model and associative model. For example, a business can forecast the quantity of raw materials to order with time series analysis of the previous five years. Moving average deals with the normal average value which is considered as the basic calculation for forecasting.
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It allows to remove the oldest values from the data and add new values. This makes the average move over time. Moving averages method can be used to reflect seasonality in demand. For example, businesses can use week-by-week sales data to predict sale for the coming week using moving averages. Experimental smoothing deals with the weight of the components during the year. It also focuses on the importance of the parameters.
For example, as the newer data emerges, the older data becomes less important. This method is very similar to moving averages. Therefore it is equally popular among retail firms to forecast demand, sales and expected profit margin Ghiani, et al. Lastly, trend projection focuses on graphical representation which provides an overview of the future trend by analyzing least-square value. This method requires data of a longer time period.
The main defining element of this method is that it assumes that all the factors that played a role in past trends will continue to be influential in future too. New companies can use long term data of similar firms in the market to forecast trends. Quantitative forecasting also helps to establish the relationship between forecasted variables. Qualitative forecasting method is characterized as the approach of analysis of data gathered from the opinions of an expert or experienced professionals in an organization. The qualitative forecasting method focuses on summative approaches for undertaking the forecasting process Punch, The opinion of the departmental heads is recorded in the presence of the third party which is accumulated for forecasting.
The opinion of the staffs is recorded in the closed room in a single manner for assessing the validity and reliability of their statement. The principle of the Delphi method aims to validate the forecast. The estimates are to be reviewed until the consensus is reached. For example, a small scale business can forecast the amount of inventory it needs to hold for the next two months. The results are analysed by all experts or department heads in a group discussion.
Group discussions are held until they reach a consensus. Qualitative forecasting techniques are used for identifying any inter-organizational issue that might disrupt regular business process Montgomery, et al. This technique also assists in performing the operation of logistics by preparing the product or service according to that dimension. There are several differences between qualitative and quantitative forecasting techniques and their use in supply chain management or logistics.
Qualitative forecasting deals with the opinion of managers or customers survey which helps to get an overview of forecasted information.
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Whereas quantitative forecasting deals with numerical data. As Qualitative forecasting deals with summative data as it is considered less accurate than that of the quantitative analysis. Qualitative forecasting is considered biased as the data is collected manually, whereas quantitative data relies upon previous performance records Guest, et al. Qualitative forecasting is applicable for short term whereas quantitative is applicable for long term decisions. Therefore, both qualitative and quantitative forecasting method is used for demand forecasting which has become crucially important in the context of managing the logistics.
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