Summary TNT has problems managing the blue cages on their Express network. Tracking of cages is done manually using data from MPSS scanners and ROM&O

Transcription

1 Executive Summary TNT has problems managing the blue cages, used for transporting small parcels, on their Express network. Tracking of cages is done manually using data from MPSS scanners to form static reports. The data is subject to human error and outdated when viewed. TNT wants to replace their current outdated tracking system with an active RFID tag on each cage that can transmit real-time data on their location to their management system. This investigation aims to provide an insight into the cage distribution network, to quantify the effect of the current tracking system and to present the risks and opportunities associated with the implementation of RFID. The main tool used to perform the investigation is the network flow analysis which reproduces the cage distribution network and gives an insight into its behaviour. There are six solution alternatives, including the current situation, four alternatives using current resources and a RFID alternative. The investigation into the current situation has shown that the largest outbound cage flows are from Arnhem and Brussels and the largest inbound cage flows are to Zurich, Madrid and Munich. Without redistribution, Arnhem has the largest undersupply of cages and Tallinn has the largest oversupply of cages. The most critical day of the week is Tuesday when the demand is at its highest. The most redistribution kilometres are made transporting empty cages from Riga, Tallinn, Oslo and Athens to Helsingborg, Turku, Stockholm and Milan. France, Germany and the UK are most sensitive to cage loss. Costs associated with cage distribution are purchase costs (6%), distribution costs (14%), scanning time costs (25%) and loading time delay costs (55%). The segmentation alternative was the best scoring alternatives as it avoided scanning time whilst optimising redistribution. The RFID was the next best scoring alternative as it avoided scanning time but it did not optimise redistribution. It is highly recommendable to TNT to undergo further research into three issues. Firstly, quantify the effect on the accuracy of scanning information when scanning is moved from the outbound to the inbound. Secondly, look into the possibilities of developing a cage flow simulation tool which can optimise cage stock and standardise the redistribution. Finally, TNT must perform a thorough feasibility study of RFID, to define the necessary RFID specifications, the risks and the costs. iv

2 Summary TNT has problems managing the blue cages on their Express network. Tracking of cages is done manually using data from MPSS scanners and ROM&O system to form static reports. The data is subject to human error and outdated when viewed. To improve the management of cages on their Express network, TNT needs to gain an insight into the cage network. Thus, TNT want to replace their current outdated tracking system with an active RFID tag on each cage that transmits signals to wireless access points as the cage enters a warehouse and feeds the data straight to the TNT data management system. However, to justify the investment in RFID for the tracking of cages, hard benefits need to be presented. This requires the cage management problem to be quantified and the risks and opportunities associated with the implementation of RFID to be determined. The investigation will answer the following research questions: 1. What is the effect of the current cage management on the ERN for TNT Express? 2. Can cage management be improved using existing resources? 3. Can cage management be improved using WiFi-based RFID for locating cages? 4. What are the risks and opportunities associated with the implementation of RFID? The investigation will be structured according to the innovation model and conceptualised according to the steady state model of the Delft Systems Approach as defined by in t Veld. The main analysis tool is the network flow analysis. The Network flow analysis is based on a model programmed in Microsoft Excel combining static data on the demand into a dynamic analysis to infer information on the cage movement in the ERN. It provides an insight into the behaviour of the cage distribution network; locating depots with an oversupply and a short supply, defining the critical times and locations, revealing the distribution of redistribution km around the network, detecting countries more susceptible to cage loss and establishing maximum and minimum cage supplies per depot. Secondly, the model allows different cage management alternatives and scenarios to be tested and evaluated by adjusting the redistribution strategy, the demand, the probability of cage loss or even the routing. TNT Express has the most expansive air and road express delivery infrastructure in Europe. The ERN connects 415 depots including 85 international depots and 16 road hubs in 34 countries. TNT works with a 'hub-and-spoke system'. This means that international consignments, picked up in different depots will nearly always travel to a hub, before travelling to another depot. Cages can be found anywhere between the origin depot and the destination depot. Having no designated linehaul or section, cages can move through any depots in the ERN in a couple of days. The position and size of depots on the network influences the amount of inbound and outbound cages. Accordingly cages need to be redistributed to prevent undersupplies and oversupplies. Last year TNT estimates to have spent 1,202,506 redistributing empty cages on the network. The Network Organisation has located 13,500 cages at 226 of the 415 depots in the ERN. Network Organisation purchases 1,900 new cages every year at a price of 608 each. On average 600 cages per year are bought to compensate loss. The other viii

3 1300 cages are purchased to accommodate growth and damaged cages. Cages are identified using either the scanner or manually by filling in a cage import sheet. The ROM&O then produces a static report for the inbound and outbound cages. In addition, in practise LBO s and FLT drivers do not always identify the cages. A cage is defined as missing when it is inactive for 1 week or longer. Cages inactive for 2 months or longer are considered as lost and the depot responsible is charged 1, TNT s ambition is to be a leader in their market. Subsequently, TNT strives towards outstanding performance to offer a service to customers that is better than their competitors. With new market pressures, optimising the distribution of cage stock on the ERN has become a vital step in that strategy. NO is concerned with increasing control, improving reliability and maintaining or increasing speed of service. The primary innovation conditions are short term benefits (within 3 years) and long-term operational potentials. Six solution alternatives will be tested, including the current situation, four alternatives using current resources and the WiFi-based RFID alternative. The first current resource alternative is the segmentation alternative which transforms the network into a clear-cut tree structure and cages are regulated according to the structural hierarchy. The second resource alternative is the integration alternative where cages are scanned 3 times on the inbound and the outbound and redistribution is based on forecasting and implemented with a strong top-down by NO. The elimination alternative is the next alternative where all cages are eliminated from the transport procedure. The last current resource alternative is the management alternative where cages are scanned on the inbound rather than the outbound. The network flow analysis model has 26 depots and 15 hubs which represent the major nodes in the network. Based on the volume transported through the network, the model represents 15% of the actual situation. The first week of data in the model is extracted from the TNT database and can be fluctuated by a predefined percentage per week. The demand is translated into cage amounts and transported over the most commonly used routes. No varying price or time restrictions are taken into account. In the model of the current situation, all redistribution goes through the Network Organisation and redistribution is bought in effect a day after the weekly inventory. The model has been validated using a sensitivity analysis and the output has been compared to data from the actual situation. The model has shown to be sensitive to its inputs and thus allows the behaviour of the network to be investigated. The largest weakness of the model is that volumes cannot be aggregated to hubs, which creates a disproportioned view of the amount of cages going through the depots. Thus, caution has to be taken in interpreting the results of the model to the actual situation. The network flow analysis has shown that during the week the largest volumes of cage flow are from Arnhem and Brussel to a range of hubs and depots in the rest of Europe. Zurich, Madrid and Munich have the largest cage inflows. Arnhem has the largest imbalance of cages with an outflow 8% larger than its inflow. Tallinn has the relative largest inflow of cages at 20% of its outflow. From the demand patterns the analysis has shown that the most critical day of the week is Tuesday when the demand is at its highest. The outflow on Tuesday must then be the minimum amount of cages in the warehouse to avoid undersupply. The most redistribution km are made transporting empty cages from Riga, Tallinn, Oslo and Athens with an oversupply to ix

4 Helsingborg, Turku, Stockholm and Milan with an undersupply. Locations with an undersupply are generally hubs and locations with an oversupply are small depots found in ends of the network. The distances between these locations are large and many trips have to be made because the oversupply at the depots is relatively low compared to the undersupply at the hubs. With respect to the locating of missing and lost cages, the network flow analysis has shown that France, Germany and the UK are the most sensitive to cage loss; however, they also transport the most cages in the network. Based on the model of the current situation, it can be said that 2 million km per year are driven to redistribute cages, that there are 779 lost cages per year, that 450,000hrs is spent on scanning and that there are 13,600 cages on the ERN in total. The segmentation alternative was the strongest alternative in the efficiency of redistribution but contained a higher total number of cages on the network. The integration alternative eliminated missing and minimised lost cages on the network but resulted in a high scanning time at depots. The elimination alternative demonstrated that cages save around TNT 2 million hours per year resulting in 21 million euros. The adjustment of outbound scanning to inbound scanning in the management alternative resulted in a decrease in missing and lost cages by but its effects could not be quantified. Finally, the RFID alternative resulted in less missing and lost cages but in more redistribution km. When the alternatives were evaluated according to the priorities of the NO on the one hand and the depots on the other hand, the elimination alternative and the segmentation alternative were the best scoring alternatives, respectively. The elimination alternative was the best-scoring alternative overall. Costs associated with the management of cages can be distinguished into purchase costs, distribution costs, scanning time costs and loading time delay costs. Based on the output of the model of the current situation; purchase costs account for 6% of the total costs, distribution costs for 14%, scanning time costs for 25% and loading time delay costs for 55%. Thus, the greatest cost drivers are the delay costs. Time spent on scanning must be minimised but more importantly, undersupply of cages at depots resulting in a loading delay must be avoided. Based on the analysis of total costs, the segmentation alternative and the RFID alternative are the best solution as distribution is improved without additional scanning time. The current situation is third place. It is recommendable that based on these results, TNT investigate the possibilities associated with the segmentation alternative or the RFID alternative in more detail. This investigation has made it clear that it is possible to simulate cage flow through the ERN to provide forecasting information for a systematic redistribution of cages like in the segmentation alternative. A cage flow tool could be based on existing TNT tools and be implemented on the short-term without implementation costs. RFID is a good alternative but has higher implementation costs and higher risks. However, RFID is a labour-free tracking method capable of providing the information that TNT need for their forecasting. Based on the analysis a number of boundary conditions and application suggestions have been formulated: RFID chip must focus improving the efficiency of distribution through forecasting. The implementation of RFID on cage tracking should therefore be at least for the mid-long term. x

5 Implementation of RFID on the cage network must be complimentary to a clean up of the network, implying the standardisation of procedures and the retracing of the large connections. The RFID chip should embody information on more than just the current location of the cage. Implement RFID at hubs first. Inbound scanning must be implemented first. It is highly recommendable to TNT to employ further research into three issues. Firstly, quantify the effect on the accuracy of scanning information when scanning is moved from the outbound to the inbound as this has potential to be a low profile but very effective solution. Secondly, look into the possibilities of developing a cage flow simulation tool which can optimise cage stock and standardise the redistribution. Finally, TNT must perform a thorough feasibility study of RFID, to define the necessary RFID specifications, the risks and the costs. xi