An input and output Excel file for a material supply assignment model

An input and output Excel file for a material supply assignment model MASTER THESIS Universiteit Gent – Industrial Management 2011/ 2012 Sergi Fabregat Corominas Supervisor: prof. dr. ir. H. Van Landeghem Co-supervisor: dr. V. Limère An input and output Excel file for a material supply assignment model Sergi Fabregat Corominas Supervisor: prof. dr. ir. H. Van Landeghem Co-supervisor: dr. V. Limère MASTER THESIS Universiteit Gent – Industrial Management 2011/ 2012 June2012 Acknowledgements First of all, I would like to thank my promoter Hendrik Van Landeghem and my supervisor Veronique Limère for the continuous support I have received during my stay at Universiteit Gent. I also would like to thank to all the Industrial Management Dempartment for offering me a workspace and for solving all my doubts during these months. I would also like to thank Universitat Politècnica de Catalunya and Universiteit Gent to give me the opportunity to realize my Master thesis abroad. Furthermore many thanks to all the friends I made during my stay in Gent. Meeting new people from other countries is always an incredible experience and, with them, I have lived some experiences that I will always remember. Finally, I would like to thank my family and my friends of Barcelona. They have supported me during all my stay abroad and have encouraged me to get the maximum performance to this new experience. Many thanks to all of you! Abstract Summary Nowadays companies need to have a competitive production system in order to face the high competition. As it increase a lot the costs, companies realize that a way for obtaining an advantage over their competitors is improving their logistics systems. Although it is not a direct activity nearly linked to the company target, at the present time most of the companies have a specific department (it could be either of their own property or an external firm) in order to improve their logistics organizations and supply chain methods. Even though it depends on the type of company that considered, is well known that logistics costs constitute around 10% of the final cost of the product. Because of this considerable percentage, the optimization cost study related with the supply chain has a lot of importance in the companies. For all these reasons mentioned above, dr. Veronique Limère realized a study that will be presented in the second chapter of the thesis in order to optimize the cost of the distribution of the different parts to be treated in the different workstations in a typical automotive industry. vi To achieve the objective of the study, a Mixed Integer Linear Programming Model (MILP) was developed previously and defined each of the parameters and variables that affect the final cost of the distribution. To obtain the optimal cost, this model informs, for each one of the parts to be treated, which of both material supply methods, bulk feeding or kitting must be used. On the one hand, bulk feeding is the most simply and direct material supply system. In this type of supply method, parts are supplied in containers to the assembly line. Each container contains a large quantity of that type of part. Containers are stored at the border of the line (BoL) of each assembly workstation in order to satisfy the demand needed in each assembly line. On the other hand, kitting system create heterogeneous packages by grouping together the exact components that are needed in a particular assembly operation. Each kit can containthe parts for one or more assembly operations. Kits are stored at the border of the line (BoL) of the assembly lines in order to satisfy the demand needed in each assembly workstation. Based on the model mentioned, the main objective of this thesis is: Objective 1: To develop a work tool for companies that are using the mathematical model designed by dr. Veronique Limère. This tool has to targets: On the one hand, it has to be able to obtain different results depending on the values of the parameters in order that the employees can compare them. On the other hand it has to be easy to use and it must give the results in a comfortable way in order to the employee can understand the necessary information faster. Moreover, this thesis has a second objective in orther to put in practice the work tool mentioned in the first objective. It consists on: Objective 2: Studying a particular company data. We will consider possible changes in the way to supply parts, from the moment that parts are in the warehouses to the moment that they are picket to be assembled. Results will be obtained and analysed for each modification considered. vii To achieve these objectives the thesis follows the next structure: Firstly, in Chapter 1, an introduction of the sector and a brief explication of the thesis of dr. Veronique Limère are presented. In Chapter 2 is developed the tool for the companies and is explained how it has been developed. In Chapter 3 an extended analysis is realized and finally, in Chapter 4, conclusions are given. viii Table of Contents Acknowledgements .......................................................................................................................... iv Abstract ................................................................................................................................................ v Table of Contents ........................................................................................................................... viii List of Figures ................................................................................................................................... xii List of Tables .................................................................................................................................... xvi Notations ........................................................................................................................................ xviii Chapter 1 Introduction ................................................................................................................ 1 1.1 Material supply systems .............................................................................................................. 2 1.1.1 Main differences between the different part feeding systems ............................ 6 1.1.2 Line stocking vs. Kitting system: Advantages and disadvantages ...................... 8 1.2 Contribution .................................................................................................................................. 12 1.2.1 Objectives .............................................................................................................................. 12 ix 1.2.2 Content ................................................................................................................................... 12 Chapter 2 Model review ............................................................................................................ 14 2.1 Material flows ............................................................................................................................... 15 2.1.1 Line stocking ........................................................................................................................ 15 2.1.2 Kitting ..................................................................................................................................... 15 2.2 Mathematical model .................................................................................................................. 17 2.2.1 Objective function .............................................................................................................. 17 2.2.2 Picking cost ........................................................................................................................... 17 2.2.3 Transport to the line ......................................................................................................... 18 2.2.4 Kitting Cost ........................................................................................................................... 18 2.2.5 Cost of replenishment ...................................................................................................... 19 2.2.6 Restrictions ........................................................................................................................... 19 Chapter 3 Possible logistic changes in the factory ........................................................... 20 3.1 Modifications considered ......................................................................................................... 20 3.1.1 New picking technology in the supermarket........................................................... 21 3.1.1.1 Pick –by-voice ........................................................................................................................ 21 3.1.1.2 Pick –by-light ......................................................................................................................... 22 3.1.1.3 Pick –by-vision ...................................................................................................................... 23 3.1.2 Outsourcing .......................................................................................................................... 24 3.1.3 New transport equipment .............................................................................................. 25 3.1.4 Kits Batch size ...................................................................................................................... 25 List of Tables Table 1.1: Advantages and disadvantages of line stocking and kitting ...................................... 10 Table 1.2: Advantages and disadvantages of line stoking and kitting (continued) ................ 11 Table 3.1: Outsourcing vs. In-house kitting. Advantages and Disadvantages .......................... 24 Table 3.2: Example of kitting a particular part depending on the batch size. ......................... 27 Table 4.1: Formulas for obtaining the values of the results ............................................................ 36 Table 4.2: Example of the template used in the work tool developed. ........................................ 39 Table 4.3: Formulas for obtaining the number of results given per parameter and the total number of results. .......................................................................................................................................... 40 Table 4.4: Computational results obtained with the parameters values of table 4.2. ........... 42 Table 4.5: Matrix of the combinations of the values that the different parameters take according to the example of the table 4.2. ............................................................................................ 44 Table 4.6: Combinations of the parameter values according to the example given in table 4.2. ...................................................................................................................................................................... 45 xvii Table 4.7: Explication of how the table is filled ................................................................................... 49 Table 4.8: Small table for the OPERARI COST named “OPERARI_COST”: ................................... 51 Table 5.1: Initial values of the parameters ........................................................................................... 57 Table 5.2: Costs values according to the parameters values of Table 5.1 .................................. 57 Table 5.3: Parameter values for each method ..................................................................................... 68 Table 5.4: Outsourcing vs. in-house kitting .......................................................................................... 68 Notations Sets Set of all parts supplied in small b o xes Set of all palletized parts Set of all parts; I = Ip ∩ I b Set of all parts used at station s Set of all work stations s Set of variant parts of i ∈ I ; the family of part i Parameters Maximum number of units of a part i in one pic k due to physical characteristics (weight, volume) of part i Capacity of the milk run tours for boxes (number of boxes per tour) Capacity of the milk run tours for kits (n um b er of kits per tour) Batch size for assembling kits xix Average distance for the operator at w orkstation s to pick from a bulk container of part i (m) Average distance for the operator in the sup er market to pick from a bulk container of part i to kit for station s (m) Average distance for the line-operator to pic k from a kit (m) Yearly demand for end product (= v ehicle) Distance of the milk run tour for boxes (m) Distance of the milk run tour for kits (m) Distance of transport between the pallet w are house and workstation s (m) Corrector factor of the distance between the pallet warehouse and the workstation s The depth of the line - i.e. the p erp endicular distance between the operator working at the product and the border of line (m) Percentage of end products for which part i is assembled at station s (frequency) Fixed production time for each kit (h) Vertical stacking heigh t of boxes (units) on the BoL Length of a box along the line (m) Length of a kit container/rack along the line (w e assume no stacking of kits con tainers) (m) Length of a pallet along the line (we assume no stacking of pallets) (m) Available length along workstation s (m) Number of units of part i assembled per v ehicle (if the specific variant part i is used) at station s Number of units of part i contained in the orig inal packaging; packing quantity of part i Cost of labour (per hour) of an operator (€/h) xx Cost of labour (per hour) of a logistic operator (€/h) Average walking speed of an operator (m/h) supplier packaging of part i {Box, P allet } Yearly usage of part i at station s; q is = m is f is d Expected capacity utilization of the milk run tours for b o xes Expected capacity utilization of the milk run tours for kits Constant cost of the replenishment of the 3PL (€) Constant cost for the replenishmen t of one b o x in the sup ermark et (€) Constant cost for the replenishmen t of one pallet in the sup ermark et (€) Average time to search for the required part from bulk stock at the lin e (s) Average time to search for the required part from bulk stock in the s up ermark et (s) Number of units of part i that will on a verage be picked in one pick when part i is kitted for station s Number of units of part i that a kit can max imally hold; this categorical parameter repre sents the volume (small, medium, large, extra-large) of a part i {100, 20, 5, 1} Corrector factor of available volume in a kit V eloci t y of the material handling equipmen t for milk run tours for b o xes (m/h) V eloci t y of the material handling equipmen t for the milk run tours for kits (m/h) V eloci t y of the material handling equipmen t for pallets (m/h) W eigh t of part i (kg) W eigh t constrain t on one kit unit; m ax im um w eigh t per kit (kg) xxi Variables Integer auxiliary v ariable Number of kits needed at stations s to assemble one v e hicle Integer auxiliary v ariable Number of facings needed to store boxes along station s (with vertical stacking of b o xes) Binary decision v ariable xis = 1, if part i is bulk fed 0, if part i is kitted Cost and time factors The yearly labor cost for kit assembly (€) The yearly labor cost for operator picking at th e assembly line (€) The yearly labor cost for the replenishmen t of the sup ermark et (€) The yearly labor cost (€) The yearly internal transport cost (€) The yearly cost for pallet transport (€) The yearly cost for box transportation (€) The yearly cost for kit transport (€) The yearly labor cost for the replenishment of the 3PL (€) The yearly fixed cost to assemble all kits (€) Average time to pick a unit of part i from a bulk container at station s (h) Average time for the line-operator to pick a unit from a kit (h) Average time for the operator in the supermar ket to pick a unit from a bulk container of part i to kit for station s (h) The yearly variable cost to assemble all kits (€) Chapter 1 Introduction In the field of the automotive industry, nowadays, it is required to get low prices and timely delivery in order to obtain an advantage against the other companies competing. For this reason, companies are trying to reduce their costs. On the one hand, companies try to reduce the costs directly related to the vehicle manufacture, as it could be the cost of the material of the different parts that conform the vehicle, or the cost of the energy consumed to assemble the parts (direct costs), On the other hand, companies also try to reduce those costs that also affects to the final cost even they are not a direct cost (indirect costs). In this type of costs it is included the costs related to material supply methods. To reduce material supply costs, logistics departments have been searching the optimal way to supply the material to the different workstations. To achieve it, companies are investing in developing mathematical models that gives the better way to supply a Chapter1: Introduction 2 component. These models have into account the part characteristics and plant design and they are developed to explain us how each of the parts needed in the floor shop has to be supplied. However, the part supply of mixed-model assembly lines is a largely unexplored field of research (Boysen and Bock, 2011). Because of the reasons explained above, dr. Veronique Limère realized her own model presented on her Ph.D.: “To Kit or Not to Kit: Optimizing Part Feeding in the Automotive Assembly Industry”. In this thesis, we will use the mentioned model developed by dr. Veronique Limère to reach the objectives. Is for this reason that before presenting the model, we must know how automotive industries are organized and which different ways to supply material exists nowadays. Once we have all this information, we will be able to understand why the model has been developed like it is. 1.1 Material supply systems In order to be able to understand the model, it is necessary to have a previous knowledge about the different ways that one can find in industry to supply the materials from the warehouses to the assembly line. Every company is allowed to choose the way to supply the material as it thought it would be more efficient. Otherwise, it is well known that there are several supply systems methods that usually obtain the best results and are the most typical applied methods. These methods are explained below: The first and most simple method is the bulk feeding or line stocking method. In this method containers are stored at the border of the line. Each container contains one different type of part. In Figure 1.1 we can see containers at the border of the line, each one with a different type of part needed. The amount of parts inside each container is not an exact quantity according to the schedule, so this method does not need previous handling operations. Chapter1: Introduction 3 Figure 1.1: Picture of a BoL with containers used in the line stocking method Next, we can find the downsizing method. In this case, parts are first repackaged into smaller containers before being supplied to the workstation. Compared to a line stocking system, downsizing needs a previous handling operation, which constitutes an extra spent time and, consequently, an extra cost. However the fact that parts are in a smaller bins will diminish the searching times and walking distances. In Sequencing supply system, parts are supplied to the line at the moment that is needed. Moreover it is supplied the exact quantity needed according to the schedule, instead of storing the parts in containers at the border of the line. In this case, time spent on handling operations will be even higher than with downsizing. On the other hand, searching times and walking distances will be less than in downsizing. Finally, the method that needs more previous work is the kitting method. Kitting is the practice of delivering components and subassemblies to the shop floor in predetermined quantities that are placed together in specific containers (Bozer and McGinnis, 1992). Instead of delivering the demanded parts of each station in huge containers with large quantities of parts, in a kitting system, the exact parts are first selected and pulled together in kit containers before they are delivered to the workstation. Every kit container can support more than one assembly operations. This method then also needs Chapter1: Introduction 4 additional material handling activities, even more sophisticated than in downsizing and sequencing. Instead of it, cost related to searching time and walking distances will be eliminated. Figure 1.2 (Limère, 2011) illustrates how parts are displayed at the border of the line depending on the material supply system. As it can be seen there is a significant reduction of parts if we compare line stocking vs. kitting, so operator walking distances will be reduced considerably and searching times will be eliminated if parts are sequenced (sequencing and kitting systems). However it can be easily seen that for displaying the material at the border of the line it is necessary more preparation time and handling activities in kitting, sequencing or downsizing compared with line stocking. Figure 1.2: Impact of different line feeding methods on the display of parts at the border of the line (Limère, 2011). Chapter1: Introduction 11 Table 1.2: Advantages and disadvantages of line stoking and kitting (continued) Chapter1: Introduction 12 1.2 Contribution After having a better knowledge of the subject we need to set our objectives and the content exposed in this thesis. 1.2.1 Objectives In this thesis the main objective is to develop a work tool for companies using the mathematical model designed by dr. Veronique Limère. This tool has to permit to the employees to realize more accurate studies. We want to develop a tool in which we can obtain different results depending on the values of the parameters and compare them. Moreover it has to be easy to use and it must give the results in a comfortable way in order to the employee can understand the necessary information faster. In the background, another objective is to put this tool in practice. A study of a particular company data will be realized. We will consider possible changes in the way to supply parts from the warehouses to the assembly line in order to put in practice the variance of the parameters. Results will be obtained and analysed for each modification considered. 1.2.2 Content The remaining part of the thesis is divided in five more chapters. In Chapter 2, it is presented the mathematical model developed by dr. Veronique Limère. In order to understand it, all parameters and variables that are included in the model are first defined and explained. The whole information of this chapter is extracted from Veronique Limère’s Ph.D. thesis: “To Kit or Not to Kit: Optimizing Part Feeding in the Automotive Assembly Industry” where the model is presented and also explained. Chapter 3, presents all possible changes that are considered in this thesis in the way to supply the parts. We will also explain how it affects to the model. In Chapter 4, the tool developed is presented. It is explained what the tool allows to do and which information it can give to the company that is using it. Moreover it is Chapter1: Introduction 13 explained how it has to be used. Finally, an explication of how it is linked with Limere’s mathematical model is given. In Chapter 5 the tool developed is put into practice by using it with a specific data of a company. Results are given and explained in order to help the company to take decisions. Chapter 6 concludes the thesis. Chapter 2 Model review As it is mentioned before, this thesis is based on a Ph.D. thesis and all the results are obtained from the model of this Ph.D. Is for this reason that in this section it is presented the model realized by Dr. Veronique Limère: “To Kit or Not to Kit: Optimizing Part Feeding in the Automotive Assembly Industry”. This model was developed in order to obtain the way to find an optimal allocation of parts to the different supply methods and let us know which would be the total supplying cost of the company. To know about where the model comes from, it is described the different types of costs that can affect the final cost. The mathematical model includes the costs associated with the parts leaving the warehouse to the moment that parts are assembled. In section 2.1 a detailed explanation of the material flow in each type of supply is given. In Section 2.2, the mathematical model is presented. All the information used in this chapter is taken from the Ph.D.: “To Kit or Not to Kit: Optimizing Part Feeding in the Automotive Assembly Industry” (Limère, 2011). Chapter 2: Model review 15 2.1 Material flows In this section it is explained how is the supply process that follows parts from the moment that they are leaving the warehouse until they are assembled with the two supply systems studied. This section is intended to make understand all the parameters that are included in the model. 2.1.1 Line stocking As explained before, this is the most direct method. Parts are supplied from the warehouse to the workstations in containers. Each container contains only one type of part. To realize the model, it is considered two kinds of containers: pallets and boxes. There are two different ways of transporting the packaging containers depending on the type of container. - If the packaging container is a unit-load (pallet) it is transported by a forklifts and it is transported one by one. Figure 2.1 shows a forklift truck carrying a pallet. - If the packaging container is a box, it is transported by a tugger train that carries out a milk run tour. Figure 2.2 illustrates a tugger train transporting boxes. 2.1.2 Kitting In kitting system, parts are also supplied to the factory in pallets or boxes in which each container contains only one type of part and in the factory there is an area to realize the kits according to the schedule. In the study realized by dr. Veronique Limère it is assumed that the company studied works with an in-house kitting and there is a supermarket where operators walk to pick the parts that are needed for preparing the kit. It is also assumed that the central picking supermarket is logically organized in picking zones, where an aisle represents a zone which contains all variant parts that can be consolidated in a kit for a certain work station (Limère, 2011). Furthermore it is Chapter 2: Model review 16 assumed that multiple kits of the same type are assembled in batches of five because five kits fit on one rack (Limère, 2011). The transport from the warehouse to the supermarket is realized, as in line stocking, in two different ways depending on the type of container: - By forklift if the container is a unit-load (pallet). Figure 2.1. - By a tugger train if the container is a box. Figure 2.2. After preparing the kits in the supermarket they are transported in kits containers (each kit container can carry multiple kits of the same type) by a tugger train that realizes a milk run tour. As one kit is consumed per takt time, kit container replenishments are needed according to constant time intervals (Limère, 2011). Figure 2.1: Forklift truck carrying a pallet Figure 2.2: Tugger train carrying boxes Chapter 2: Model review 17 2.2 Mathematical model In this section the mathematical model is given and parameters are defined in order to understand the model. This is a summary of the P.hD. based on: “To Kit or Not to Kit: Optimizing Part Feeding in the Automotive Assembly Industry” written by Dr. Veronique Limère, 2011. In this Ph.D. we can find an extended explication about why it is modelled like this. This model gives us the information of which supply method has to be applied in each of the parts in order to have the lowest cost. The model is implemented using the modelling language AMPL 11.2 and solved with CPLEX 11.2. 2.2.1 Objective function The Final Cost of the whole model is divided into four different types of costs: Picking cost at the line, internal transport cost to the line, the cost for kitting and finally the replenishment cost of the supermarket. With all that, the objective function of the model represented as: (2.1) 2.2.2 Picking cost The labor cost for operator picking at the assembly line is given by the following expression: (2.2) With, (2.3) Chapter 2: Model review 18 (2.4) e Any small number (2.5) (2.6) 2.2.3 Transport to the line The total transport cost is the sum of the costs for the different types of transportation: trough pallets, box or kits. (2.7) With, (2.8) (2.9) (2.10) 2.2.4 Kitting Cost The labor cost for Kit assembly is given by: (2.11) Chapter 2: Model review 19 With, (2.12) 2.2.5 Cost of replenishment The total cost of the replenishment of the supermarket can be defined as: (2.13) 2.2.6 Restrictions The objective function is subjected to the constraints exposed below: (2.14) (2.15) (2.16) (2.17) (2.18) (2.19) (2.20) Chapter 3 Possible logistic changes in the factory As we have seen in the previous chapter, the mathematical model offers the opportunity to select, for each of the parts that have to be supplied, the material supply method which is most cost effective for the overall material delivery system. However, some considerations have been assumed with this model. In this thesis, we want to take into consideration some changes that the company could realize in the way of performing the parts supply. In section 3.1 are presented the possible modifications that we want to consider. In section 3.2, we expose the changes realized in the mathematical model in order to study these considerations. 3.1 Modifications considered As it has been explained, to create the mathematical model some parameters have been fixed and some characteristics of the way to supply the parts have been assumed to be Chapter 3: Possible logistic changes in the model 27 The following values are used in this example: Batch size (kits) 3 4 5 6 7 8 9 10 1,5 2 2,5 3 3,5 4 4,5 5 4 4 4 4 4 4 4 4 2,5 2,5 2,5 2,5 2,5 2,5 2,5 2,5 2,5 2,5 2,5 3 3,5 4 4 4 Satisfies conditions? NO NO NO YES YES NO NO NO Kitting cost 50,80 50,80 50,80 42,33 36,29 31,75 31,75 31,75 Table 3.2: Example of kitting a particular part depending on the batch size. As we can see in the results shown in table 3.2 and can be appreciated in figure 3.5, there is a range of values where the opportunity for batch for a particular part remains constant and, consequently, as we can see in figure 3.6, the kitting cost for that part also is constant in certain values. This range corresponds to those values that do not satisfy at least one of the conditions 3.2 and 3.3 mentioned before, which corresponds to a batch size of 8, 9 and 10 kits and also a batch size ok 3, 4 and 5 kits. This fact demonstrates that the batch size value only affects to the cost of kit a part if condition 3.2 and condition 3.3 are satisfied. Chapter 3: Possible logistic changes in the model 28 Figure 3.5: θ value for a particular part depending on the batch size. Figure 3.6: Kitting cost for a particular part depending on the batch size. 0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 345678910 θ Batch size (kits) Oportunity for batch for one part 0,00 10,00 20,00 30,00 40,00 50,00 60,00 345678910 Kitting cost (€) Batch size (kits) Kitting cost for one part Chapter 3: Possible logistic changes in the model 29 3.1.5 Length available at the workstation The available length along a workstation is a restrictive parameter. In this thesis we want to study how it affects at the final cost. It is easy to understand that as a restrictive parameter, when the available length along the workstation is higher, the cost will be lower. Normally, factories do not use the whole length of the workstation because a part of it is reserved for being used in special circumstances. We are going to study how it affects to the final cost and to the percentage of parts kitted if the company uses at least some of the space that is not being used. 3.1.6 Total volume of a kit We are also going to consider the variance of the kits dimension. As a restrictive parameter, if the available volume increases, the final cost will diminish and vice versa. In this thesis we want to consider that volume available in kits increases to see if it will suppose a significant reduction of the cost. 3.1.7 Other possible modifications It could happen that companies realize changes in the disposition of the stores like the warehouses and the supermarket. If it happens the distance that the tugger train walk to realize the milk run tour and also the distance walked by the forklift from the warehouse to a station will vary. It can either increase or decrease depending on the new disposition. Moreover, because of the variance of the distance between the supermarket and the warehouses, the cost of replenishment of the supermarket will also vary according to the variance of the value that the constants of replenishment (Rb and Rp) acquire. For this reason we will consider the possibility that the mentioned parameters (Dsp, Dk, Db Rb and Rp) vary. We will also give the opportunity to vary the maximum weight per kit. The labour cost of an operator (per hour) could also vary depending on the company. Chapter 3: Possible logistic changes in the model 30 3.2 Changes on the mathematical model To be able to consider all the possibilities explained on the section 3.1 of this chapter we have had to define new parameters and add them in the mathematical model. We also have defined a new cost. 3.2.1 Operator logistic cost As it has been mentioned before, in this thesis we are considering the possibility that a Third Party Logistic realizes the kitting activity. As is it explained in Table 3.1 one of the differences between outsourcing and in-house kitting was the cost of labour of an operator. Therefore, to differentiate between the labour cost of a factory operator and the labour cost of an operator of the external company that realizes the kitting activities, a new parameter has been defined. Moreover the parameter that indicates the cost of labour of an operator (OC) has been redefined: OC (€/h): Cost of labour (per hour) of an operator working in the factory. OCL (€/h): Cost of labour (per hour) of an operator working in an external company. Thereby, all the formulas of the costs mentioned in Chapter 2 remain the same excepting formula 2.11, which corresponds to the kitting cost. The new formula is: (3.1) In which OCL is the parameter already defined and the other parameters and variables are the same that defined in Chapter 2. Chapter 3: Possible logistic changes in the model 31 3.2.2 Available length corrector factor We would like to consider that the available length along the work station is not a constant value and we are going to study how it affects to the final results. In the Ph.D realized by dr. Veronique Limère it is supposed that the available length is 8 metres. In this thesis, in order to obtain different values, we have defined a correction factor which is multiplied per the available length supposed (8 meters). Lfact Available length corrector factor. Thereby, the restriction referred to the available space is modified: (3.2) 3.2.3 Available kit volume corrector factor To be able to analyse which would be the reduction of the cost if available kit volume increases, we have defined a correction factor which is included in the restriction related with the kits volume Vfact Available volume correction factor. Thereby, the restriction is modified: (3.3) As the kit can contain less, we also have to consider that the size of the kit is smaller. Therefore, we should also reduce the space it takes at the line (Lk). Thereby, the restriction of the space available is modified: Chapter 3: Possible logistic changes in the model 32 (3.4) 3.2.4 Cost of replenishment of the 3PL We have included a new cost in order to satisfy the transport between the factory and the 3PL and vice versa if the company is outsourcing the realization of the kits. If not, this cost will be zero. This cost, C3PL, is determined by a constant cost for the replenishment of one box or pallet (we consider it is the same cost) defined as: R3PL(€) Constant cost for the replenishment of one box or pallet in the 3PL. The cost of replenishment can then be defined as: (3.5) 3.2.5 Forklift distance corrector factor As have the possibility to modify the distance that the forklift has to realize we have defined a factor, which is multiplying with de this distance in the cost of transport of pallets. Dpfact Corrector factor of the distance between the pallet warehouse and workstation s (3.6) Chapter 3: Possible logistic changes in the model 33 3.3 Conclusions Some real situations are now considered and the model is now prepared to take into account the possibilities mentioned during the chapter. Therefore, companies that are interested on implant some of this considerations can now use the model. However, a work tool is needed to be developed in order to study the impact of these new considerations. In Chapter 4 we present this tool. Chapter 4 Company work tool In this chapter we present a tool that has been developed with the objective to be used by companies. It has been created in order to satisfy and analyse all the considerations exposed in Chapter 3. The target of this tool, a part of giving new possibilities to study that will be mentioned during this chapter, is to make the work more comfortable to the employee using it. For this reason, we have chosen Excel to be the software of our tool, as it is common software to be used in companies. To make it possible, we have linked the Excel file with the Run file of the mathematical model developed by dr. Veronique Limère, which is implemented using the modelling language AMPL 12.2 and solved with CPLEX 12.2. Thereby, the employee introduces the parameter value needed to be studied in the excel file and, after executing the model, results are obtained on it. Chapter 4: Company work tool 35 Figure 4.1: Link between the Company work tool and the model In section 4.1 it is explained what the tool allows us to do and in section 4.2 we present the modifications realized in the files of the mathematical model. Finally, section 4.3 explains the steps to follow to use the tool. 4.1 Front-end: Excel file developed In section 4.1.1 it is explained the structure of the files. In section 4.1.2 an explication of how the employee has to introduce the values of the parameters that the company wants to consider is given. Section 4.1.3 explains how the results are obtained. 4.1.1 Structure of the files To develop our tool we have created two new excel files: The ACTIVATE_Company_Tool file and the COMPANY_TOOL file. The first one is the one where the results obtained from executing the model are displayed. It is only there to let the COMPANY_TOOL file read the results obtained from it. It is needed because as the results comes from another program, excel does not allows to write on this file. It is just an “only read” file. The second file, the COMPANY_TOOL, is the main part of the tool developed. On it we can find five sheets: COMPANY PARAMETERS, OBTAIN RESULTS, GRAPHS, txt and Values. Input Data file Output Run file Mod file Company work tool Front-end Back-end Chapter 4: Company work tool 36 The first three sheets, which their names are written in capital letters, are the ones that the employee has to use. The two others (txt and Values) are information required but not to be modified. In the ‘COMPANY PARAMETERS’ sheet we find the template that the employee has to fill in. Moreover there is a table where we obtain all the combinations of the parameter values, once we have filled the template. Finally, we also have a table that is needed only to calculate the values for all the possible combinations. These are intermediate results and do not have to be modified and do not give information. In next section the template is presented and an example of how to use it is given. In the ‘OBTAINRESULTS’ sheet, we first find the lists of results obtained. Secondly there is the parameter’s combination and the table of results. Finally it gives information about the feasibility of the solution. The ‘GRAPHS’ sheet shows the results in graphs. In the txt sheet there are copied the list of results obtained in the ACTIVATE_Company_Tool. In the ‘Values’ sheet, the list of the ‘txt’ sheet is manipulated in order to transform the text in numerical values. To achieve it we have used the following excel formulas: Formula Description =LEFT(txt!A1;SEARCH("=";txt !A1;1)) It writes the part of the text that is in the left of the equal (=). In this case it writes the name of the result. =RIGHT(txt!A1;LEN(txt!A1)- SEARCH("=";txt!A1;1)) It writes the part of the text that is in the right of the equal (=). In this case it writes the value of the result =SUBSTITUTE(B1;".";",";1) It substitutes dots per comas =VALUE(C1) It convert a text in value Table 4.1: Formulas for obtaining the values of the results Chapter 4: Company work tool 43 =OFFSET(OBTAINRESULTS!$AK$7;0;0;COUNTA(OBTAINRESULTS!$AK$7:$AK$5005);1) Figure 4.2: Formula created for obtain the results in the graphs The first value (OBTAINRESULTS!$AK$7) indicates the cell that we are making reference. The second value (0) indicates the number of rows we want to move to the right. The third one (0) indicates the number of columns we want to move down. The fourth one (COUNTA(OBTAINRESULTS!$AK$7:$AK$5005)) indicates the number of rows that we have to return. In this case, as we are using the function COUNTA, this number will be the number of cells occupied from cell AK7 to cell AK5005. Finally the last value (1) indicates the number of columns to return. Finally, the tool informs about the feasibility of the solution. It could be that there is no feasible solution. It could happen for example if we reduce the length available along the station, because even if we kit everything for reducing the space needed at the border of the line, we could not have enough space. After experimenting many times with the tool, we have noticed that when a solution is unfeasible, we obtain that the cost of transporting kits is zero and, at the same time, the cost of picking from a kit is higher than zero, which is non-viable. In order to make that the work tool recognises unfeasible solutions and it can advise to the employee of this situation we have developed the formula presented in figure 4.3, which has the same meaning of the algorithm presented in figure 4.4. =IF('COMPANY PARAMETERS'!H15=" ";" ";IF(AG7>0;IF(AJ7>0;"feasible";"UNFEASIBLE!!");IF(AJ7=0;"feasible";"UNFEASIBLE!!"))) Figure 4.3: Formula for recognizing the feasibility of the results. Chapter 4: Company work tool 44 Algorithm – Recognizing the feasibility if all results are checked do Do not write anything else if cost to pick from a kit>0 do if cost to transport a kit>0 do Write “feasible” else Write “UNFEASIBLE!!” end if else if cost to transport a kit=0 do Write “feasible” else Write “UNFEASIBLE!!” end if end if end if Figure 4.4: Algorithm for recognizing the feasibility. Table 4.5: Matrix of the combinations of the values that the different parameters take according to the example of the table 4.2. 20 30 40 30 #1 #2 #3 40 #4 #5 #6 50 #7 #8 #9 OPERARI_COST (€/h) OPERARI_COST_LOGISTIC (€/h) Chapter 4: Company work tool 45 Table 4.6: Combinations of the parameter values according to the example given in table 4.2. 4.1.3.1 Macro for obtain parameter values combination In order to obtain the value of the parameters for all the possible combinations in the right order according to the lower, upper and step values given to each parameter, we have developed a macro named OBTAINCOMBINATIONS. To execute it we have to click the OBTAIN COMBINATIONS button. Next, we have to introduce in the window that appears, the number of results we are obtaining. The macro creates a table with 19 columns (each one corresponds to a parameter) and the same number of rows that number of results is going to be obtained. Each column is filled with integer numbers depending on two values: The number of times to repeat the integer and the number of integers to use. Figure 4.5 gives an example. #OC OCL wkDkDbτkτbulk BkLfact VbVpVkAbAkRbRpVfact Dpfact R3PL 130 20 30 1640 1640 1,08 1,08 5 1 2412 2880 2412 60 70 0,2 1,2 1 1 0 230 30 30 1640 1640 1,08 1,08 5 1 2412 2880 2412 60 70 0,2 1,2 1 1 0 330 40 30 1640 1640 1,08 1,08 5 1 2412 2880 2412 60 70 0,2 1,2 1 1 0 440 20 30 1640 1640 1,08 1,08 5 1 2412 2880 2412 60 70 0,2 1,2 1 1 0 540 30 30 1640 1640 1,08 1,08 5 1 2412 2880 2412 60 70 0,2 1,2 1 1 0 640 40 30 1640 1640 1,08 1,08 5 1 2412 2880 2412 60 70 0,2 1,2 1 1 0 750 20 30 1640 1640 1,08 1,08 5 1 2412 2880 2412 60 70 0,2 1,2 1 1 0 850 30 30 1640 1640 1,08 1,08 5 1 2412 2880 2412 60 70 0,2 1,2 1 1 0 950 40 30 1640 1640 1,08 1,08 5 1 2412 2880 2412 60 70 0,2 1,2 1 1 0 Parameter's values combinations Search Time Kit Search Time Bulk Batch Size Length Factor Number of result Operari Cost Operari Cost Logistic Maximum weight Milk Run Tour Kit Milk Run Tour Box Constant Replenish Pallet Volume Factor Distance Pallet Factor Constant Replenish 3PL Transport Velocity Box Transport Velocity Pallet Transport Velocity Kit Capacity MRT Box Capacity MRT Kit Constant Replenish Box Obtain Combinations Chapter 4: Company work tool 46 Number of times to repeat the integer: 2 Number of integers to use: 4 Sequence obtained in this column with the macro: 0, 0, 1, 1, 2, 2, 3, 3 Figure 4.5: Example of results obtained with the macro OBTAINCOMBINATIONS The sequence obtained with the macro is repeated as many times as needed in order to fill the number of rows needed. The number of times to repeat is obtained by multiplying the results obtained per parameter of the previous parameters (the ones that are in the right of the parameter being studied in table 4.5). The number of integers to use is the same as the number of results obtained per the parameter being studied. If for example, the OPERARI_COST has a lower value of 30, an upper value of 50 and a step value of 10. We will obtain results when the OPERARI COST is 30, 40 and 50 (3 results obtained per this parameter), so the number of integers to use it will be 3 (integers 0,1 and 2). As we have said, the macro gives integer numbers but does not gives the real values of the parameters. In order to obtain the real values of the parameters we have implemented a new function. An example is given in figure 4.6. It multiplies the values obtained with the macro per the step value and sums the lower value. Like this, in the first row of the table we will obtain the lower value plus zero times the step value. In the second one we will obtain the lower value plus one time the step value. In the third one we will obtain the lower value plus two times the step value. We will keep obtaining results until the row number becomes higher than the total number of results to obtain. Figure 4.7 shows the algorithm developed in order to obtain all the possible parameter’s values combinations. It represents the combination of the results obtained by the macro and the formula of figure 4.6. Chapter 4: Company work tool 47 =SI($H15=" ";" ";$A$7+AC17*$C$7) Figure 4.6: Formula for obtaining the parameter values from the values obtained with the macro Where H15 is the cell which indicates the number of the combination, A7 correspond to the cell of the lower value, AC17 is the value obtained in the macro and C7 is the cell which contains the step value. Chapter 4: Company work tool 48 Algorithm-Obtain all parameters combinations while there is new parameter do Write the lower parameter value if number of results ≤ #total results do if there is right parameter do while there is right parameter do if (#results right parameter>1) do if right parameter value ≥ above right parameter value do Write above parameter value else if (above parameter value + step parameter value) ≤ upper value do Write (above parameter value + step parameter value) else Write lower parameter value end if end if else Actualize right parameter value end if end while else if (above parameter value + step parameter value) ≤ upper value do Write (above parameter value + step parameter value) end if end if end if Actualize new parameter end while Figure 4.7: Algorithm for obtaining all the parameters combination Chapter 4: Company work tool 49 Table 4.7 is presented in order to understand the names defined in the algorithm. Table 4.7: Explication of how the table is filled Number (1) indicates where the algorithm starts and the arrows indicate the order to fill. If (2) is the cell being studied by the algorithm, then (3) is the above parameter value, number (4) is the right parameter value, (5) is the above right parameter value. If the right parameter value is actualized, then, (6) becomes the right parameter value and (7) becomes the above right parameter value. 4.1.3.2 Macro to obtain the results In order to obtain the results in columns we have developed a macro named OBTAINRESULTS. This macro places the results from the LIST OF RESULTS to the TABLE OF RESULTS. As each type of result is repeated every 23 cells the macro is ordered to actualize the row every 23 reads of values. It is activated with the OBTAIN #OC OCL wkDkDbτkτbulk BkLfact VbVpVkAbAkRbRpVfact Dpfact R3PL 11 2 3 43 5 7 52 4 6 6 7 8 9 Parameter's values combinations Search Time Kit Search Time Bulk Batch Size Length Factor Number of result Operari Cost Operari Cost Logistic Maximum weight Milk Run Tour Kit Milk Run Tour Box Constant Replenish Pallet Volume Factor Distance Pallet Factor Constant Replenish 3PL Transport Velocity Box Transport Velocity Pallet Transport Velocity Kit Capacity MRT Box Capacity MRT Kit Constant Replenish Box Obtain Combinations Chapter 4: Company work tool 50 RESULTS button. Figure 4.8 shows the algorithm developed in order to obtain the results in the table. Algorithm – Obtaining results in table Row := 1 Read first result While there are more results below to read do Column := 1 While Column ≤ 23 do Write result in Position (Row, Column) Column := Column + 1 End while Row := Row + 1 End while Figure 4.8: Algorithm for obtaining results in table 4.2 Back-end: Model files There are three files necessaries to execute de model: The model file, the data file and the run file. In order to obtain all the result in the way explained in the previous section and to obtain them in the excel file we have modified the files of the mathematical model. 4.2.1 Model file: Defining the parameters of the template The model file contains the mathematical model developed by dr. Veronique Limère which is constituted by the objective function, the constraints, the variables and the parameters. As we have created three new parameters for each of the parameters of the template (lower value, upper value and step value) we have defined them in the model file. Figure 4.9 shows an example. param OPERARI_COST_lower; param OPERARI_COST_upper; param OPERARI_COST_step; Figure 4.9: Definition of the three parameters created in the template. OPERARI COST Chapter 4: Company work tool 51 4.2.2 Data file: Reading the template To make possible that the model runs with using the values we have created a link between the template filled by the employee and the model programmed. First of all we have named all the small tables created for each parameter of the template with the name manager option of excel. Table 4.8 shows the table for the OPERARI COST. Table 4.8: Small table for the OPERARI COST named “OPERARI_COST”: Secondly, we have ordered to the data file to read all these tables. For it, we have first defined the table by mentioning it with the name given with the name manager of excel software in order it can be read. Figure 4.10 shows an example of how to a table and how to order to read it. table OPERARI_COST IN "ODBC" "COMPANY_TOOL.xls" "OPERARI_COST": [], OPERARI_COST_lower, OPERARI_COST_upper, OPERARI_COST_step; read table OPERARI_COST; Figure 4.10: Example of the definition of a table and the order to read it. OPERARI COST. As the run file receive the information from the data file and the model file, it is ready to obtain the results and write them in the excel file. OPERARI_COST_lower OPERARI_COST_upper OPERARI_COST_step 20 30 5 Chapter 4: Company work tool 52 4.2.3 Run file: Algorithm designed and writing the output In the run file we have first created sets for the 19 parameters of the template. Then, we have ordered them to takes as value all the values defined by the lower value, the upper value and the step value. Figure 4.11 shows an example. set OPERARI_COST; let OPERARI_COST := OPERARI_COST_lower..OPERARI_COST_upper by OPERARI_COST_step; Figure 4.11: Example of a creation of a set and the values that it takes. OPERARI COST. Next, we have developed a “for loop” for each of the parameters. Each loop has an auxiliary variable assigned and, at first, it takes the lower value of the parameter. After the loop has been run once the auxiliary variable value is actualized by adding the value of the step. If the auxiliary variable remains inside the interval defined by de lower and the upper value written in the excel file then, it lets to the parameter takes the value of the auxiliary variable. If not, this “for loop” is finished. In order to obtain all the results for all the possible combinations, each loop is inside another loop. Moreover, a conditional condition has been created in order to differentiate between if the company is making in-house kitting or outsourcing. If it is in-house kitting, the parameter that corresponds to the operator cost of the external company (OCL) is taking the value of the parameter corresponding to the cost of the operator working in the factory (OC). However, if the company is outsourcing, it takes a different value. Finally, we have ordered to the run file to display the results in the excel file. Figure 4.12 shows the algorithm developed. Chapter 6: Conclusions 59 one more part is being kitted. However, the number of kits used remains constant in 55 kits. Figure 5.2: Effect of the searching time in the supermarket in the percentage of parts kitted. The picking cost from kits in the assembly line increases when the searching time in the supermarket decreases due to the fact that more parts are kitted. At the same time, cost of pick from a bulk in the assembly line decreases because some parts that where in bulks are now in kits. Kitting cost tends to decrease while searching time in the supermarket is reduced. It seems logical because if searching time decreases means that the time to kit diminishes. However, the fact that more parts are kitted makes this cost increase. The sum of the two factors leads to a decrease of the kitting cost because searching times has more effect in the cost. As we can see in Figure 5.3 cost of kitting decreases when searching time in the supermarket becomes lower. However, we can appreciate that in some points of the graph, there is a change in the slope because a new part is being kitted. 0,554 0,555 0,556 0,557 0,558 0,559 0,56 0,561 135791113151719212325272931333537394143454749515355 % of Parts Kitted Chapter 6: Conclusions 60 Figure 5.3: Effect of the searching time in the supermarket on the cost of kitting. Kitting transport cost remains constant because the number of kits is the same. Either cost of replenishment of pallets or cost of replenishment of box varies every time there is a change in the percentage of parts kitted. When cost of replenishment of pallets increases, the cost of transport pallets to the line decreases because pallets that were transported to the line are now transported to the supermarket because that parts have now to be kitted and vice versa. The same happens with boxes. 5.1.2 In the supermarket and in the assembly line In this case we are simulating that new technologies are already being used in the supermarket and we want to analyse the impact of implementing them in the assembly line. For this reason, the searching time in the supermarket has been fixed in 0,54 seconds and searching time in the bulks of the assembly line goes from 0,54 seconds to 1,08 in steps of 0,01 seconds. Logically, the total cost decreases when we reduce the searching time (figure 5.4) because it reduces de cost to pick a part from a bulk. For this reason, the total cost of picking from a bulk tends to decrease when the searching time in bulks decreases. However, if there is a change in the number of parts kitted, then it could happen that the 47000 48000 49000 50000 51000 52000 53000 54000 1 3 5 7 9 1113151719212325272931333537394143454749515355 Cost Kitting Chapter 6: Conclusions 61 cost of picking from a bulk increases, as we can see in figure 5.5. We can notice that every time that it happens, there is a step in the percentage of kitting (figure 5.6). When the searching time change from 0,67 seconds to 0,66 seconds (point 14), the number of kits used diminishes in one (from 55 to 54). This fact leads to an important reduction in the percentage of parts kitted and, consequently the cost of pick from a kit at the line and the cost ok kitting diminishes, as less parts are kitted. For the same reason both constants of replenishment of the supermarket (pallets and boxes) also decreases in that point. Also because of the reduction in number of kits used, cost of transport kits diminishes and costs of transport pallets and boxes increase. Figure 5.4: Effect of the searching time at the line in the total cost. 360000 362000 364000 366000 368000 370000 372000 374000 376000 378000 380000 1 3 5 7 9 1113151719212325272931333537394143454749515355 Total Cost Chapter 6: Conclusions 62 Figure 5.5: Effect of the searching time at the line in the cost to pick from a bulk. Figure 5.6: Effect of the searching time at the line in the percentage of parts kitted. 5.2 Outsourcing We consider that for simulating an outsourcing situation with our model the labour cost of the operator working in the 3PL has to be lower. Moreover, searching time in the 92000 93000 94000 95000 96000 97000 98000 99000 100000 101000 102000 1 3 5 7 9 1113151719212325272931333537394143454749515355 Cost Pick Bulk 0,542 0,544 0,546 0,548 0,55 0,552 0,554 0,556 0,558 0,56 0,562 135791113151719212325272931333537394143454749515355 % of Parts Kitted Chapter 6: Conclusions 63 supermarket has also to be lower as the bests technologies are being used in the 3PL. Finally, we have to take into account the fact that parts needed to be kitted have to be transported from the factory to the 3PL and, once they are in kits, return them to the factory. It involves adding a new cost of external transport. However, as the supermarket has not to be replenished, costs of replenishment disappear. First of all, we will study how the reduction of the labour cost of an operator affects and then the impact of de distance between the 3PL and the factory. 5.2.1 Impact of the labour cost To simulate it we will study how the results varies if the value of the labour operator cost of the 3PL varies between 15 and 30 euros (this last value is the labour cost for an operator from the factory) with an interval between costs of 1€. Assuming that the 3PL, as a specialized in logistics, uses the best technologies (pick to voice, pick to light or pick to vision) we will half the value of the searching time in the supermarket (0.54 instead of 1.08 seconds). Logically, the value of the total cost decreases when the labour cost of the operator of 3PL becomes lower, as we can see in figure 5.7. Considering that the labour cost in the 3PL is 20 €, the final cost is reduced in 17368€ (from 377333€ to 359965€, which supposes a reduction of 4,6%. Kitting cost also decreases because the cost to kit a part is directly proportional to the labour cost of the 3PL operator. However, as we can see in Figure 5.8 it is not linear. It is explained with Figure 5.9 in which we can notice that percentage of kitting increases if the operator cost working in the 3PL diminishes due to the fact that is cheaper to kit parts, so it makes kitting cost decrease. The cost of pick from a kit increases all the time as there are more parts kitted when the labour cost diminishes. Contrary, the cost of pick from a bulk decreases. The cost to transport kits increases as the number of kits used also tends to increase during the entire interval. The cost of transport boxes tends to decrease as more parts are kitted, which leads to an increase of the cost of replenishment of boxes. In the case of pallets, it tends to increase while the labour cost decreases from 30 to 20€, but from that point, it decreases again. Chapter 6: Conclusions 64 Figure 5.7: Impact of the kitting labour cost of an operator in the total cost. Figure 5.8: Impact of the kitting labour cost of an operator in the kitting cost. 335000 340000 345000 350000 355000 360000 365000 370000 375000 380000 12345678910 11 12 13 14 15 16 Total Cost 0 10000 20000 30000 40000 50000 60000 12345678910 11 12 13 14 15 16 Cost Kitting Chapter 6: Conclusions 65 Figure 5.9: Impact of the kitting labour cost of an operator in the percentage of parts kitted. 5.2.2 Impact of the distance In order to take into account the external transport, a new cost has been defined. This cost varies according to a constant (R3PL), which represents the cost of transporting a part. The value of this cost depends on the velocity of the truck transporting the part, the labour cost of the operator driving the truck and de distance between the factory and the 3PL. Assuming that the labour cost is always the same and the truck always goes at the same velocity, the constant defined is directly proportional to the distance. To see the impact of the distance we have obtained the results given by the model for values of the constant between 0 and 1 in steps of 0,1. We suppose for an outsourcing situation a labour cost of an operator working in the 3PL of 20€/h and a searching time in the supermarket of 0.54s, as best technologies are being used. Moreover, both constants of replenishment of the supermarket (boxes and pallets) are zero because kits are not being made in the supermarket. When the distance of the external transport increases, the percentage of kitting decreases (figure 5.9) as it suppose more expensive to realize this transport and it 0,53 0,54 0,55 0,56 0,57 0,58 0,59 0,6 0,61 12345678910 11 12 13 14 15 16 % of Parts Kitted Chapter 6: Conclusions 66 discourages to kit. The total cost obviously increases (figure 5.10) but it is not linear because, as less parts are being kitted, the external transport costs affects to less parts. For this reason, as the distance increases, the cost difference is smaller. The picking cost from a kit decreases and the picking cost from a bulk increases also because of the reduction of parts kitted. For the same reason, the kitting cost also decreases as the distance becomes bigger. Costs of replenishments of the supermarket are zero because parts are carried directly from the warehouses to the 3PL. Instead of it, cost of replenishment of the 3PL appears, and as we can see in figure 5.13 it tends to increase with the distance but, as number of parts kitted decreases with the distance, it does not increase linearly. The cost of the internal transport of kits tends to increase as there are more kits to be transported. Cost of transport boxes decreases and cost of transport pallets increases, which probably means that most of parts now kitted come from boxes. Figure 5.10: Impact of the distance between the warehouse and the 3PL in the percentage of kitting. 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 1 2 3 4 5 6 7 8 9 10 11 % of Parts Kitted Chapter 6: Conclusions 67 Figure 5.11: Impact of the distance between the warehouse and the 3PL in the total cost. Figure 5.12: Impact of the distance between the warehouse and the 3PL in the kitting cost. 310000 320000 330000 340000 350000 360000 370000 380000 390000 400000 410000 12345678910 11 Total Cost 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 12345678910 11 Cost Kitting Chapter 6: Conclusions 68 Figure 5.13: Impact of the distance between the warehouse and the 3PL in the cost of replenish the 3PL. 5.2.3 Comparison with the initial situation If we compare outsourcing with in-house kitting, considering that parameters appropriated for each situations are those shown in table 5.3 and assuming that the 3PL company is really close from the factory (R3PL=0) we obtain the results shown in table 5.4. Parameter In-house kitting Outsourcing OCL 30 20 τk 1,08 0,54 Rb 0,2 0 Rp 1,2 0 Table 5.3: Parameter values for each method Result In-house kitting Outsourcing Total Cost 382271€ 341166€ % of kitting 55,7% 59,5% Table 5.4: Outsourcing vs. in-house kitting 0 5000 10000 15000 20000 25000 30000 12345678910 11 Cost Replenish 3PL Chapter 6: Conclusions 75 5.4 Kit batch size We have studied the variances in the costs if we reduce the batch size from 5 to 1 kits. Figure 5.23 shows that when the batch size is reduced the total cost increases 2328€. The percentage of kitting tends to decrease but it does not suppose a significant change (figure 5.24). As the percentage of kitting does not suffer significant changes the kitting cost increases almost as the same manner as total cost (figure 5.25). This also means that in this situation (with this specific parts and family parts) the opportunity for picking multiple units of a part at once is, in most of the times, affected for the batch size (restrictions 3.2 and 3.3 are satisfied). The other costs do not suffer important modifications. Figure 5.23: Impact of the kit batch size in the total cost. 381000 381500 382000 382500 383000 383500 384000 384500 385000 12345 Total Cost Chapter 6: Conclusions 76 Figure 5.24: Impact of the kit batch size in the percentage of parts kitted. Figure 5.25: Impact of the kit batch size in the kitting cost 0,542 0,544 0,546 0,548 0,55 0,552 0,554 0,556 0,558 1 2 3 4 5 % of Parts Kitted 52000 52500 53000 53500 54000 54500 55000 1 2 3 4 5 Cost Kitting Chapter 6: Conclusions 77 5.5 Available space at the border of the line As mentioned in chapter 3, we will study how the cost varies is if we vary the available length at the border of the line. Not all the space available is always used and we want to study how important it could be in the total cost to use more space. To study it we have obtained the results when the length factor varies from 1 to 2 in steps of 0.125. As the standard size is 8 meters (Ph.D. of dr. Veronique Limère), it means that we are obtaining the results in steps of 1 meter from 8 to 16 meters. As this change permits a bigger slack to the restriction (2.13), the total cost will always decrease. However, we can see in Figure 5.26 that, as the space available value becomes bigger, the impact in the cost is smaller. For this reason we have reduced the interval from 8 to 12 meters giving the results obtained every half meter. As it happens the same that in the first study (the impact in the cost is much more significant in the first meters), we have reduced again the interval from 8 to 10 giving the results every 0.25 meters (which corresponds with a lower value of 1 in the length factor, an upper value of 1.25 and a step value of 0.03125). We can see in figure 5.27 that if the available space at the border of the line is extended in 2 meters (from 8 meters to 10), we obtain a save of 11143€, which suppose a 2.9% of the total cost. Moreover, only extending half a meter the length (until 8.5 meters) we will also have an important save (5220€, that corresponds to a 1.36% of the total cost). The percentage of parts kitted tends to decrease (figure 5.28) as more space is available and more bulks can be allocated at the border of the line. As it, the number of kits used also decreases (figure 5.29). Chapter 6: Conclusions 78 Figure 5.26: Impact of the space available at the station in the total cost (from 8 to 16 meters). Figure 5.27: Impact of the space available at the station in the total cost (from 8 to 10 meters). 360000 365000 370000 375000 380000 385000 123456789 Total Cost 364000 366000 368000 370000 372000 374000 376000 378000 380000 382000 384000 123456789 Total Cost Chapter 6: Conclusions 79 Figure 5.28: Impact of the space available at the station in the percentage of parts kitted (from 8 to 10 meters) Figure 5.29: Impact of the space available at the station in the number of kits used (from 8 to 10 meters). 0,44 0,46 0,48 0,5 0,52 0,54 0,56 0,58 1 2 3 4 5 6 7 8 9 % of Parts Kitted 0 10 20 30 40 50 60 1 2 3 4 5 6 7 8 9 Number of Kits Chapter 6: Conclusions 80 5.6 Total volume of a kit We want to know how the volume of a kit affects to the costs and to the percentage of parts kitted. If not all the volume of the kits can be occupied, maybe there are some parts that do not fit in the kits, or maybe we cannot kit all the parts that we would like to kit because there is not enough space for all the kits at the border of the line. To see it, we have modified the volume factor. We have analysed the evolution of the results when the volume factor increases from 0.5 to 1 in steps of 0.1. This means that we will obtain the results for kit volume going from the 50% of the original volume to the 100% of the original volume, in steps of 10%. We observe in figure 5.30 that total cost increases every time we reduce the volume. If we half the volume of the kit, the cost increases in 47076€, which suppose a 12.31% of the total cost. The number of kits used increases when the kit volume decreases. However the percentage of parts kited decreases, maybe because it supposes a high spending of space at the border of the line. Both costs of replenish tends to decrease as less parts are kitted. Figure 5.30: Impact of the volume available in a kit in the total cost 350000 360000 370000 380000 390000 400000 410000 420000 430000 440000 123456 Total Cost Chapter 6: Conclusions 81 Figure 5.31: Impact of the volume available in a kit in the number of kits used Figure 5.32: Impact of the volume available in a kit in the percentage of parts kitted 0 10 20 30 40 50 60 70 80 1 2 3 4 5 6 Number of Kits 0,44 0,46 0,48 0,5 0,52 0,54 0,56 0,58 1 2 3 4 5 6 % of Parts Kitted Chapter 6: Conclusions 82 Chapter 6 Conclusions This thesis is a further research of the Ph.D. realized by Veronique Limère: “To Kit or Not to Kit: Optimizing Part Feeding in the Automotive Assembly industry”. On the one hand, this thesis deals with the development of a tool in order to facilitate and make more comfortable the work for the employees of a company using it. The work tool is created to complement the mathematical model realized in the Ph.D. mentioned before. For this reason it can only be used in factories where the mathematical model is implemented. This model supports the best choice between two supply systems: bulk feeding and kitting. On the other hand, in this thesis we have considered the possibility that factories can suffer some modifications in the way they supply the materials. We have used the tool to analyse the differences that these modifications can lead. Answering to the first point, the tool developed satisfies all the requisites needed: First of all we have to mention the fact that it has been developed in an excel file. Excel is one of the most used programs in companies. Therefore it the makes work of the Chapter 6: Conclusions 83 employee more comfortable. Moreover the fact that the employee using the tool only has to fill a template to obtain the results makes it even more comfortable. Secondly, as it has been required, it allows us to study how the costs and the number of parts kitted varies when one or more parameters also varies between two values (lower value and upper value) with an interval between two consecutives values (step value). It let us now the tendency that results follows. Finally, it gives us the results in a sheet and also in graphs, which helps the employee to analyse the results. Moreover, it informs about the feasibility of the solution. In order to answer the second point, we have used the tool to realize a study with the data of a particular company. If all the others parameters keep the original value, we have concluded that: If we are using a new picking technology in the supermarket, for every 0.1 seconds that the searching time is reduced, we have a saving of 0.24% of the total cost, and the percentage of parts kitted tends to increase while we reduce the searching time. The reduction of the labour cost for kitting operations if the company is outsourcing (from 30€ to 20€), reduces de total cost in a 4.6%. Moreover, if the distance between the factory and the 3PL increases, the total cost tends to increase, and the percentage of parts kitted tends to decrease. Increasing the forklift velocity diminishes the total cost in 15.312€ and it is directly proportional to the velocity, as the time that the operator spends on transporting the pallets is lower. However, it does not have a significant repercussion in the number of parts kitted. The same happens if we increase the velocity of the tugger train transporting kits. Reducing the batch size from 5 kits to 1 kit does not lead to a significant change in the percentage of parts kitted. Moreover, the reduction of the total cost only changes in 2328€. Chapter 6: Conclusions 84 Increasing the available length along the workstation reduces the total cost, as it is a restrictive factor. However, for every half meter it increases, the reduction that the total cost suffers is less. Increasing the length available from 8 to 8.5 will suppose a reduction of a 1.26% of the total cost. On the other hand, the percentage of parts kitted, tends to decrease in a considerable value if the space available increases. The total cost increases every time the kit volume is reduced. If we half the volume of the kit, the total cost increases in a 12,31%. The percentage of parts kitted diminishes in a significant way (from55.66% to 48.27%).