Cost Control - Revision history

Morning at 20:07, 4 March 2019

2019-03-04T20:07:25Z

← Older revision		Revision as of 20:07, 4 March 2019
Line 19:		Line 19:

	=='' Data mining and OLAP general idea ''==		=='' Data mining and OLAP general idea ''==
	Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure 3. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way. [[File:cube.jpg\|300px\|thumb\|right\|Figure 3: A data cube from Lu Zhao's article <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref>.. This is a ~~multidimentional~~ array of data points.]]		Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure 3. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way. [[File:cube.jpg\|300px\|thumb\|right\|Figure 3: A data cube from Lu Zhao's article <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref>. This is a multidimensional array of data points.]]

	In figure 4 the whole structure of the project cost management system (PCMS) is visualized. From below, the cost data from previous experiences are collected and stored in the cost data warehouse. Then from the data warehouse, it can be extracted for analysis (OLAP), modeling, data mining and aid decision making.[[File:PCMS.jpg\|500px\|thumb\|center\|Figure 4: Project Cost Management System. From Lu Zhao's article <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref>. ]]		In figure 4 the whole structure of the project cost management system (PCMS) is visualized. From below, the cost data from previous experiences are collected and stored in the cost data warehouse. Then from the data warehouse, it can be extracted for analysis (OLAP), modeling, data mining and aid decision making.[[File:PCMS.jpg\|500px\|thumb\|center\|Figure 4: Project Cost Management System. From Lu Zhao's article <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref>. ]]
Line 37:		Line 37:
	As visualization is just one sort of algorithm build on top of the PCMS, so one can add on more algorithmic tools, such as decision-making models. Amongst all the knowledge stored in the database, you can save and use predefined algorithms for making for example regression over data, trends and more. So you can also store previous reactions and decisions in the database. If for example one particular model or trend calculated from the data prompted the project manager to make a decision, this can and should also be stored systematically in the database, so the future aggregation of data can recognize the pattern and suggest a catalog of previously tested reactions to this sort of situation.		As visualization is just one sort of algorithm build on top of the PCMS, so one can add on more algorithmic tools, such as decision-making models. Amongst all the knowledge stored in the database, you can save and use predefined algorithms for making for example regression over data, trends and more. So you can also store previous reactions and decisions in the database. If for example one particular model or trend calculated from the data prompted the project manager to make a decision, this can and should also be stored systematically in the database, so the future aggregation of data can recognize the pattern and suggest a catalog of previously tested reactions to this sort of situation.
	Early warning systems are also a possibility with this way of working with project data. With predefined and built-in automatic frequent aggregation of the project cost data, forecasting is possible to visualize and can be set to evaluate the actual and forecasted costs to the estimated and budgeted costs. In cases where there is a risk of overrun of the budget, it can be automatically spotted and dealt with earlier in the project, before the overrun has happened.		Early warning systems are also a possibility with this way of working with project data. With predefined and built-in automatic frequent aggregation of the project cost data, forecasting is possible to visualize and can be set to evaluate the actual and forecasted costs to the estimated and budgeted costs. In cases where there is a risk of overrun of the budget, it can be automatically spotted and dealt with earlier in the project, before the overrun has happened.
	In figure ~~XXXX~~ such a visualization is exemplified from the article of Lu Zhao ~~REF REF~~. [[File:overrun.jpg\|400px\|thumb\|center\|~~alt text~~]]		In figure 6 such a visualization is exemplified from the article of Lu Zhao<ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> . [[File:overrun.jpg\|400px\|thumb\|center\|Figure 6: Illustration of how the OLAP spots an expected cost overrun before it happens <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref>. ]]


Line 61:		Line 61:
	Location. Also has a significant impact on price ranges within construction projects		Location. Also has a significant impact on price ranges within construction projects
	Promoter. Was there a promotor or project sponsor who finances the project, and was it private or public promotion?		Promoter. Was there a promotor or project sponsor who finances the project, and was it private or public promotion?
	~~Tye~~ of construction. Of course, this has huge implications on the cost of the project, so It is a relevant parameter to sort the data by for comparison		Type of construction. Of course, this has huge implications on the cost of the project, so it is a relevant parameter to sort the data by for comparison
	Company. Sorted by the size of the company, number of employees.		Company. Sorted by the size of the company, number of employees.
	Task. WBS at different levels.		Task. WBS at different levels.
Line 72:		Line 72:


	Query 1: “Average benefit of detached projects depending on the promotor typology”		Query 1: “Average benefit of detached projects depending on the promotor typology”.
	[[File:q1.jpg\|300px\|thumb\|center\|~~alt text~~]]		[[File:q1.jpg\|300px\|thumb\|center\|Figure 7: Query 1 gave this output, that illustrates the average benefit of detached projects depending on the promotor typology <ref name="Rojas">M. Mart´ınez-Rojas, Cost Analysis in Construction Projects using Fuzzy
			OLAP Cubes, Ieee International Conference on Fuzzy Systems, 2015. </ref>.]]

	Query 2: “Average benefit in the beginning phase according to the company size”		Query 2: “Average benefit in the beginning phase according to the company size”.
	[[File:q2.jpg\|300px\|thumb\|center\|~~alt text~~]]		[[File:q2.jpg\|300px\|thumb\|center\|Query 2 gave this output, that illustrates average benefit in the beginning phase according to the company size <ref name="Rojas">M. Mart´ınez-Rojas, Cost Analysis in Construction Projects using Fuzzy
			OLAP Cubes, Ieee International Conference on Fuzzy Systems, 2015. </ref>.]]

	Query 3: ~~“Average benefit~~ in ~~the beginning~~ phase ~~according to the company size”~~		Query 3: “Maximum cost deviation in each phase for each residential project typology”.
	[[File:q3.jpg\|300px\|thumb\|center\|~~alt text~~]]		[[File:q3.jpg\|300px\|thumb\|center\|Query 3 gave this output, that illustrates the maximum cost deviation in each phase for each residential project typology <ref name="Rojas">M. Mart´ınez-Rojas, Cost Analysis in Construction Projects using Fuzzy OLAP Cubes, Ieee International Conference on Fuzzy Systems, 2015. </ref>.]]

	Query 4: “Maximum cost deviation in big-sized projects according to the project phases”		Query 4: “Maximum cost deviation in big-sized projects according to the project phases”.
	[[File:q4.jpg\|300px\|thumb\|center\|~~alt text~~]]		[[File:q4.jpg\|300px\|thumb\|center\|Query 4 gave this output, that illustrates the maximum cost deviation in big-sized projects according to the project phases<ref name="Rojas">M. Mart´ınez-Rojas, Cost Analysis in Construction Projects using Fuzzy OLAP Cubes, Ieee International Conference on Fuzzy Systems, 2015. </ref>.]]

	Query 5: “Average cost deviation in big-sized projects according to the project chapters”.		Query 5: “Average cost deviation in big-sized projects according to the project chapters”.
	[[File:q5.jpg\|300px\|thumb\|center\|~~alt text~~]]		[[File:q5.jpg\|300px\|thumb\|center\|Query 4 gave this output, that illustrates the average cost deviation in big-sized projects according to the project chapters <ref name="Rojas">M. Mart´ınez-Rojas, Cost Analysis in Construction Projects using Fuzzy OLAP Cubes, Ieee International Conference on Fuzzy Systems, 2015. </ref>.]]

Morning at 19:55, 4 March 2019

2019-03-04T19:55:52Z

← Older revision		Revision as of 19:55, 4 March 2019
Line 26:		Line 26:

	== Collecting the data cubes ==		== Collecting the data cubes ==
	One of the most important aspects of the data cubes are their multidimensional structure, and therefore to make them as useful as possible, they should be established in a consistent manner, meaning that as far as possible, they should have the same input variables. In the example figure 5 of the data cube, see fig 5, a few examples of parameters are shown, but in reality, one would like to add as many relevant parameters as possible. This is done to enable comparison of parameters between phases of a project or over several projects where the same parameters are relevant to compare over. In figure 5 an example of a data cube with parameters relevant to construction projects is shown with seven different branches, all with several subparameters is shows. Such a level of detail gives the user a myriad of possibilities to aggregate and visualize data.[[File:fuzzycube.jpg\|400px\|thumb\|center\|Figure 5: An example of a data cube]]		One of the most important aspects of the data cubes are their multidimensional structure, and therefore to make them as useful as possible, they should be established in a consistent manner, meaning that as far as possible, they should have the same input variables. In the example figure 5 of the data cube, see fig 5, a few examples of parameters are shown, but in reality, one would like to add as many relevant parameters as possible. This is done to enable comparison of parameters between phases of a project or over several projects where the same parameters are relevant to compare over. In figure 5 an example of a data cube with parameters relevant to construction projects is shown with seven different branches, all with several subparameters is shows. Such a level of detail gives the user a myriad of possibilities to aggregate and visualize data.[[File:fuzzycube.jpg\|400px\|thumb\|center\|Figure 5: An example of a data cube from M. Martınez-Rojas's article <ref name="Rojas">M. Mart´ınez-Rojas, Cost Analysis in Construction Projects using Fuzzy
			OLAP Cubes, Ieee International Conference on Fuzzy Systems , 2015. </ref>.]]

	So the first task at hand when deciding to use data mining and OLAP is to establish an overview of all the relevant parameters you want to collect data from. Especially if one is working in the same field over several projects or if it is just a single big project, the data mining and the analysis done from it can start to provide the benefits from this method.		So the first task at hand when deciding to use data mining and OLAP is to establish an overview of all the relevant parameters you want to collect data from. Especially if one is working in the same field over several projects or if it is just a single big project, the data mining and the analysis done from it can start to provide the benefits from this method.

Morning at 19:52, 4 March 2019

2019-03-04T19:52:50Z

← Older revision		Revision as of 19:52, 4 March 2019
Line 13:		Line 13:

	Would it not be nice, if we could just do projects all day and never have to worry about the cost? Such thoughts have no place in the real world, where projects success and performance is measured directly by how much it has cost compared to how much was budgeted to cost. If you send your hungry project manager to McDonalds to get the team for 80 kr. of junk food per person and he returns having spent 125 kr. per person, it obviously will be regarded as a failure, disregarding the fact that you had the food delivered to your desk and you all have Sundaes and Tops. The same thing goes with projects, where the making or breaking of a project budget can determine whether or not you will be reassigned to such projects again.		Would it not be nice, if we could just do projects all day and never have to worry about the cost? Such thoughts have no place in the real world, where projects success and performance is measured directly by how much it has cost compared to how much was budgeted to cost. If you send your hungry project manager to McDonalds to get the team for 80 kr. of junk food per person and he returns having spent 125 kr. per person, it obviously will be regarded as a failure, disregarding the fact that you had the food delivered to your desk and you all have Sundaes and Tops. The same thing goes with projects, where the making or breaking of a project budget can determine whether or not you will be reassigned to such projects again.
	For projects and management of cost control, it can be a lot more complex, as the cost of the project is tightly bound to almost every other aspect of the project. To illustrate this, the ~~“IRON TRIANGLE REF”~~ is often used, as it shows the three dominant constraints of projects, time, scope and cost as being interrelated to one another, meaning, that if you alter one of them, it will unavoidably also affect the other two. If it takes an extra two months to implement the new software or build a bridge, the project still has to keep paying its workers for that duration.[[File:IronTriangle.png\|300px\|thumb\|right\|~~alt text~~]]		For projects and management of cost control, it can be a lot more complex, as the cost of the project is tightly bound to almost every other aspect of the project. To illustrate this, the Iron Triangle, see figure 1, is often used, as it shows the three dominant constraints of projects, time, scope and cost as being interrelated to one another, meaning, that if you alter one of them, it will unavoidably also affect the other two. If it takes an extra two months to implement the new software or build a bridge, the project still has to keep paying its workers for that duration.[[File:IronTriangle.png\|300px\|thumb\|right\|Figure 1: The Iron Triangle illustrates three central aspects of a project, cost, time schedule, and scope. It is generally assumed that altering one of these affects the other two.]]
	Therefore, to successfully control the cost of the project, measures and procedures can be applied to provide realistic estimates of the budget and track the performance of the project and its cost. These cost control and assessing measures rely on details from other tools commonly used in project management, such as the project charter, WBS, enterprise environmental factors, agreements and many more. According to the Project Management Body of Knowledge (PMBOK), there are four processes to incorporate to get the best reliable overview and control of costs in a project. These are Plan Cost Management, Estimate Costs, Determine Budget and Control Costs, and a more elaborated overview of these can be seen in figure \ref “Project cost management overview (s 268 PMBOK)”<ref name="PMBOK">Project Management Institute, A guide to the project management body of knowledge (PMBOK guide), Project Management Institute, Inc., 2017. </ref>. In summary, they can be recapped as an encouragement to have a well defined, detailed plan of the project, using this to correctly estimate costs and hence determine the actual budget as more details and knowledge is attained and lastly to measure the progress of work and costs throughout the duration of the project. This PMBOK approach relies on, amongst others, two distinct tools used in every step, namely expert judgment and data analysis. As valuable as expert judgment can be, it is not just a bit vaguely described and can be difficult to quickly attain if you have little experience from other similar projects. It also relies on amongst others, previous experiences from similar projects. This is exactly where the data mining and OLAP compliments the existing methods by giving a structured approach on how to gather data from previous experiences and put them use from current and future projects.		Therefore, to successfully control the cost of the project, measures and procedures can be applied to provide realistic estimates of the budget and track the performance of the project and its cost. These cost control and assessing measures rely on details from other tools commonly used in project management, such as the project charter, WBS, enterprise environmental factors, agreements and many more. According to the Project Management Body of Knowledge (PMBOK), there are four processes to incorporate to get the best reliable overview and control of costs in a project. These are Plan Cost Management, Estimate Costs, Determine Budget and Control Costs, and a more elaborated overview of these can be seen in figure 2 \ref “Project cost management overview (s 268 PMBOK)”<ref name="PMBOK">Project Management Institute, A guide to the project management body of knowledge (PMBOK guide), Project Management Institute, Inc., 2017. </ref>. In summary, they can be recapped as an encouragement to have a well defined, detailed plan of the project, using this to correctly estimate costs and hence determine the actual budget as more details and knowledge is attained and lastly to measure the progress of work and costs throughout the duration of the project. This PMBOK approach relies on, amongst others, two distinct tools used in every step, namely expert judgment and data analysis. As valuable as expert judgment can be, it is not just a bit vaguely described and can be difficult to quickly attain if you have little experience from other similar projects. It also relies on amongst others, previous experiences from similar projects. This is exactly where the data mining and OLAP compliments the existing methods by giving a structured approach on how to gather data from previous experiences and put them use from current and future projects.
	[[File:PMBOKcost.jpg\|300px\|thumb\|right\|~~alt text~~]]		[[File:PMBOKcost.jpg\|300px\|thumb\|right\|Figure 2: Project cost management overview from the Project Management Body Of Knowledge.]]


	=='' Data mining and OLAP general idea ''==		=='' Data mining and OLAP general idea ''==
	Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure XX. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way. [[File:cube.jpg\|300px\|thumb\|right\|~~alt text~~]]		Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure 3. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way. [[File:cube.jpg\|300px\|thumb\|right\|Figure 3: A data cube from Lu Zhao's article <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref>.. This is a multidimentional array of data points.]]

	In figure XX the whole structure of the project cost management system (PCMS) is visualized. From below, the cost data from previous experiences are collected and stored in the cost data warehouse. Then from the data warehouse, it can be extracted for analysis (OLAP), modeling, data mining and aid decision making.[[File:PCMS.jpg\|500px\|thumb\|center\|~~alt text~~]]		In figure 4 the whole structure of the project cost management system (PCMS) is visualized. From below, the cost data from previous experiences are collected and stored in the cost data warehouse. Then from the data warehouse, it can be extracted for analysis (OLAP), modeling, data mining and aid decision making.[[File:PCMS.jpg\|500px\|thumb\|center\|Figure 4: Project Cost Management System. From Lu Zhao's article <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref>. ]]



	== Collecting the data cubes ==		== Collecting the data cubes ==
	One of the most important aspects of the data cubes are their multidimensional structure, and therefore to make them as useful as possible, they should be established in a consistent manner, meaning that as far as possible, they should have the same input variables. In the example figure of the data cube, see fig ~~XXX~~, a few examples of parameters are shown, but in reality, one would like to add as many relevant parameters as possible. This is done to enable comparison of parameters between phases of a project or over several projects where the same parameters are relevant to compare over. In figure ~~XXX~~ an example of a data cube with parameters relevant to construction projects is shown with seven different branches, all with several subparameters is shows. Such a level of detail gives the user a myriad of possibilities to aggregate and visualize data.[[File:fuzzycube.jpg\|400px\|thumb\|center\|~~alt text~~]]		One of the most important aspects of the data cubes are their multidimensional structure, and therefore to make them as useful as possible, they should be established in a consistent manner, meaning that as far as possible, they should have the same input variables. In the example figure 5 of the data cube, see fig 5, a few examples of parameters are shown, but in reality, one would like to add as many relevant parameters as possible. This is done to enable comparison of parameters between phases of a project or over several projects where the same parameters are relevant to compare over. In figure 5 an example of a data cube with parameters relevant to construction projects is shown with seven different branches, all with several subparameters is shows. Such a level of detail gives the user a myriad of possibilities to aggregate and visualize data.[[File:fuzzycube.jpg\|400px\|thumb\|center\|Figure 5: An example of a data cube]]

	So the first task at hand when deciding to use data mining and OLAP is to establish an overview of all the relevant parameters you want to collect data from. Especially if one is working in the same field over several projects or if it is just a single big project, the data mining and the analysis done from it can start to provide the benefits from this method.		So the first task at hand when deciding to use data mining and OLAP is to establish an overview of all the relevant parameters you want to collect data from. Especially if one is working in the same field over several projects or if it is just a single big project, the data mining and the analysis done from it can start to provide the benefits from this method.

Morning at 19:42, 4 March 2019

2019-03-04T19:42:44Z

← Older revision		Revision as of 19:42, 4 March 2019
Line 21:		Line 21:
	Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure XX. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way. [[File:cube.jpg\|300px\|thumb\|right\|alt text]]		Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure XX. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way. [[File:cube.jpg\|300px\|thumb\|right\|alt text]]

	In figure XX the whole structure of the project cost management system (PCMS) is visualized. From below, the cost data from previous experiences are collected and stored in the cost data warehouse. Then from the data warehouse, it can be extracted for analysis (OLAP), modeling, data mining and aid decision making.[[File:PCMS.jpg\|~~300px~~\|thumb\|~~right~~\|alt text]]		In figure XX the whole structure of the project cost management system (PCMS) is visualized. From below, the cost data from previous experiences are collected and stored in the cost data warehouse. Then from the data warehouse, it can be extracted for analysis (OLAP), modeling, data mining and aid decision making.[[File:PCMS.jpg\|500px\|thumb\|center\|alt text]]



	== Collecting the data cubes ==		== Collecting the data cubes ==
	One of the most important aspects of the data cubes are their multidimensional structure, and therefore to make them as useful as possible, they should be established in a consistent manner, meaning that as far as possible, they should have the same input variables. In the example figure of the data cube, see fig XXX, a few examples of parameters are shown, but in reality, one would like to add as many relevant parameters as possible. This is done to enable comparison of parameters between phases of a project or over several projects where the same parameters are relevant to compare over. In figure XXX an example of a data cube with parameters relevant to construction projects is shown with seven different branches, all with several subparameters is shows. Such a level of detail gives the user a myriad of possibilities to aggregate and visualize data.[[File:fuzzycube.jpg\|~~300px~~\|thumb\|~~right~~\|alt text]]		One of the most important aspects of the data cubes are their multidimensional structure, and therefore to make them as useful as possible, they should be established in a consistent manner, meaning that as far as possible, they should have the same input variables. In the example figure of the data cube, see fig XXX, a few examples of parameters are shown, but in reality, one would like to add as many relevant parameters as possible. This is done to enable comparison of parameters between phases of a project or over several projects where the same parameters are relevant to compare over. In figure XXX an example of a data cube with parameters relevant to construction projects is shown with seven different branches, all with several subparameters is shows. Such a level of detail gives the user a myriad of possibilities to aggregate and visualize data.[[File:fuzzycube.jpg\|400px\|thumb\|center\|alt text]]

	So the first task at hand when deciding to use data mining and OLAP is to establish an overview of all the relevant parameters you want to collect data from. Especially if one is working in the same field over several projects or if it is just a single big project, the data mining and the analysis done from it can start to provide the benefits from this method.		So the first task at hand when deciding to use data mining and OLAP is to establish an overview of all the relevant parameters you want to collect data from. Especially if one is working in the same field over several projects or if it is just a single big project, the data mining and the analysis done from it can start to provide the benefits from this method.
Line 35:		Line 35:
	Visualizing data gives new insights. As a project manager you always have too much, too little or inconsistent data, and from there it can be a tough challenge to know how to look at all this data. With the multidimensional but consistent data gathered from previous experiences, you still have a lot of data, but when knowing that it is consistent, you can easily sieve through the parameters and analyze them from so many ways. On top of that, this also allows you to visualize the data exactly in the relevant way to explore new aspects of cost development, and see interdependencies and trends, that would have been impossible or extremely complicated with less systematically collected data.		Visualizing data gives new insights. As a project manager you always have too much, too little or inconsistent data, and from there it can be a tough challenge to know how to look at all this data. With the multidimensional but consistent data gathered from previous experiences, you still have a lot of data, but when knowing that it is consistent, you can easily sieve through the parameters and analyze them from so many ways. On top of that, this also allows you to visualize the data exactly in the relevant way to explore new aspects of cost development, and see interdependencies and trends, that would have been impossible or extremely complicated with less systematically collected data.
	As visualization is just one sort of algorithm build on top of the PCMS, so one can add on more algorithmic tools, such as decision-making models. Amongst all the knowledge stored in the database, you can save and use predefined algorithms for making for example regression over data, trends and more. So you can also store previous reactions and decisions in the database. If for example one particular model or trend calculated from the data prompted the project manager to make a decision, this can and should also be stored systematically in the database, so the future aggregation of data can recognize the pattern and suggest a catalog of previously tested reactions to this sort of situation.		As visualization is just one sort of algorithm build on top of the PCMS, so one can add on more algorithmic tools, such as decision-making models. Amongst all the knowledge stored in the database, you can save and use predefined algorithms for making for example regression over data, trends and more. So you can also store previous reactions and decisions in the database. If for example one particular model or trend calculated from the data prompted the project manager to make a decision, this can and should also be stored systematically in the database, so the future aggregation of data can recognize the pattern and suggest a catalog of previously tested reactions to this sort of situation.
	Early warning systems are also a possibility with this way of working with project data. With predefined and built in automatic frequent aggregation of the project cost data, forecasting is possible to visualize and can be set to evaluate the actual and forecasted costs to the estimated and budgeted costs. In cases where there is a risk of overrun of the budget, it can be automatically spotted and dealt with earlier in the project, before the overrun has happened.		Early warning systems are also a possibility with this way of working with project data. With predefined and built-in automatic frequent aggregation of the project cost data, forecasting is possible to visualize and can be set to evaluate the actual and forecasted costs to the estimated and budgeted costs. In cases where there is a risk of overrun of the budget, it can be automatically spotted and dealt with earlier in the project, before the overrun has happened.
	In figure XXXX such a visualization is ~~examplified~~ from the article of Lu Zhao REF REF. [[File:overrun.jpg\|~~300px~~\|thumb\|~~right~~\|alt text]]		In figure XXXX such a visualization is exemplified from the article of Lu Zhao REF REF. [[File:overrun.jpg\|400px\|thumb\|center\|alt text]]


Line 72:		Line 72:

	Query 1: “Average benefit of detached projects depending on the promotor typology”		Query 1: “Average benefit of detached projects depending on the promotor typology”
	[[File:q1.jpg\|300px\|thumb\|~~right~~\|alt text]]		[[File:q1.jpg\|300px\|thumb\|center\|alt text]]

	Query 2: “Average benefit in the beginning phase according to the company size”		Query 2: “Average benefit in the beginning phase according to the company size”
	[[File:q2.jpg\|300px\|thumb\|~~right~~\|alt text]]		[[File:q2.jpg\|300px\|thumb\|center\|alt text]]

	Query 3: “Average benefit in the beginning phase according to the company size”		Query 3: “Average benefit in the beginning phase according to the company size”
	[[File:q3.jpg\|300px\|thumb\|~~right~~\|alt text]]		[[File:q3.jpg\|300px\|thumb\|center\|alt text]]

	Query 4: “Maximum cost deviation in big-sized projects according to the project phases”		Query 4: “Maximum cost deviation in big-sized projects according to the project phases”
	[[File:q4.jpg\|300px\|thumb\|~~right~~\|alt text]]		[[File:q4.jpg\|300px\|thumb\|center\|alt text]]

	Query 5: “Average cost deviation in big-sized projects according to the project chapters”.		Query 5: “Average cost deviation in big-sized projects according to the project chapters”.
	[[File:q5.jpg\|300px\|thumb\|~~right~~\|alt text]]		[[File:q5.jpg\|300px\|thumb\|center\|alt text]]

Morning at 19:31, 4 March 2019

2019-03-04T19:31:33Z

Morning at 19:30, 4 March 2019

2019-03-04T19:30:07Z

← Older revision		Revision as of 19:30, 4 March 2019
Line 15:		Line 15:
	For projects and management of cost control, it can be a lot more complex, as the cost of the project is tightly bound to almost every other aspect of the project. To illustrate this, the “IRON TRIANGLE REF” is often used, as it shows the three dominant constraints of projects, time, scope and cost as being interrelated to one another, meaning, that if you alter one of them, it will unavoidably also affect the other two. If it takes an extra two months to implement the new software or build a bridge, the project still has to keep paying its workers for that duration.[[File:IronTriangle.png\|300px\|thumb\|right\|alt text]]		For projects and management of cost control, it can be a lot more complex, as the cost of the project is tightly bound to almost every other aspect of the project. To illustrate this, the “IRON TRIANGLE REF” is often used, as it shows the three dominant constraints of projects, time, scope and cost as being interrelated to one another, meaning, that if you alter one of them, it will unavoidably also affect the other two. If it takes an extra two months to implement the new software or build a bridge, the project still has to keep paying its workers for that duration.[[File:IronTriangle.png\|300px\|thumb\|right\|alt text]]
	Therefore, to successfully control the cost of the project, measures and procedures can be applied to provide realistic estimates of the budget and track the performance of the project and its cost. These cost control and assessing measures rely on details from other tools commonly used in project management, such as the project charter, WBS, enterprise environmental factors, agreements and many more. According to the Project Management Body of Knowledge (PMBOK), there are four processes to incorporate to get the best reliable overview and control of costs in a project. These are Plan Cost Management, Estimate Costs, Determine Budget and Control Costs, and a more elaborated overview of these can be seen in figure \ref “Project cost management overview (s 268 PMBOK)”<ref name="PMBOK">Project Management Institute, A guide to the project management body of knowledge (PMBOK guide), Project Management Institute, Inc., 2017. </ref>. In summary, they can be recapped as an encouragement to have a well defined, detailed plan of the project, using this to correctly estimate costs and hence determine the actual budget as more details and knowledge is attained and lastly to measure the progress of work and costs throughout the duration of the project. This PMBOK approach relies on, amongst others, two distinct tools used in every step, namely expert judgment and data analysis. As valuable as expert judgment can be, it is not just a bit vaguely described and can be difficult to quickly attain if you have little experience from other similar projects. It also relies on amongst others, previous experiences from similar projects. This is exactly where the data mining and OLAP compliments the existing methods by giving a structured approach on how to gather data from previous experiences and put them use from current and future projects.		Therefore, to successfully control the cost of the project, measures and procedures can be applied to provide realistic estimates of the budget and track the performance of the project and its cost. These cost control and assessing measures rely on details from other tools commonly used in project management, such as the project charter, WBS, enterprise environmental factors, agreements and many more. According to the Project Management Body of Knowledge (PMBOK), there are four processes to incorporate to get the best reliable overview and control of costs in a project. These are Plan Cost Management, Estimate Costs, Determine Budget and Control Costs, and a more elaborated overview of these can be seen in figure \ref “Project cost management overview (s 268 PMBOK)”<ref name="PMBOK">Project Management Institute, A guide to the project management body of knowledge (PMBOK guide), Project Management Institute, Inc., 2017. </ref>. In summary, they can be recapped as an encouragement to have a well defined, detailed plan of the project, using this to correctly estimate costs and hence determine the actual budget as more details and knowledge is attained and lastly to measure the progress of work and costs throughout the duration of the project. This PMBOK approach relies on, amongst others, two distinct tools used in every step, namely expert judgment and data analysis. As valuable as expert judgment can be, it is not just a bit vaguely described and can be difficult to quickly attain if you have little experience from other similar projects. It also relies on amongst others, previous experiences from similar projects. This is exactly where the data mining and OLAP compliments the existing methods by giving a structured approach on how to gather data from previous experiences and put them use from current and future projects.
	[[File:~~PMBOK~~.png\|300px\|thumb\|right\|alt text]]		[[File:PMBOKcost.png\|300px\|thumb\|right\|alt text]]

Morning at 19:26, 4 March 2019

2019-03-04T19:26:37Z

← Older revision		Revision as of 19:26, 4 March 2019
Line 13:		Line 13:

	Would it not be nice, if we could just do projects all day and never have to worry about the cost? Such thoughts have no place in the real world, where projects success and performance is measured directly by how much it has cost compared to how much was budgeted to cost. If you send your hungry project manager to McDonalds to get the team for 80 kr. of junk food per person and he returns having spent 125 kr. per person, it obviously will be regarded as a failure, disregarding the fact that you had the food delivered to your desk and you all have Sundaes and Tops. The same thing goes with projects, where the making or breaking of a project budget can determine whether or not you will be reassigned to such projects again.		Would it not be nice, if we could just do projects all day and never have to worry about the cost? Such thoughts have no place in the real world, where projects success and performance is measured directly by how much it has cost compared to how much was budgeted to cost. If you send your hungry project manager to McDonalds to get the team for 80 kr. of junk food per person and he returns having spent 125 kr. per person, it obviously will be regarded as a failure, disregarding the fact that you had the food delivered to your desk and you all have Sundaes and Tops. The same thing goes with projects, where the making or breaking of a project budget can determine whether or not you will be reassigned to such projects again.
	For projects and management of cost control, it can be a lot more complex, as the cost of the project is tightly bound to almost every other aspect of the project. To illustrate this, the “IRON TRIANGLE REF” is often used, as it shows the three dominant constraints of projects, time, scope and cost as being interrelated to one another, meaning, that if you alter one of them, it will unavoidably also affect the other two. If it takes an extra two months to implement the new software or build a bridge, the project still has to keep paying its workers for that duration.[[File:IronTriangle.png\|~~200px~~\|thumb\|~~left~~\|alt text]]		For projects and management of cost control, it can be a lot more complex, as the cost of the project is tightly bound to almost every other aspect of the project. To illustrate this, the “IRON TRIANGLE REF” is often used, as it shows the three dominant constraints of projects, time, scope and cost as being interrelated to one another, meaning, that if you alter one of them, it will unavoidably also affect the other two. If it takes an extra two months to implement the new software or build a bridge, the project still has to keep paying its workers for that duration.[[File:IronTriangle.png\|300px\|thumb\|right\|alt text]]
	Therefore, to successfully control the cost of the project, measures and procedures can be applied to provide realistic estimates of the budget and track the performance of the project and its cost. These cost control and assessing measures rely on details from other tools commonly used in project management, such as the project charter, WBS, enterprise environmental factors, agreements and many more. According to the Project Management Body of Knowledge (PMBOK), there are four processes to incorporate to get the best reliable overview and control of costs in a project. These are Plan Cost Management, Estimate Costs, Determine Budget and Control Costs, and a more elaborated overview of these can be seen in figure \ref “Project cost management overview (s 268 PMBOK)”<ref name="PMBOK">Project Management Institute, A guide to the project management body of knowledge (PMBOK guide), Project Management Institute, Inc., 2017. </ref>. In summary, they can be recapped as an encouragement to have a well defined, detailed plan of the project, using this to correctly estimate costs and hence determine the actual budget as more details and knowledge is attained and lastly to measure the progress of work and costs throughout the duration of the project. This PMBOK approach relies on, amongst others, two distinct tools used in every step, namely expert judgment and data analysis. As valuable as expert judgment can be, it is not just a bit vaguely described and can be difficult to quickly attain if you have little experience from other similar projects. It also relies on amongst others, previous experiences from similar projects. This is exactly where the data mining and OLAP compliments the existing methods by giving a structured approach on how to gather data from previous experiences and put them use from current and future projects.		Therefore, to successfully control the cost of the project, measures and procedures can be applied to provide realistic estimates of the budget and track the performance of the project and its cost. These cost control and assessing measures rely on details from other tools commonly used in project management, such as the project charter, WBS, enterprise environmental factors, agreements and many more. According to the Project Management Body of Knowledge (PMBOK), there are four processes to incorporate to get the best reliable overview and control of costs in a project. These are Plan Cost Management, Estimate Costs, Determine Budget and Control Costs, and a more elaborated overview of these can be seen in figure \ref “Project cost management overview (s 268 PMBOK)”<ref name="PMBOK">Project Management Institute, A guide to the project management body of knowledge (PMBOK guide), Project Management Institute, Inc., 2017. </ref>. In summary, they can be recapped as an encouragement to have a well defined, detailed plan of the project, using this to correctly estimate costs and hence determine the actual budget as more details and knowledge is attained and lastly to measure the progress of work and costs throughout the duration of the project. This PMBOK approach relies on, amongst others, two distinct tools used in every step, namely expert judgment and data analysis. As valuable as expert judgment can be, it is not just a bit vaguely described and can be difficult to quickly attain if you have little experience from other similar projects. It also relies on amongst others, previous experiences from similar projects. This is exactly where the data mining and OLAP compliments the existing methods by giving a structured approach on how to gather data from previous experiences and put them use from current and future projects.
			[[File:PMBOK.png\|300px\|thumb\|right\|alt text]]


	=='' Data mining and OLAP general idea ''==		=='' Data mining and OLAP general idea ''==
	Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure XX. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way.		Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure XX. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way. [[File:cube.png\|300px\|thumb\|right\|alt text]]

			In figure XX the whole structure of the project cost management system (PCMS) is visualized. From below, the cost data from previous experiences are collected and stored in the cost data warehouse. Then from the data warehouse, it can be extracted for analysis (OLAP), modeling, data mining and aid decision making.[[File:PCMS.png\|300px\|thumb\|right\|alt text]]

	In figure XX the whole structure of the project cost management system (PCMS) is visualized. From below, the cost data from previous experiences are collected and stored in the cost data warehouse. Then from the data warehouse, it can be extracted for analysis (OLAP), modeling, data mining and aid decision making.


	== Collecting the data cubes ==		== Collecting the data cubes ==
	One of the most important aspects of the data cubes are their multidimensional structure, and therefore to make them as useful as possible, they should be established in a consistent manner, meaning that as far as possible, they should have the same input variables. In the example figure of the data cube, see fig XXX, a few examples of parameters are shown, but in reality, one would like to add as many relevant parameters as possible. This is done to enable comparison of parameters between phases of a project or over several projects where the same parameters are relevant to compare over. In figure XXX an example of a data cube with parameters relevant to construction projects is shown with seven different branches, all with several subparameters is shows. Such a level of detail gives the user a myriad of possibilities to aggregate and visualize data.		One of the most important aspects of the data cubes are their multidimensional structure, and therefore to make them as useful as possible, they should be established in a consistent manner, meaning that as far as possible, they should have the same input variables. In the example figure of the data cube, see fig XXX, a few examples of parameters are shown, but in reality, one would like to add as many relevant parameters as possible. This is done to enable comparison of parameters between phases of a project or over several projects where the same parameters are relevant to compare over. In figure XXX an example of a data cube with parameters relevant to construction projects is shown with seven different branches, all with several subparameters is shows. Such a level of detail gives the user a myriad of possibilities to aggregate and visualize data.[[File:fuzzycube.png\|300px\|thumb\|right\|alt text]]

	So the first task at hand when deciding to use data mining and OLAP is to establish an overview of all the relevant parameters you want to collect data from. Especially if one is working in the same field over several projects or if it is just a single big project, the data mining and the analysis done from it can start to provide the benefits from this method.		So the first task at hand when deciding to use data mining and OLAP is to establish an overview of all the relevant parameters you want to collect data from. Especially if one is working in the same field over several projects or if it is just a single big project, the data mining and the analysis done from it can start to provide the benefits from this method.
Line 34:		Line 36:
	As visualization is just one sort of algorithm build on top of the PCMS, so one can add on more algorithmic tools, such as decision-making models. Amongst all the knowledge stored in the database, you can save and use predefined algorithms for making for example regression over data, trends and more. So you can also store previous reactions and decisions in the database. If for example one particular model or trend calculated from the data prompted the project manager to make a decision, this can and should also be stored systematically in the database, so the future aggregation of data can recognize the pattern and suggest a catalog of previously tested reactions to this sort of situation.		As visualization is just one sort of algorithm build on top of the PCMS, so one can add on more algorithmic tools, such as decision-making models. Amongst all the knowledge stored in the database, you can save and use predefined algorithms for making for example regression over data, trends and more. So you can also store previous reactions and decisions in the database. If for example one particular model or trend calculated from the data prompted the project manager to make a decision, this can and should also be stored systematically in the database, so the future aggregation of data can recognize the pattern and suggest a catalog of previously tested reactions to this sort of situation.
	Early warning systems are also a possibility with this way of working with project data. With predefined and built in automatic frequent aggregation of the project cost data, forecasting is possible to visualize and can be set to evaluate the actual and forecasted costs to the estimated and budgeted costs. In cases where there is a risk of overrun of the budget, it can be automatically spotted and dealt with earlier in the project, before the overrun has happened.		Early warning systems are also a possibility with this way of working with project data. With predefined and built in automatic frequent aggregation of the project cost data, forecasting is possible to visualize and can be set to evaluate the actual and forecasted costs to the estimated and budgeted costs. In cases where there is a risk of overrun of the budget, it can be automatically spotted and dealt with earlier in the project, before the overrun has happened.
	In figure XXXX such a visualization is examplified from the article of Lu Zhao REF REF.		In figure XXXX such a visualization is examplified from the article of Lu Zhao REF REF. [[File:overrun.png\|300px\|thumb\|right\|alt text]]


Line 70:		Line 72:

	Query 1: “Average benefit of detached projects depending on the promotor typology”		Query 1: “Average benefit of detached projects depending on the promotor typology”
			[[File:q1.png\|300px\|thumb\|right\|alt text]]

	Query 2: “Average benefit in the beginning phase according to the company size”		Query 2: “Average benefit in the beginning phase according to the company size”
			[[File:q2.png\|300px\|thumb\|right\|alt text]]

	Query 2: “Average benefit in the beginning phase according to the company size”		Query 3: “Average benefit in the beginning phase according to the company size”
			[[File:q3.png\|300px\|thumb\|right\|alt text]]

	Query 4: “Maximum cost deviation in big-sized projects according to the project phases”		Query 4: “Maximum cost deviation in big-sized projects according to the project phases”
			[[File:q4.png\|300px\|thumb\|right\|alt text]]

	Query 5: “Average cost deviation in big-sized projects according to the project chapters”.		Query 5: “Average cost deviation in big-sized projects according to the project chapters”.
			[[File:q5.png\|300px\|thumb\|right\|alt text]]

Morning at 19:18, 4 March 2019

2019-03-04T19:18:02Z

← Older revision		Revision as of 19:18, 4 March 2019
Line 13:		Line 13:

	Would it not be nice, if we could just do projects all day and never have to worry about the cost? Such thoughts have no place in the real world, where projects success and performance is measured directly by how much it has cost compared to how much was budgeted to cost. If you send your hungry project manager to McDonalds to get the team for 80 kr. of junk food per person and he returns having spent 125 kr. per person, it obviously will be regarded as a failure, disregarding the fact that you had the food delivered to your desk and you all have Sundaes and Tops. The same thing goes with projects, where the making or breaking of a project budget can determine whether or not you will be reassigned to such projects again.		Would it not be nice, if we could just do projects all day and never have to worry about the cost? Such thoughts have no place in the real world, where projects success and performance is measured directly by how much it has cost compared to how much was budgeted to cost. If you send your hungry project manager to McDonalds to get the team for 80 kr. of junk food per person and he returns having spent 125 kr. per person, it obviously will be regarded as a failure, disregarding the fact that you had the food delivered to your desk and you all have Sundaes and Tops. The same thing goes with projects, where the making or breaking of a project budget can determine whether or not you will be reassigned to such projects again.
	For projects and management of cost control, it can be a lot more complex, as the cost of the project is tightly bound to almost every other aspect of the project. To illustrate this, the “IRON TRIANGLE REF” is often used, as it shows the three dominant constraints of projects, time, scope and cost as being interrelated to one another, meaning, that if you alter one of them, it will unavoidably also affect the other two. If it takes an extra two months to implement the new software or build a bridge, the project still has to keep paying its workers for that duration.		For projects and management of cost control, it can be a lot more complex, as the cost of the project is tightly bound to almost every other aspect of the project. To illustrate this, the “IRON TRIANGLE REF” is often used, as it shows the three dominant constraints of projects, time, scope and cost as being interrelated to one another, meaning, that if you alter one of them, it will unavoidably also affect the other two. If it takes an extra two months to implement the new software or build a bridge, the project still has to keep paying its workers for that duration.[[File:IronTriangle.png\|200px\|thumb\|left\|alt text]]
	Therefore, to successfully control the cost of the project, measures and procedures can be applied to provide realistic estimates of the budget and track the performance of the project and its cost. These cost control and assessing measures rely on details from other tools commonly used in project management, such as the project charter, WBS, enterprise environmental factors, agreements and many more. According to the Project Management Body of Knowledge (PMBOK), there are four processes to incorporate to get the best reliable overview and control of costs in a project. These are Plan Cost Management, Estimate Costs, Determine Budget and Control Costs, and a more elaborated overview of these can be seen in figure \ref “Project cost management overview (s 268 PMBOK)”<ref name="PMBOK">Project Management Institute, A guide to the project management body of knowledge (PMBOK guide), Project Management Institute, Inc., 2017. </ref>. In summary, they can be recapped as an encouragement to have a well defined, detailed plan of the project, using this to correctly estimate costs and hence determine the actual budget as more details and knowledge is attained and lastly to measure the progress of work and costs throughout the duration of the project. This PMBOK approach relies on, amongst others, two distinct tools used in every step, namely expert judgment and data analysis. As valuable as expert judgment can be, it is not just a bit vaguely described and can be difficult to quickly attain if you have little experience from other similar projects. It also relies on amongst others, previous experiences from similar projects. This is exactly where the data mining and OLAP compliments the existing methods by giving a structured approach on how to gather data from previous experiences and put them use from current and future projects.		Therefore, to successfully control the cost of the project, measures and procedures can be applied to provide realistic estimates of the budget and track the performance of the project and its cost. These cost control and assessing measures rely on details from other tools commonly used in project management, such as the project charter, WBS, enterprise environmental factors, agreements and many more. According to the Project Management Body of Knowledge (PMBOK), there are four processes to incorporate to get the best reliable overview and control of costs in a project. These are Plan Cost Management, Estimate Costs, Determine Budget and Control Costs, and a more elaborated overview of these can be seen in figure \ref “Project cost management overview (s 268 PMBOK)”<ref name="PMBOK">Project Management Institute, A guide to the project management body of knowledge (PMBOK guide), Project Management Institute, Inc., 2017. </ref>. In summary, they can be recapped as an encouragement to have a well defined, detailed plan of the project, using this to correctly estimate costs and hence determine the actual budget as more details and knowledge is attained and lastly to measure the progress of work and costs throughout the duration of the project. This PMBOK approach relies on, amongst others, two distinct tools used in every step, namely expert judgment and data analysis. As valuable as expert judgment can be, it is not just a bit vaguely described and can be difficult to quickly attain if you have little experience from other similar projects. It also relies on amongst others, previous experiences from similar projects. This is exactly where the data mining and OLAP compliments the existing methods by giving a structured approach on how to gather data from previous experiences and put them use from current and future projects.

Morning at 23:10, 22 February 2019

2019-02-22T23:10:44Z

← Older revision		Revision as of 23:10, 22 February 2019
Line 1:		Line 1:
	~~===Cost control in project management with Data Mining and OLAP===~~		Thomas Boel - s124865

	== Summary==		==='''Cost control in project management with Data Mining and OLAP'''===

			==''' Summary'''==

	Cost control is arguably one of the most important skills to master within the discipline of project management. Cost and the ability to stay on the set budget is often used as the direct measure of the performance or success of a project, and as it is intrinsically bound up on virtually every other aspect of the project, early information on shortcomings of cost control measures can be an important warning sign telling about other aspects of the project undergoing unforeseen or underestimated challenges or to entail drastic changes or perhaps even failure the project.		Cost control is arguably one of the most important skills to master within the discipline of project management. Cost and the ability to stay on the set budget is often used as the direct measure of the performance or success of a project, and as it is intrinsically bound up on virtually every other aspect of the project, early information on shortcomings of cost control measures can be an important warning sign telling about other aspects of the project undergoing unforeseen or underestimated challenges or to entail drastic changes or perhaps even failure the project.
Line 8:		Line 10:


	==Big Idea==		=='''Big Idea'''==

	Would it not be nice, if we could just do projects all day and never have to worry about the cost? Such thoughts have no place in the real world, where projects success and performance is measured directly by how much it has cost compared to how much was budgeted to cost. If you send your hungry project manager to McDonalds to get the team for 80 kr. of junk food per person and he returns having spent 125 kr. per person, it obviously will be regarded as a failure, disregarding the fact that you had the food delivered to your desk and you all have Sundaes and Tops. The same thing goes with projects, where the making or breaking of a project budget can determine whether or not you will be reassigned to such projects again.		Would it not be nice, if we could just do projects all day and never have to worry about the cost? Such thoughts have no place in the real world, where projects success and performance is measured directly by how much it has cost compared to how much was budgeted to cost. If you send your hungry project manager to McDonalds to get the team for 80 kr. of junk food per person and he returns having spent 125 kr. per person, it obviously will be regarded as a failure, disregarding the fact that you had the food delivered to your desk and you all have Sundaes and Tops. The same thing goes with projects, where the making or breaking of a project budget can determine whether or not you will be reassigned to such projects again.
Line 15:		Line 17:


	== Data mining and OLAP general idea ==		=='' Data mining and OLAP general idea ''==
	Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure XX. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way.		Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure XX. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way.

Morning at 23:09, 22 February 2019

2019-02-22T23:09:25Z

← Older revision		Revision as of 19:31, 4 March 2019
Line 15:		Line 15:
	For projects and management of cost control, it can be a lot more complex, as the cost of the project is tightly bound to almost every other aspect of the project. To illustrate this, the “IRON TRIANGLE REF” is often used, as it shows the three dominant constraints of projects, time, scope and cost as being interrelated to one another, meaning, that if you alter one of them, it will unavoidably also affect the other two. If it takes an extra two months to implement the new software or build a bridge, the project still has to keep paying its workers for that duration.[[File:IronTriangle.png\|300px\|thumb\|right\|alt text]]		For projects and management of cost control, it can be a lot more complex, as the cost of the project is tightly bound to almost every other aspect of the project. To illustrate this, the “IRON TRIANGLE REF” is often used, as it shows the three dominant constraints of projects, time, scope and cost as being interrelated to one another, meaning, that if you alter one of them, it will unavoidably also affect the other two. If it takes an extra two months to implement the new software or build a bridge, the project still has to keep paying its workers for that duration.[[File:IronTriangle.png\|300px\|thumb\|right\|alt text]]
	Therefore, to successfully control the cost of the project, measures and procedures can be applied to provide realistic estimates of the budget and track the performance of the project and its cost. These cost control and assessing measures rely on details from other tools commonly used in project management, such as the project charter, WBS, enterprise environmental factors, agreements and many more. According to the Project Management Body of Knowledge (PMBOK), there are four processes to incorporate to get the best reliable overview and control of costs in a project. These are Plan Cost Management, Estimate Costs, Determine Budget and Control Costs, and a more elaborated overview of these can be seen in figure \ref “Project cost management overview (s 268 PMBOK)”<ref name="PMBOK">Project Management Institute, A guide to the project management body of knowledge (PMBOK guide), Project Management Institute, Inc., 2017. </ref>. In summary, they can be recapped as an encouragement to have a well defined, detailed plan of the project, using this to correctly estimate costs and hence determine the actual budget as more details and knowledge is attained and lastly to measure the progress of work and costs throughout the duration of the project. This PMBOK approach relies on, amongst others, two distinct tools used in every step, namely expert judgment and data analysis. As valuable as expert judgment can be, it is not just a bit vaguely described and can be difficult to quickly attain if you have little experience from other similar projects. It also relies on amongst others, previous experiences from similar projects. This is exactly where the data mining and OLAP compliments the existing methods by giving a structured approach on how to gather data from previous experiences and put them use from current and future projects.		Therefore, to successfully control the cost of the project, measures and procedures can be applied to provide realistic estimates of the budget and track the performance of the project and its cost. These cost control and assessing measures rely on details from other tools commonly used in project management, such as the project charter, WBS, enterprise environmental factors, agreements and many more. According to the Project Management Body of Knowledge (PMBOK), there are four processes to incorporate to get the best reliable overview and control of costs in a project. These are Plan Cost Management, Estimate Costs, Determine Budget and Control Costs, and a more elaborated overview of these can be seen in figure \ref “Project cost management overview (s 268 PMBOK)”<ref name="PMBOK">Project Management Institute, A guide to the project management body of knowledge (PMBOK guide), Project Management Institute, Inc., 2017. </ref>. In summary, they can be recapped as an encouragement to have a well defined, detailed plan of the project, using this to correctly estimate costs and hence determine the actual budget as more details and knowledge is attained and lastly to measure the progress of work and costs throughout the duration of the project. This PMBOK approach relies on, amongst others, two distinct tools used in every step, namely expert judgment and data analysis. As valuable as expert judgment can be, it is not just a bit vaguely described and can be difficult to quickly attain if you have little experience from other similar projects. It also relies on amongst others, previous experiences from similar projects. This is exactly where the data mining and OLAP compliments the existing methods by giving a structured approach on how to gather data from previous experiences and put them use from current and future projects.
	[[File:PMBOKcost.~~png~~\|300px\|thumb\|right\|alt text]]		[[File:PMBOKcost.jpg\|300px\|thumb\|right\|alt text]]


	=='' Data mining and OLAP general idea ''==		=='' Data mining and OLAP general idea ''==
	Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure XX. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way. [[File:cube.~~png~~\|300px\|thumb\|right\|alt text]]		Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure XX. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way. [[File:cube.jpg\|300px\|thumb\|right\|alt text]]

	In figure XX the whole structure of the project cost management system (PCMS) is visualized. From below, the cost data from previous experiences are collected and stored in the cost data warehouse. Then from the data warehouse, it can be extracted for analysis (OLAP), modeling, data mining and aid decision making.[[File:PCMS.~~png~~\|300px\|thumb\|right\|alt text]]		In figure XX the whole structure of the project cost management system (PCMS) is visualized. From below, the cost data from previous experiences are collected and stored in the cost data warehouse. Then from the data warehouse, it can be extracted for analysis (OLAP), modeling, data mining and aid decision making.[[File:PCMS.jpg\|300px\|thumb\|right\|alt text]]



	== Collecting the data cubes ==		== Collecting the data cubes ==
	One of the most important aspects of the data cubes are their multidimensional structure, and therefore to make them as useful as possible, they should be established in a consistent manner, meaning that as far as possible, they should have the same input variables. In the example figure of the data cube, see fig XXX, a few examples of parameters are shown, but in reality, one would like to add as many relevant parameters as possible. This is done to enable comparison of parameters between phases of a project or over several projects where the same parameters are relevant to compare over. In figure XXX an example of a data cube with parameters relevant to construction projects is shown with seven different branches, all with several subparameters is shows. Such a level of detail gives the user a myriad of possibilities to aggregate and visualize data.[[File:fuzzycube.~~png~~\|300px\|thumb\|right\|alt text]]		One of the most important aspects of the data cubes are their multidimensional structure, and therefore to make them as useful as possible, they should be established in a consistent manner, meaning that as far as possible, they should have the same input variables. In the example figure of the data cube, see fig XXX, a few examples of parameters are shown, but in reality, one would like to add as many relevant parameters as possible. This is done to enable comparison of parameters between phases of a project or over several projects where the same parameters are relevant to compare over. In figure XXX an example of a data cube with parameters relevant to construction projects is shown with seven different branches, all with several subparameters is shows. Such a level of detail gives the user a myriad of possibilities to aggregate and visualize data.[[File:fuzzycube.jpg\|300px\|thumb\|right\|alt text]]

	So the first task at hand when deciding to use data mining and OLAP is to establish an overview of all the relevant parameters you want to collect data from. Especially if one is working in the same field over several projects or if it is just a single big project, the data mining and the analysis done from it can start to provide the benefits from this method.		So the first task at hand when deciding to use data mining and OLAP is to establish an overview of all the relevant parameters you want to collect data from. Especially if one is working in the same field over several projects or if it is just a single big project, the data mining and the analysis done from it can start to provide the benefits from this method.
Line 36:		Line 36:
	As visualization is just one sort of algorithm build on top of the PCMS, so one can add on more algorithmic tools, such as decision-making models. Amongst all the knowledge stored in the database, you can save and use predefined algorithms for making for example regression over data, trends and more. So you can also store previous reactions and decisions in the database. If for example one particular model or trend calculated from the data prompted the project manager to make a decision, this can and should also be stored systematically in the database, so the future aggregation of data can recognize the pattern and suggest a catalog of previously tested reactions to this sort of situation.		As visualization is just one sort of algorithm build on top of the PCMS, so one can add on more algorithmic tools, such as decision-making models. Amongst all the knowledge stored in the database, you can save and use predefined algorithms for making for example regression over data, trends and more. So you can also store previous reactions and decisions in the database. If for example one particular model or trend calculated from the data prompted the project manager to make a decision, this can and should also be stored systematically in the database, so the future aggregation of data can recognize the pattern and suggest a catalog of previously tested reactions to this sort of situation.
	Early warning systems are also a possibility with this way of working with project data. With predefined and built in automatic frequent aggregation of the project cost data, forecasting is possible to visualize and can be set to evaluate the actual and forecasted costs to the estimated and budgeted costs. In cases where there is a risk of overrun of the budget, it can be automatically spotted and dealt with earlier in the project, before the overrun has happened.		Early warning systems are also a possibility with this way of working with project data. With predefined and built in automatic frequent aggregation of the project cost data, forecasting is possible to visualize and can be set to evaluate the actual and forecasted costs to the estimated and budgeted costs. In cases where there is a risk of overrun of the budget, it can be automatically spotted and dealt with earlier in the project, before the overrun has happened.
	In figure XXXX such a visualization is examplified from the article of Lu Zhao REF REF. [[File:overrun.~~png~~\|300px\|thumb\|right\|alt text]]		In figure XXXX such a visualization is examplified from the article of Lu Zhao REF REF. [[File:overrun.jpg\|300px\|thumb\|right\|alt text]]


Line 72:		Line 72:

	Query 1: “Average benefit of detached projects depending on the promotor typology”		Query 1: “Average benefit of detached projects depending on the promotor typology”
	[[File:q1.~~png~~\|300px\|thumb\|right\|alt text]]		[[File:q1.jpg\|300px\|thumb\|right\|alt text]]

	Query 2: “Average benefit in the beginning phase according to the company size”		Query 2: “Average benefit in the beginning phase according to the company size”
	[[File:q2.~~png~~\|300px\|thumb\|right\|alt text]]		[[File:q2.jpg\|300px\|thumb\|right\|alt text]]

	Query 3: “Average benefit in the beginning phase according to the company size”		Query 3: “Average benefit in the beginning phase according to the company size”
	[[File:q3.~~png~~\|300px\|thumb\|right\|alt text]]		[[File:q3.jpg\|300px\|thumb\|right\|alt text]]

	Query 4: “Maximum cost deviation in big-sized projects according to the project phases”		Query 4: “Maximum cost deviation in big-sized projects according to the project phases”
	[[File:q4.~~png~~\|300px\|thumb\|right\|alt text]]		[[File:q4.jpg\|300px\|thumb\|right\|alt text]]

	Query 5: “Average cost deviation in big-sized projects according to the project chapters”.		Query 5: “Average cost deviation in big-sized projects according to the project chapters”.
	[[File:q5.~~png~~\|300px\|thumb\|right\|alt text]]		[[File:q5.jpg\|300px\|thumb\|right\|alt text]]

← Older revision		Revision as of 23:09, 22 February 2019
Line 16:		Line 16:

	== Data mining and OLAP general idea ==		== Data mining and OLAP general idea ==
	Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao ~~“ref Lu Zhao”~~ not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure XX. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way.		Of course, project managers always try to use historical data to assist their decisions for new projects, but it is, according to an article by Lu Zhao <ref name="Zhao">L. Zhao, Project Cost Control System Based on Data Mining, International Forum on Information Technology and Applications, 2009. </ref> not until it is done in a systematic and consistent way over data from several projects that the project manager can reap the full benefits of this data. Therefore Lu Zhao suggests a method, where data is systematically collected and stored in data cubes in a data warehouse, so it can easily be aggregated in new ways, extracting and comparing relevant parameters over time. An example of such a data cube could look like figure XX. Here the data points are stored in a multidimensional array combining, time, estimated and actual cost, participants and other relevant statistics in one data point. Having several of these data cubes, stored in a data warehouse, then allows users and project managers to access them and visualize patterns from earlier in the project or from earlier projects in an easy and relevant way.

	In figure XX the whole structure of the project cost management system (PCMS) is visualized. From below, the cost data from previous experiences are collected and stored in the cost data warehouse. Then from the data warehouse, it can be extracted for analysis (OLAP), modeling, data mining and aid decision making.		In figure XX the whole structure of the project cost management system (PCMS) is visualized. From below, the cost data from previous experiences are collected and stored in the cost data warehouse. Then from the data warehouse, it can be extracted for analysis (OLAP), modeling, data mining and aid decision making.
Line 50:		Line 50:

	== An example of application ==		== An example of application ==
	This method has been used and tested in probably every field of science, but more often you see it used within construction projects or other big scale projects that stretch over a long time. One example is from a series of construction projects where the team behind had gathered data in the following categories:		This method has been used and tested in probably every field of science, but more often you see it used within construction projects or other big scale projects that stretch over a long time. One example is from a series of construction projects where the team behind had gathered data in the following categories <ref name="Rojas">M. Mart´ınez-Rojas, Cost Analysis in Construction Projects using Fuzzy
			OLAP Cubes, Ieee International Conference on Fuzzy Systems , 2015. </ref>:
	Project, scale. Was it a series of projects or an unrelated project? Was it small, medium or big?		Project, scale. Was it a series of projects or an unrelated project? Was it small, medium or big?
	Time. Very important as prices for materials, labor and others can vary through time, and because time is typically a parameter you will want on one of the axes when visualizing data.		Time. Very important as prices for materials, labor and others can vary through time, and because time is typically a parameter you will want on one of the axes when visualizing data.
	Location. Also has significant impact on price ranges within construction projects		Location. Also has a significant impact on price ranges within construction projects
	Promoter. Was there a promotor or project sponsor who finances the project, and was it private or public promotion?		Promoter. Was there a promotor or project sponsor who finances the project, and was it private or public promotion?
	Tye of construction. Of course, this has huge implications on the cost of the project, so It is a relevant parameter to sort the data by for comparison		Tye of construction. Of course, this has huge implications on the cost of the project, so It is a relevant parameter to sort the data by for comparison
Line 60:		Line 61:

	From these gathered data over several projects, the research team could ask the PCMS the following queries and get the following outputs.		From these gathered data over several projects, the research team could ask the PCMS the following queries and get the following outputs.
	~~Citation~~ from article ~~Ref ref:~~		The following text and pictures are taken directly cited from Rojas <ref name="Rojas">M. Mart´ınez-Rojas, Cost Analysis in Construction Projects using Fuzzy
			OLAP Cubes, Ieee International Conference on Fuzzy Systems, 2015. </ref>, however some of the text has been omitted to keep this article short.