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Insight – PATA SYSTEMS

The Case For Procurement Analytics

Insight 2018-09-25T08:25:55+00:00

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Procurement Analytics Services

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What is Procurement Analytics?

Reports and analytics are two very distinct things, the former just produces data reports pure and simple, but the latter, assuming the data selected is the most appropriate, takes the data from different sources, integrates it and then analyses it to determine what each data stream means on its own as well as when it is combined with the other data streams. From this, trends, anomalies and details warranting further investigation can be derived. This then produces useful data that can be used to predict future trends, identify inefficiencies and may lead to changes in business process, changes to procurement strategy etc.

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Don’t We already do this?

At present although procurement organisations claim to be undertaking analytical reviews, the reality is that many simply run regular reports, not necessarily of the most useful information and too often think that analysis means identifying savings, including both cost avoidance and hard savings. This fails to understand the reality of what procurement data analysis can reveal. Do those reports get read in detail? Some organisations buy systems in an effort to “tick the analytics box” and then fail to get real benefit from them for various reasons. This is then compounded by the fact that in many cases the staff running these processes lack comprehensive functional knowledge of the environment in which they are operating. Anyone can analyse data but in order to really understand what data to use and what the analysis indicates requires a thorough functional knowledge of the business processes.

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The Need for Procurement Analytics

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Data-driven business processes are not a nice-to-have but a need-to-have capability today. Data is an extremely valuable commodity, it’s a free by-product of conducting business which is often overlooked, which if used appropriately can deliver business process improvements, increase competitiveness and save money on the bottom line. Why would an organisation not want to use this to provide all the benefits mentioned? The reality is many organisations believe that reports of savings on procurement spend is sufficient when in fact procurement analytics can provide so much more than just savings on purchasing given the analysis of the wealth of data procurement organisations generate in their day to day business.

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The Solution

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This is where PATA Systems can make a considerable difference. We have experience of procurement operations, supply chain, the use of technology to improve business process, including automation and finally the ability to undertake comprehensive analytical research. We can provide guidance with regard to the importance of the various types of data, reviewing data quality and undertaking cleansing of the data. Analysis of the data would then be followed by interpretation of the results and where appropriate an action plan identifying what needs to be addressed. In addition we can provide advice regarding what must change, what can change and the priorities when committing money for these changes.

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Cost, Delivery times for results and Frequency of Regular Analytics.

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Cost and Delivery of results clearly depends up many factors including but not limited to:
Quality and complexity of Data and where necessary Data Cleansing. In order to obtain reliable analytics the data needs to be as accurate as possible so a very important task is data cleansing. The reality is “Rubbish in Rubbish Out” inaccurate data results in poor inaccurate analytical results.
Source of the data. Organizations often have data held in various electronic systems resulting in data outputs in different formats. A major challenge is to combine these different data sources  into one comprehensive data bank. Some examples of systems where data may reside are SAP Business Warehouse, Oracle, SQL server, other in-house built systems, etc. with the output format of this data from each of these systems ranging from Excel, txt, csv, etc.
Quantity of Data and what level of analytics is required.
If interpretation and a prioritised action plan is required.
In the case of the frequency in the supply of regular analytics this is dependent upon the transaction rates and the frequency and size of data change.

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Data Analysis process

Data analysis would follow the following steps:
Data Acquisition:

“Identify data sources”
All the relevant data sources will be identified and pulled into a central repository. The data sources can vary from Excel file to SQL databases to text files to csv files to online data. Ideally a secured server (Client owned) would be used to store & pre-process the data.

Process/Clean Data:
“The accuracy of the results of analysis depends on the quality of data”. The Data Cleansing step is considered to be one of the most
important phases in Data analysis. The accuracy of the analysis depends on the quality of data. A few approaches to achieving this include: Formatting the data, depending upon the data analytical tools being use.

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Missing data handling:

Data Transformations like normalizing the data, identifying outliers and
handling etc.

Exploratory Analysis:

“Embrace the data visually before diving further”

The objective of this step is to understand the main characteristics of the data. This analysis is generally done using visualizing tools. Performing an Exploratory analysis helps us: To understand causes of an observed event. To understand the nature of the data we are dealing with Assess assumptions on which our analysis will be based Model Generation & Validation: (Only for Advanced Analytics):[/fusion_text][/fusion_builder_column][/fusion_builder_row][/fusion_builder_container][fusion_builder_container hundred_percent=”no” hundred_percent_height=”no” hundred_percent_height_scroll=”no” hundred_percent_height_center_content=”yes” equal_height_columns=”no” menu_anchor=”” hide_on_mobile=”small-visibility,medium-visibility,large-visibility” class=”” id=”” background_color=”” background_image=”” background_position=”center center” background_repeat=”no-repeat” fade=”no” background_parallax=”none” enable_mobile=”no” parallax_speed=”0.3″ video_mp4=”” video_webm=”” video_ogv=”” video_url=”” video_aspect_ratio=”16:9″ video_loop=”yes” video_mute=”yes” video_preview_image=”” border_size=”” border_color=”” border_style=”solid” margin_top=”” margin_bottom=”” padding_top=”” padding_right=”” padding_bottom=”” padding_left=””][fusion_builder_row][fusion_builder_column type=”1_6″ layout=”1_6″ spacing=”” center_content=”no” link=”” target=”_self” min_height=”” hide_on_mobile=”small-visibility,medium-visibility,large-visibility” class=”” id=”” background_color=”” background_image=”” background_position=”left top” background_repeat=”no-repeat” hover_type=”none” border_size=”0″ border_color=”” border_style=”solid” border_position=”all” padding_top=”” padding_right=”” padding_bottom=”” padding_left=”” dimension_margin=”” animation_type=”” animation_direction=”left” animation_speed=”0.3″ animation_offset=”” last=”no”][/fusion_builder_column][fusion_builder_column type=”2_3″ layout=”2_3″ spacing=”” center_content=”no” link=”” target=”_self” min_height=”” hide_on_mobile=”small-visibility,medium-visibility,large-visibility” class=”” id=”” background_color=”” background_image=”” background_position=”left top” background_repeat=”no-repeat” hover_type=”none” border_size=”0″ border_color=”” border_style=”solid” border_position=”all” padding_top=”” padding_right=”” padding_bottom=”” padding_left=”” dimension_margin=”” animation_type=”” animation_direction=”left” animation_speed=”0.3″ animation_offset=”” last=”no”][fusion_separator style_type=”none” hide_on_mobile=”small-visibility,medium-visibility,large-visibility” class=”” id=”” sep_color=”” top_margin=”40px” bottom_margin=”” border_size=”” icon=”” icon_circle=”” icon_circle_color=”” width=”” alignment=”center” /][fusion_text]

“Select-Train-Evaluate”

This step involves extracting features from the data and feeding them into tools to build a model. Model is the solution proposed for the problem statement. This step involves: Model selection, model training and model evaluation. Data Transformations like normalizing the data, identifying outliers and handling etc.

Visualize Results:

“Show the results visually”

This is the final step of Data analysis where the results are presented generally in visual reports, dashboards and story boards. Some visualizing tools include Tableau, SAP Crystal and Google analytics.

Technologies:

Data Transfer: The most secure way would be a Secured server owned by the Client to access

data transfer outputs and supply the completed analysis once completed.

Tools to be used for data analysis includes but is not limited to Advanced Excel , Tableau, Python, R Studio and Talend.

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Ready to talk?

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Animators see things find more information in ways, many others miss.