How do analytics support a Procurement Organizations? Let us take a look at the challenges of implementation: data is in multiple system, existing data is not analysed, skill set in an organization is not available, right technology is not available and one-off versus operationalised. With the advancement in automation it has completely changed the business world. A data centric approach, which take over the hectic laborious information compilation, organisation, and dissemination processes that used to take ages to complete. This means the procurement leaders making use of the larger improved quantities analysis collected in real time can improvise the data and enabling smarter and more accurate decisions on spending, managing suppliers and design better strategies.
Introduction to Data Analytics
Data analytics refers to the two different techniques and process of the measurement of data. The two techniques namely, qualitative and quantitative, are used to improve productivity and business profits. Data obtained are organized to recognize and analyse behavioural data and patterns. The techniques used by different companies varies according to their requirements for data collection.
Qualitative technique refers to the collection of data in non-numerical form. Usually, involves interpretative and natural approach to the topic. Which means that the study is based on their natural settings and the meaning that people gave it.
Quantitative technique refers to the collection of data in numerical form which can be classified into different categories or in terms of measurement unit. The data collected are used to create graphs.
Data analytics turn quantitative data into information that aids company with making important decisions. As the statistics generated from the data can be used to monitor trends, patterns and relationships. There are two types of statistics, namely, descriptive and inferential. Descriptive statistics summarises data while inferential statistics recognises the differences between groups of data.Data analysis is important to improve efficiency, cost reduction, market understanding, new products and services, faster and better decision making and industry knowledge.
What is Procurement Analytics?
This is the use of quantitative technique in data analytics the process of collecting and analysing procurement data to gather important perception and help to make good business decision. It typically involves the collection of data from several different sources, grouping data accordingly and presenting the data as a diagram or other means of intelligence tool. When the data are used effectively, the data can be used to derive at a decision. This will result in supplier relationships and purchasing decisions being managed productively.
Four Types of Procurement Analytics
(1) Descriptive analytics – Is the use of full range of data to give accurate of the changes that had occurred to a business. By incorporating reading of data over multiple successive points in time to better understand the trend. Example, when the company expand rapidly this can be overwhelming to keep up with the change. To stay competitive, company will need to study and identify area of strength and weakness and spot a spending pattern that has taken place in the past.
(2) Diagnostic analytics – Is analysing data with the help of external information to deepen the understanding of why it happened in the past to make the best decision. They fall into 3 categories, namely, identifying anomalies, discovery and determining causal relationship. Example, the company after reviewing the descriptive analytics report wants to know the root cause behind the change in trends, they will then look at external information which are relevant to determine the reason behind the changes. The manual solution to it would be for analyst to identify unusual occurrence. As the volumes and variety of data increases, it is not possible to analyse the data manually. Alternatively, the modern solution would be to employ the use of artificial intelligence to complement the analyst in finding the root cause. With the use of artificial intelligence, unbiased and misinterpretation of correlation as causation would be reduced and thus portraying a more accurate analysis.
(3) Predictive analytics – Predicts what will happen in the future based on what was analysed and interpreted in descriptive and diagnostic analytics. It uses a number of data mining, predictive modelling and analytical techniques to bring together the management, information technology and modelling business process. Example, the company wants to keep itself as the top choice of their customer. They would want to predict the changes they can make to keep their customer base as well as to attract new customers. The solution would be to use predictive analytics to anticipate customer’s needs by understanding the purchasing patterns their customer’s made in the past. It can be used to identify loss and opportunities for future.
(4) Prescriptive analytics – Is utilising the understanding of what happened, why it happened and what might happen to help the company determine the best solution. Example, data-intensive businesses and government agencies can benefit from using prescriptive analytics where cost of human error is high.
Applications of Procurement Analytics
Procurement analytics are applied differently across the industry depending on their needs. The most common applications are:
o Spend Analytics – The use of analysing spend data to reduce cost, increase efficiency and to improve the relationship with the supplier. These findings stressed on the importance of a company’s cost reduction strategy combined with training and development program benefit both the staff and company as a whole.
o Contract management analytics – Improving business performances through the clarity of risk, commitments and opportunities in the contract. Contract management analytics means that you can get a clearer and more precise picture of contract hotspots. By applying contract management an organizations can build stronger relationships with customers, suppliers, strategic partners and investors. Identify trends and opportunities for performance improvement, cost recovery, savings and loss mitigation. It can make intelligent procurement decisions from choice of payment terms with existing customers.
o Category management analytics – When effectively used, analytics give category managers superpowers. Procurement analytics allows category managers to identify savings opportunities, segment and prioritize suppliers, address supply risk opportunities and facilitate innovation.
o Savings lifecycle analytics – Progress throughout savings projects is captured based on actual spend and reported in intuitive and intelligent dashboards. Powerful analytics gives up-to-date visibility to your savings realization with full details when needed. Procurement and finance work together towards the same set of targets, have one shared version of the facts and figures to discuss on, and the ability to deliver results in efficient and transparent way.
o Procurement benchmarking – Using key performance indicators (KPIs) to monitor and evaluate performance and compliance. KPIs can be established for internal processes, vendor performance and compliance, or organizational performance in the marketplace.
Diagram of Spend Analysis
Challenges in implementing Effective Spend Analysis
Confusion is often caused by direct and indirect spending. As far as metrics are concerned, procurement information can be categorized based on a number of Spend Analysis key performance indicators (KPI) applicable to the procurement function.
The four main difficulties facing procurement agencies in adopting an efficient spend analysis are as follows: Lack of understanding, insufficient resources, required analysis capabilities and interdepartmental relationship.
Spend analysis is the first step to initiate an effective procurement. With artificial intelligence (A.I), the effectiveness can be increased through machine learning. Machining learning (ML) algorithms make one or more decisions or predictions based on one or many inputs of data. ML is the ability of computers to self-teach and improve from experience without being explicitly programmed. ML would be able to predict and understand procurement’s needs even before the specialists do.
Effective spend analysis is better achieved with Artificial Intelligence rather than being done manually. Machine learning would be able to make a better prediction and understanding of the procurement’s needs based on the data it has learnt to then complete task it is assigned. This would then allow manual task to be automated and free up time spend on routine task. With artificial intelligence, new data from reliable sources can be captured.
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