Distribution Management – What do most businesses overlook?

Businesses spend years in building an efficient distribution network. Through the distribution network, every business strives to make its products available for consumption at the right place at the right time and at the right price point. Not being able to do that can easily lead to loss of revenue and customer churn. This holds especially true in low involvement products where there isn’t much differentiation within competitors.

On the other hand, efficient distribution can be a huge differentiator for a business and gives it an edge in the marketplace, irrespective of any differentiation in the product or price. Just the availability of the products as per consumer’s comfort can help a business grow. All the investments in customer acquisition initiatives like marketing, advertisements, promotions etc. can go for a toss in the absence of a robust distribution setup. It is like generating the demand for your products but not being able to fulfill it. It is a straight opportunity loss – apart from the customer experience and perception thatgets impacted, which is one the toughest challenges in marketing to correct.

You would already be aware of the above facts. Businesses have been working on it since last 2-3 decades. Most large players have extremely strong distribution networks now. They have done all the heavy lifting in setting up their supply chain which moves goods from factories till it hits the shelves.

While you may have a robust distribution network, here are a few things that you may have overlooked:

Moving the needle for RPA

With the highly competitive nature of businesses, leaders today are constantly searching ways to unlock more value from within their processes. Over the last few years, robotic process automation (RPA) has become the de facto go-to approach for business leaders to drive higher efficiencies in the organizational processes. It has helped delegate “robotic” work to machines while keep the value adding activities in the value chain for humans. And what a success it has been!! According to estimates, automation software could save over 140 million FTE hours by 2025, effectively giving that many hours back to business to be used in more productive work. Apart from hard business savings, it has also improved customer satisfaction, brought in more quality into the processes and aided in compliance.

However, it is now time to move the needle and as we mature in this space there are newer challenges that beckon. Bots are not inherently intelligent or cognitive, they are good at doing the stuff assigned to them but do not learn from their mistakes on their own. This is where Artificial Intelligence (AI) can play along with bots to drive greater value. McKinsey has estimated that AI techniques has the potential to unlock value of $9.5 trillion to $15.4 trillion annually and as such it has become imperative that businesses start looking towards a future where automation and AI co-exist.

Components of cognitive RPA

Although AI as a field is quite wide in terms of its scope, in relation to RPA, we can divide the AI blocks into 4 categories. These categories are in a constant flux and may change over time and would serve as a starting point to discuss the impact of AI on automation. Those components are:

Machine Vision or Computer Vision

Machine Vision or Computer Vision refers to the ability to identify objects, scenes and activities in images. It involves a number of processing techniques to break down images into manageable chunks of information which can then be analyzed to gather insights. Perhaps, the most common type of computer vision that is in use in the RPA world is Optical Character Recognition (OCR) which helps in identifying characters of a particular language from an image. The use cases range from reading documents (PDFs, images) to identifying UI elements in a web page.

Think of an invoice processing use case which is typically beset with large number of manual tasks, errors and inefficiencies. The entry point of this process is generally an invoice which could be digitized into an image or an PDF. OCR helps in identifying the various elements of the invoice which would then be typically posted into an accounting software or an ERP package (like SAP) using RPA.

Essentially computer vision with RPA can help in processing structured and semi-structured documents and taking them to logical conclusion.

A couple of use cases where computer vision can be used in conjunction with RPA are:

The future holds infinite possibilities with advancement in technologies and we might be able to intelligently read and capture information from unstructured documents as well such as contracts, agreements and letters, and determine the right course of action.


In the US, the number of chat app users has crossed 150 million users in 2018 and is expected to reach 171 million users by 2022 effectively meaning that 45.5% of US population would be using one of the messaging apps. Chatting has become one of the most preferred way of communicating with almost all aspects our life, be it friends, customer service, or even business.

Chatbots could use their cognitive prowess to recognize the said or in some cases, unsaid needs of a customer or a business user. Now, it could invoke an integrated RPA bot to initiate the backend processes that might be required to resolve the customer needs, almost on a real time basis.

Think of a typical organizational process where senior executives want certain data points to assess organizational health. While there are standard and custom reports that are periodic in nature, chatbots could enable a culture where data is available whenever needed. Just ask (or perhaps, type)!!

From a consumer perspective think of Facebook chatbots which can be used along with RPA to trigger campaign workflows to delight customers with the right information.

A few other use cases where chatbots in could be useful are:

Text Analytics and Knowledge Ontologies

By some estimates, over 80% of all data available in an organization is unstructured in nature. Take the example of lot of knowledge being captured in free form emails or chats; or even tickets where user is allowed free hand in describing what they need.

An example is analysis of social media content to identify adverse events to take action to manage brand reputation. Typically, this is done in a manual manner where analysts read and analyze social media content and then derive specific insights leading to longer time to call to action. This can be solved by deploying a real-time alerting mechanism where text analytics identifies the sentiment of real-time social media posts and then, an RPA bot calculates velocity of adverse comments, fetches relevant data from different social listening platforms and finally sends alert emails to right stakeholders. It will result in higher customer satisfaction and provide enough time to douse the social media fires before they get out of hand.

Extending this to an enterprise, think of a tech support that is working day in and day out maintaining certain software. Now of course there would be SLAs and other procedures in place to handle spikes in maintenance ticket volumes. But think of a major customer raising a concern over channels other than the ticketing application, for example multiple concern emails to higher management. Text analytics can be used to determine the sentiment of those emails, correlate them to the concern raised and then a bot raises an appropriate flag to the support team for immediate resolution.

Other use cases where text analytics could be used along with RPA to drive exponential benefits are:

Predictive Analytics / Machine Learning

In this article, till now we have explored use cases which enable a trigger to invoke certain process activities. Let’s turn this problem around, and we are presented with a treasure trove of historical data that is generated out of the process audit trails (something that RPA bots are typically tuned to do for compliance and track-and-trace capabilities). If we feed that data into various machine learning algorithms, one might be able to find answers to a lot of questions such as which elements of the process can drive a certain outcome.

For example, let’s say there is a bot that processes orders for an organization. One of the correlations you might be able to see from the audit logs is that the order fulfilment rate goes up when ordered at a certain period of time which could then be incorporated into the bot settings.

A few other use cases where data analytics can drive immense value are:

The holy grail is when through process mining and auto-generated process maps, robots can be created automatically without the need to configure them manually. We have taken a significant leap towards it by launching AssistEdge Discover which helps in doing automated process discovery and generating process maps through deep neural network algorithms, thus providing empirical data to backup the intent to automate.


In the next few years, AI is expected to have tremendous impact on the way we work. Delegation of work to machines by human would become routine and business benefits would encompass a much broader area than mere cost savings. AI technologies are becoming more and more accessible to citizen developers and combined with RPA would bring in the next wave of business excellence.

As an RPA vendor, the onus is now on us to find out ways to integrate AI into our offerings more seamlessly, develop transformational use cases that utilize the full power of AI and make the tech accessible to all. As an RPA buyer have a vision beyond cost savings while implementing AI and automation, focus on use cases around customer experience and hire the right experts to implement them.


1) https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/disruptive-technologies
2) https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep-learning#part3
3) https://www.emarketer.com/content/messaging-apps-and-marketing-2018

Roadblocks Deterring Effective Procurement Analytics

From being a mere support function to becoming a strategic partner in an organization, procurement is surely going through a huge transformation. Technology holds the key to unlocking the ability of procurement to propel enhanced performance, strategically consult and drive innovation across the supply chain.

However, the path ahead for procurement analytics is not without its difficulties, despite the clear possibilities it offers. The 2018 SAP/ Ariba Global CPO survey1 identified analytics and data quality as the largest roadblock to procurement efficiency. Concerningly, the Infosys Portland 2017, Asia Pacific CPO2 survey indicated that, while technology is the fastest growing priority area for CPOs, less than 40% were confident of the success of their procurement technology plans. Working in an organization where there is no data-driven decision-making culture could imply that we have little faith in the reliability of the data at our disposal. The organizational priorities will then dictate the specific objectives to drive procurement’s adoption and use of analytics.

Improved technology convergence, richer information sources, and enhanced company models give a route forward. To enhance actionable insights, most of the issues faced by existing procurement analytics solution fall under two main categories. To succeed, each solution must be addressed, namely: organizational readiness, information quality, team capacity, and technology deficiencies.

Data collection and analysis will be useless without clear objectives. Data and analytics must develop beyond spend analysis in a procurement-specific context. CPOs have to spend as much time creating new data assets as they take advantage of existing ones. Three Key elements are required to realize the readiness of an organization adopting such a solution.

Organizational Challenge #1: What does Value mean to an Organization?

Many companies use “value” as a synonym for “cost reduction”. As important as cost reduction is, a wider variety of value levers are controlled by leading procurement organizations that assist companies boost income and lower risk. Actionable insights from supply chain and procurement data give CPOs and their teams the ability to better apply force to these levers. The key to acquiring these insights is an analytics solution that can find and leverage the right data.

A procurement analytics solution provides value based on how well procurement organizations can identify opportunities to:

With such large quantity, range and speed of information being generated, customers need to automate business processes to guarantee that information is accessible for efficient assessment. However, purchasers may experience difficulties in automating distinct business processes owing to the prevalence of legacy systems. To tackle such issues, one should invest in technologies and systems that can provide real-time data recording capabilities to implement analytical solutions.

Organizational Challenge #2 – How to enable Data Quality?

While analytics can perform sophisticated functions, the final insights obtained rely heavily on the nature of information supplied as inputs. The information captured by distributors may differ, making it vital to correctly define data points for analytics software. Although most software providers assist buyers in filtering and utilizing data for insights, they might later find it difficult to judge the data insights provided by the solutions.

Ideally, an organization has a robust master data management; however, procurement teams do not typically have this luxury, so their analytics solution must contend with:

In addition, the amount of information produced internally and externally is larger than ever and continues to expand steadily, across the stakeholders. Much of this information has the potential to help CPOs boost various business process efficiency, manage supplier risks and improve savings.

Organizational Challenge #3 – Can teams read and convert insights?

Even if a procurement function has a robust approach, the ability to identify value levers, clarity in data, and analytical capacity is often scarce within the team. It is anticipated that a procurement analyst will combine the skills of a data scientist, a data visualization specialist and an expert in the procurement category to obtain, recognize and visualize ideas. Analysts with this unusual mixture of talent are often prompted rapidly; therefore, procurement features tend to provide inconsistent in-house analytics.

While wider analytical functions may exist within organizations, they lack the procurement skills necessary to obtain actionable ideas. The challenge is even higher in smaller organizations where, in addition to their periodic projects and duties, individual procurement team members are also supposed to do this.

A procurement analytics solution’s goal is not to show information in aesthetic, user-friendly dashboards. Its aim should be to identify actionable ideas that move forward and bring value to the strategy of an organization. A procurement function struggles to concentrate its analytical resources without a clear approach to deliver value to its organization.

Technological Challenge – Are Procurement Analytics Software smart enough?

Because of a number of shortcomings, procurement analytics solutions have come short in consistently assisting CPOs and their teams to derive value from their information.

Even if the organization is well prepared, prevalent deficiencies of the current alternatives are invariably short of expectations. These weaknesses include:

Always Value Actionable Insights

With many businesses struggling to make sense of their information and generate value with their big data investments, the promise of actionable insights sounds great. While we would be pleased to get as many insights from our data as possible, not all takeaways are actionable. With some insights, we may not understand what to do and therefore choose to disregard them.

A solution for procurement analytics must be designed to provide actionable thoughts. That is, to provide specific key insights into the organization that defines value areas. To support the delivery of the procurement strategy, it must move beyond generic historical spend dashboards.

Predictive and prescriptive analytics assist, and drive organizations forward as analytical maturity rises. Using external information sources and more sophisticated (predictive) technology, organizations will be more proactive in addressing value regions rather than adopting more reactive methods of descriptive and diagnostic analytics.

In short,

Large-scale procurement automation procedures are increasingly generating large quantities of information that can bring fresh insight into the company, helping them better comprehend the evolution and adaptation of supply chain. However, as data quantity increases, observation and analysis simultaneity becomes a necessity, but exposes the danger of trade-offs with data processing velocity between reliability and information depth.

Procurement analytics will therefore need to create ‘intelligence’ not only based on prior findings, but also on ideas gathered from understanding continuing trends and situations linked to subject matter. Intelligence’s role is not restricted to forecast. The main aim of addressing future occurrences is, after all, to shorten the lead time to the action.

We have covered more on this in our latest whitepaper on “Demystifying Procurement Analytics”. Download it today to learn how to overcome procurement challenges.

1 – SAP Ariba CPO Survey 2018 – What’s the Next Big Thing in Procurement
2 – Infosys Portland Survey 2017 – The Changing Face of Procurement

How are intelligent digital workers adding value to businesses today?

Robotics Process Automation has traditionally been targeted at processes that run on legacy applications and is often looked as tactical solution to address short-term business requirement. Front office application UI automation follows pre-defined rules, limiting the capabilities of Digital Workers. The predominant use cases RPA industry been automating does not demand intelligence; however, the question been always lingering:

Can these digital workers extend the scope of automation by acquiring intelligence capabilities such as seeing, learning and communicating? Can the digital workers apply intelligence to add value to e-commerce or to the labor-intensive and yet to computerized farm industry?

AssistEdge, EdgeVerve’s automation solution, recently launched its most intelligent offering – AssistEdge RPA 18.0. The product offers holistic automation suite by empowering digital workers with the intelligence of vision, analytics, and self-learning capabilities.

So how can Intelligent Automation be applied to e-commerce and farm industry?

OnlineMeds, an online commerce portal for medicines, offers both prescription and OTC medicines. One can purchase medicines online at discounted rate and get them delivered at home for free. For prescription medicines – one can either submit prescription online or request for a call back from a doctor to issue a prescription. The below workflow captures the typical stakeholders involved in the transaction and purchase experience.

The tasks involved at OnlineMeds in the whole purchase experience:

The ease of purchase is driving the change in consumer behavior and increasing the transaction volume for such online portals; however, the execution model has predominantly been supported by manual tasks:

With intelligence capability now embedded in Digital workers, they are empowered to add more value to business. Below is possible implementation of automation, with intelligent capability such as Vision and OCR, in the online medicine purchase use case. Vision will mark relevant information on handwritten or digital prescription, while OCR will convert the data to structured format for DWs to execute a purchase order.

OnlineMeds significantly invests manual efforts to review the prescription, dispatch medicines and manage huge contact center base to address customer queries. A digital worker can mimic user action right from backend operations of analyzing a prescription to confirming the patient on the purchase.

At the outset, a digital worker will be able to cut-down the whole process execution time by half, improving customer experience and saving cost for OnlineMeds.

Robotics has been associated with machineries that follow human command or to RPA that mimics human behavior as per pre-defined business rules. Software as a service on cloud has extended the scope to fields where one may not even establish any hardware at premises.

Taking farming for instance. Can digital workers offer any automation and reduce the manual efforts involved in farming? Can digital workers apply intelligence to offer seamless monitoring and maintenance asks at a farm?

Automation solution – AssistEdge RPA is capable to run entirely on cloud, access intelligence and take business decisions.

Below is a representation of how intelligent digital workers with assistance from monitoring products such as drones can help deliver value to farming at lower costs.

A farmer largely spends time in monitoring the field, irrigation and evaluation of crops for harvesting.

Digital workers can be trained using machine learning models to take decision and perform the above tasks. The ML model will instruct digital workers to initiate irrigation, analyze whether pest control is needed or the crop is ready for harvesting. A digital worker lets all these operations be managed remotely, saving manual efforts and leading to more efficient farm production.

The acquired intelligence has exponentially increased the possibilities of automation. Digital workers can now do more than just follow pre-defined business rules – they can analyze data run-time, communicate with stakeholders and take decisions. They have the capability to continuously learn and build history to take best possible decisions.

Businesses must now explore all possibilities to increase efficiency and grab new growth opportunities by hiring intelligent digital workers.

AI: The new competitive edge for organizations

The AI revolution is here and is rapidly transforming both business and personal ecosystems. AI has become commonplace across many aspects that we interact with every day. The power of AI is not just changing the way businesses function, but even how governments operate. As organizations continue to make AI a strategic priority, the last few years have seen the adoption of AI growing exponentially. Gartner estimates that the enterprise AI adoption grew by over 270% in the last 4 years. Today, AI is a key boardroom topic. There are many ways how AI is becoming a competitive advantage for organizations across the value chain — foreseeing and unifying the omnichannel customer journey for superior experience or predicting the needs across a supply chain to optimize inventory and maximize revenue.

In one of my recent discussions with a few financial sector CXOs, one of them remarked that the first wave of digital delight is over. While the context was from a banking viewpoint, that statement rings true across industries. Consumers have embraced the digital revolution and have become tech-savvy digital natives with higher expectations. Hence, organizations need AI — now more than ever — to be digitally competitive.

Moving beyond the hype to reality

Organizations today are using more data than ever before. They are looking to transform these data-points into insights and business opportunities to stay ahead of their competitors. As AI enables organizations to understand data, enhance processes, improve decision-making and create better offerings, the opportunities this unfolds are limitless. Yet, they find it tough to scale their AI initiatives. A recent study from PwC states that 73% of organizations are still in the planning/experimentation phase in their AI projects.

Implementing and scaling AI has many practical challenges — here are three barriers in scaling AI across the enterprise:

The business impact of AI

AI has demonstrated the potential to address business pain-points and is helping organizations transform to create new business and revenue models. AI has matured significantly and is offering plug-and-play solutions that can offer tangible, real-time business outcomes.

A leading bank in the US wanted to modernize its collection process to reduce delinquency rates. We suggested a ready-to-use AI product that not only improved process efficiencies but also made it intelligent by effectively mapping customer segments to customize collection strategies. The result was not just a reduction in delinquency rates, but also a significant enhancement in customer experience. This enabled the bank to kick-start their AI journey in less than 3 months.

Retail is another sector being transformed by AI since there is a significant flow of data across the demand value chain. AI provides a competitive advantage by contextualizing external business data and making it insight-ready, thereby providing varying go-to-market scenarios that help retail enterprises scale by adding new distributors, improving retail execution and reaching new markets faster. There are many such examples of AI powering transformation in areas like fraud detection, contracts analysis, and procurement effectiveness across industry sectors.

The next phase of business transformation through AI will be when there is visibility into customer journeys across value chains and stitching the insights together. This can power the ability of an enterprise to predict future outcomes, highlight likely issues and suggest suitable actions. Businesses must accelerate their AI strategy and leverage it for business growth.

A practical approach to purposeful AI

AI is enabling organizations to work better and is fast becoming a competitive advantage for organizations that are able to leverage it effectively. Here are a few ways to embrace AI:

Rapid experimentation for AI innovation – AI should be viewed as a tool with the power to solve business pain-points that seemed unsolvable in the past. This calls for an agile way of experimenting on the many enterprise use-cases that can help initiate the AI journey.

Scale across the enterprise – It is imperative to have a clear enterprise-wide AI strategy. The success of this strategy will depend on identifying the right areas for AI to scale and stimulate growth.

Identify the right technology building blocks – With a focus on solving specific business problems, AI technology should be easy to deploy and use. A buy-and-build approach, a robust enterprise-grade AI platform, and plug-and-play business applications have enabled organizations to roll out strategies in a faster and effective manner.

I believe that through a series of experiments and innovations, AI will continue to grow as a competitive advantage for organizations. Success will depend on leadership vision, AI strategy and a sustained commitment to getting it right.

AI has truly arrived and is presenting businesses with endless new possibilities to leap ahead!

Assuring Automation Efficiency with AssistEdge Cloud RPA

Forrester predicts the RPA market will reach $2.9 billion in 2021. And that automation can cut operating costs by up to 90%. Given these figures, it is no surprise that RPA is growing at an unprecedented rate across various industries and business units.

Organizations looking to embark on a digital transformation journey have implemented RPA in diverse business functions — from invoice processing and inventory management to client onboarding and customer support.

Automating highly repetitive rule-based processes helps achieve significant cost savings, increased accuracy, and optimized cycle time. Robotic Process Automation or RPA alone is not a wonder drug, but it sure does relieve the human workforce of mundane clerical tasks, thus improving business processes’ efficiency across the enterprise.

Some of the adoption challenges businesses face with RPA implementation are:

These factors call for an automation service that brings together capabilities of cloud, RPA product and managed service delivery. Deploying a cloud-based enterprise RPA platform empowers businesses to take advantage of the industry-leading automation capabilities.

How can Cloud RPA resolve these challenges? Is Cloud RPA worth the investment? Why should it take center stage?

Here are a few benefits of AssistEdge Cloud RPA. Let’s dive in:

Saves time and costs related to infrastructure deployment

Setting up RPA platforms require high capital investment. Scaling up and scaling down the platform based on business demand is not easy, thus leading to the high cost of maintaining and operating RPA programs.

RPA Cloud imposes minimal IT burden as there is no need to install the software locally. It also significantly lowers organizational costs, reduces the need to invest in large-scale IT infrastructure and decreases the complexity and time involved in setting up the RPA platform within organizations.

Enables teams to focus on value-adding tasks and not technology setup woes

What concerns businesses? Excessive efforts spent on repetitive mundane tasks, complex processes and dependency on people.

With Cloud-based services, business teams do not have to worry about spending time and energy to operate the automation technology platform. Enterprises can benefit from a new generation of automation capabilities that will enable you to streamline processes, mine into resources and adapt to customer requirements effortlessly.

If the benefits of automation are not leveraged quickly, whatever innovation is promised goes by the wayside and ROI is prolonged in the process, leading to an IT nightmare.

Rapidly scale across the enterprise

HR, procurement, finance, and customer support functions do not have to operate in individual silos any more. Cloud RPA can be deployed across the enterprise, empowering businesses to collaborate with multiple users simultaneously.

It makes your business processes smarter, faster and more reliable. You can also streamline and optimize your process flow easily.

Highly scalable and easy to setup

Scalability is a blessing in RPA implementation. It can scale quickly across the breadth and depth of an enterprise/business function. RPA implementation on cloud is a lot easier, scalable and faster than using on-premises solution.

Introducing AssistEdge Cloud RPA

Bringing together the power of AssistEdge, Cloud infrastructure and Deep Automation, enterprises get access to real-time monitoring of automated processes. A comprehensive cloud automation suite, it offers RPA as a managed service powered by standard cloud infrastructure and a flexible service model that meets diverse enterprise needs and accelerates time to value.

What can AssistEdge Cloud RPA do for you?

With AssistEdge Cloud RPA, enterprises can now tap into the industry’s best RPA delivery capability and deep cloud expertise, having deployed 50,000+ BOTs across business domains. It provides RPA on tap to futureproof your business with greater speed, scalability and security through hassle-free rapid process automation.

We experienced this with one of the largest global healthcare technology companies, where we implemented AssistEdge Cloud RPA for their finance and accounting function. With AssistEdge, the organization was able to automate a staggering 140 processes across its operations and saved 400,000 person-hours. Click here to read more.

Features and benefits of AssistEdge Cloud RPA:


Managing everything on the Cloud!

Interest in new technologies such as Artificial Intelligence (AI), the Cloud, Machine Learning (ML), and cognitive intelligence has increased over the past year. Early adopters have already achieved significant benefits with RPA implementation — from automating repetitive business processes to accelerating time to value.

Moving to the Cloud is but a natural step in the journey to digital transformation. Small and mid-sized companies that focus on scaling automation processes are gradually shifting to cloud-based services within the organization.

In a nutshell, cloud convergence is signalling a new era of automation. Are you ready to take your business to the next level?

To learn more about AssistEdge Cloud RPA, visit our website now.

The new era of Procurement – Intelligent Spend Analysis

Procurement is often defined as a series of activities and processes designed for efficient acquisition of products and services necessary for regular business functioning. It can involve functions such as selection of suppliers, negotiation and management of contracts, establishing payment conditions, actual acquisition of the products and much more.

In other words, procurement is the umbrella term for a multitude of cost and core business functions, qualifying to be considered as a part of an enterprise’s corporate strategy. Since an organization can end up spending well over half of its income on buying products and services, it is essential to manage adequate procurement. Even the slightest reduction in buying expenses can have a significant direct effect on earnings, while a shortage of strategic choices can cause a financially healthy business to sink.

Spend Analysis – The foundation of procurement’s performance

The costs incurred by an organization are often termed as inevitable by most procurement managers. Most often, they aren’t able to gauge which expenses are avoidable and which ones are contributing to the organization’s success. This is why procurement managers often rely on spend analysis. It helps increase procurement efficiency and cost savings opportunity by collecting, classifying and analyzing the spend data.

Most enterprises today are using spend analysis as a catalyst to improve efficiency, reduce maverick spend and achieve cost savings.

Spend analysis, simply put, is a process, which involves analysis of the enterprise purchases and invoices, in an effort to find opportunities to curb maverick and unmanaged spend. In turn, it provides insights to better supplier negotiations, compliance adherence and better sense of the demand and pricing.

Procurement organizations worldwide use spend analysis tools to proactively define savings possibilities, handle risks and optimize the purchasing power of the organization. It is often considered the basis of sourcing. Spend Analysis has the capacity to transform any organization’s data into a powerhouse of ideas and information that can be used to obtain Spend visibility.

Significance and Features of Spend Analysis

Spend visibility and analysis give procurement another chance to contribute to broader company goals. A thorough spend analysis can:

Benefits of Spend Analysis

Challenges in implementing Effective Spend Analysis

Spend Analysis is the process of collecting, cleansing, classifying and analyzing spend information through a dedicated software, provide complete visibility through spend analysis dashboards. Though it is a crucial part of a procurement strategy, implementing an effective spend analysis is not quite easy.

Confusion is often caused between 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 (KPIs) applicable to the procurement function.

Among most popular Spend Analysis KPIs are

However, procurement teams implementing a solution for spend analysis often have to overcome roadblocks before gaining more impact and spending with the instrument under leadership.

The four difficulties facing procurement agencies in adopting an efficient spend analysis are as follows:

Cognitive Procurement – Taking Spend Analysis to the next level.

Owing to the worldwide market dynamics and the need for organizations to reduce expenses without eliminating resources, there has been an increase in the use of Procurement Service Providers (PSPs). The use of Artificial Intelligence, particularly machine learning with its capability to self-learn enables existing systems to learn from the volumes of data without human intervention and detect potential issues and resolve them quickly.

By applying analysis to the millions of data points they already have, leaders today, are equipped with intelligent and informed business decisions which in turn is helping them increase cost savings, decrease operating costs, and reduce risk.

Today, leading procurement organizations are not merely measuring what they have spent but pushing their spend analysis definition to encompass their complete value contribution to the company, using both standard and newly available information sources to gain insights from true supply analysis.

Forecasting Insights

Predictive Spend analysis in procurement is still fairly new, but it is increasingly essential, especially in organizations that have already been through the process of spend analysis, leveraging suppliers, segmentation and consolidation. Prediction needs a thorough knowledge of the supply chain ecosystem’s technical and commercial characteristics, as well as sophisticated statistical and modeling capacities. This enables income forecasting, mitigating risk, identifying market opportunities, and much more. It is often constructed on a strategic platform that offers visibility of information and near-real-time accessibility as well as sophisticated data warehouses to collect appropriate third-party data sets. They can also provide an in-depth view of the operational outcomes of the supply chain that are not readily visible to business executives.

Key to this evolution is the rise of artificial intelligence within the company, which now helps procurement organizations obtain fresh perspectives and shape fresh policies with conventional spend analysis methods that are not feasible. Procurement leaders in the fields of Strategic Sourcing, Supply Management & Risk Management take their analytic approach to the next level with AI.


The journey towards procurement becoming a strategic function has just begun vis-a-vis its tactical past. In many aspects, successful procurement functioning has been rendered unattainable by historical flaws. But the gaps in Spend Analysis Services are starting to narrow with developments in machine learning and artificial intelligence, especially the power of deep learning. Accurate, real-time classification has become the new norm, and with previous offerings, prescriptive intelligence based on community-based benchmarks brings fresh perspectives to procurement that were once not feasible. Only in this manner will AI be able to assist address today’s most urgent, contemporary procurement problems. 

As Magnus Bergfors, research director at Gartner puts it; “The first step to automating spend management is to make sure that existing tools are both fit for purpose and are used throughout the organization. Proper use of existing tools provides the platform for AI, as well as the data on which to train it. Application leaders should prioritize investment in solutions that support automation for spending categories that are not well-served by current systems.”

(Quote Source: https://www.gartner.com/smarterwithgartner/prepare-for-the-impact-of-ai-on-procurement/)