Picture a major soft drinks operation. This entity you have imagined consists of a company that owns the brand and makes the concentrate, and a number of other companies, acting in the manner of a consortium, who bottle and distribute the end product. Let’s say the consortium decides to run a price promotion of 97 cents per can, against the usual price of a dollar. The potential problem with this promotion is that individual bottlers have different systems, and some of them may not recognize a single can as an entity – in which case, the promotion will either be applied inaccurately, or it won’t run at all.
Now let’s say it’s not the consortium running the promotion, but an individual bottler – or even that it’s one specific regional sales team for an individual bottler.
In all these cases, what we might call standard automation could be introduced to handle all the discrepancies created by the thousands of three-cent claims from retailers.
But intelligent automation would approach the problem in a different way. It would create a consistent platform, and not allow a unit price to be acted upon unless it resides in the system. This eliminates the need to have a large team correcting master data or handling claims.
In short, the difference between the two approaches is that standard automation addresses the problem after it’s happened, whereas intelligent automation looks for areas of inefficiency, and addresses them up front. What’s more, the standard approach may act on a few transactions and automate them imperfectly, creating a magnifier effect on far more transactions further down the line.
It’s clear, then, that intelligent process automation is rooted in practicality. It’s not about technology looking for an application: it’s about looking first, and in detail, at processes, addressing issues, and streamlining tasks, before automation and robotization technology is brought into the picture. Capgemini’s sequential methodology – Eliminate, Standardize, Optimize, Automate, and Robotize (ESOAR) – is especially relevant here.
Here’s another example. Major organizations process orders in one of two ways: either manually, by responding to emails or PDFs; or via electronic data interchange (EDI), which is of course a form of automation in itself from their customers.
However, EDI’s ability to automate is not limitless. For example, individual products have ID codes, and the code assigned to an item by the customer may not be the same as that assigned by the manufacturer or supplier. As with our soft drinks example, there is scope in such cases for error or disruption, because all EDI is doing is pushing data indiscriminately through the system.
At Capgemini, we’ve employed intelligent process automation in one real-world client case to address this problem, by deliberately breaking the natural flow of EDI, passing the order information through a business rules engine. This order validation engine creates a common and consistent data set – as before, which prevents a problem getting into the system or process up front, rather than having to deal with the issues it causes later downstream.
Intelligent process automation doesn’t just provide a way to address problems in advance: it can also enable supply chain developments that weren’t possible before.
For example, demand planning conventionally depends on sales history. When you know how well a product has sold before, in different geographies and at different times of the year, you can make predictions about future demand.
But when you’re bringing a new product to market, there is by definition no sales history. It’s difficult to make forecasts.
Capgemini’s proprietary approach uses intelligent process automation to bring together statistical models and machine learning tools so as to create a means of analysis in such instances. The majority of new products aren’t completely new territory for an organization. They are iterations of, or extensions to, other stock keeping units (SKU). They are, in short, more often than not joining a pre-existing product family. Our proprietary approach extrapolates data from similar, relevant SKUs, and leverages this data to compare the forecast and the actual sales history to enable the planner to make a data-based informed decision of the sales forecast for the new product. This gives planners a statistical frame of reference that wasn’t available to them before.
Another area in which intelligent process automation comes into its own is promotion planning.
This is an area of especial importance in consumer goods and over-the-counter pharmaceuticals, because promotions account for a significant proportion of overall revenue.
We have found that companies in these markets have tended not to keep libraries of past promotions. They haven’t logged the last few years of promotions – the nature of the offer, and its expected and actual effects on sales – and because of this, they can’t be confident of the uptick they can expect on future planned incentives.
Capgemini has been able to revisit historical sales data and apply machine learning techniques to gauge forecasts against reality – and thereby to create a library that didn’t exist before. Armed with this, organizations have real data on which to make decisions about future promotions, and to decide in each case whether the balance should tip towards the instinctive caution of the finance team or the characteristic optimism of people in marketing.
Problems and opportunities
We’ve always prided ourselves at Capgemini on the practicality of our approach. The methodologies we apply, and the processes and tools we employ, are not rooted in pure theory, but in principles derived from real business cases.
Intelligent Process Automation is the latest such development. It’s about the application of digital transformation principles to specific individual scenarios – enabling us, in the supply chain and in other areas of the enterprise, not just to solve perennial problems, but to create exciting opportunities for innovation and growth.
To learn more about how Capgemini’s Intelligent Process Automation offering can help to standardize and integrate your supply chain master data with planning, execution, and insights based on a proven and comprehensive framework to deliver outcome-based results and smarter operations, contact: email@example.com
Learn more about Capgemini’s Digital Supply Chain Practice can increase your competitive advantage by strengthening your business drivers and focusing on your end customers.
Dharmendra Patwardhan is responsible for developing offers and capabilities for transforming supply chain operations that drive tangible business outcomes for Capgemini’s clients.