August 21, 2019

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How Digitalization – through automation and AI – is transforming demand planning | Technology

How Digitalization – through automation and AI – is transforming demand planning | Technology

The strategy of demand planning is present process huge transformation. While it has traditionally been a reactive course of involving responding to altering market circumstances, the appearance of expertise is permitting – and on the identical time forcing – demand planning to develop into way more strategic. Digitalising demand planning is changing into crucial for organizations that wish to keep forward of rivals, impress prospects and drive firm earnings. Demand planning is not a case of merely reacting – as a substitute, it requires steady proactivity to efficiently predict demand. In line with this, synthetic intelligence (AI) is changing into an intrinsic a part of the demand planning perform, additional boosting planning accuracy through sensing the markets’ wishes.

A latest Capgemini report discovered that, on the subject of provide chain digitalization, organizations work on too many tasks concurrently, with near 30 tasks at pre-deployment phases. This excessive quantity inevitably results in some initiatives failing to take off, and locations probably the most important tasks in danger. The digitalization of demand planning – and subsequent implementation of AI – is one instance of a important initiative which companies should prioritize, and that has tangible and fast advantages, together with:

Strategic decision-making

AI drives automation of the extra conventional and labour-intensive duties inside demand planning to the subsequent stage – most notably, analyzing and decoding batches of knowledge. Not solely is AI ready to do that extra precisely and shortly, however – by automating these important however advanced duties – the crew’s time is freed up in order that they will give attention to extra strategic enterprise endeavours.

Additionally, demand planners not must dedicate massive quantities of time to creating short-term demand plans or triggering inventory replenishment – AI can do that for them. The crew can then think about progressing higher-value enterprise targets that can have a larger impression on the group. Demand planners might want to interpret their position extra strategically, e.g. dedicate extra time to analyze methods to enhance operational effectivity, establish new methods to extend earnings and develop into extra concerned within the enterprise as an entire.

Improved forecasting

With a lot knowledge available, it has develop into harder to detect buyer buying patterns. Artificial intelligence can work to chop through this noise, processing the info to uncover delicate patterns that people would have missed. By aggregating datasets from Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) and Internet of Things (IoT) methods – and combining this with exterior variables and contextual knowledge equivalent to a calendar of occasions, seasonality and the climate – AI works to supply extra correct demand planning forecasts.

If this holistic method is taken, AI forecasts can then be linked through provide and stock planning to automate replenishment triggers, in order that organizations persistently have the right amount of merchandise in inventory. This leads to elevated gross sales by enhancing order fill charges and shelf availability.


For instance, a worldwide group for private care merchandise constructed a demand-driven provide chain utilizing knowledge analytics to extend visibility into real-time demand tendencies. This enabled the corporate to provide and retailer the precise quantity of stock required to switch what customers really bought, as a substitute of producing based mostly on forecasts from historic knowledge. The firm additionally utilized point-of-sales (POS) knowledge from retailers equivalent to Walmart to generate forecasts that triggered shipments to shops and knowledgeable inside deployment selections and tactical planning.

This method helped the corporate to successfully observe inventory preserving items and delivery areas. As a end result, it noticed as much as a 35% discount in forecast errors for a one-week planning horizon and 20% for a two-week horizon.

More responsive   

Supply chain channels are undoubtedly weak to quite a lot of exterior components – for instance pure disasters or availability of uncooked supplies– that may impression demand forecasting. Rather than counting on historic knowledge, AI and machine studying instruments use real-time calculations to reply to and discover resolutions for provide chain disruptions. As nicely as this, automation permits for speedy responses to altering client demand, enhancing gross sales and earnings, and boosting client loyalty. This added responsivity boosts the accuracy of demand planning and limits financial losses.

An workplace merchandise retailer, for instance, had disparate methods working autonomously with completely different SKUs, forecasting and planning processes. Management acknowledged that, and not using a “synchronized view of demand” of its provide chain, the corporate couldn’t reply quickly sufficient to market adjustments. Capgemini and a software program options supplier had been introduced in to implement an progressive answer designed to empower the retailer with synchronized decision-making and, in the end, a singular aggressive benefit. The answer is permitting the corporate to proactively meet fluctuations by tightly integrating a spread of core enterprise processes, beginning at merchandise planning through to the replenishment course of. The firm expects this to extend top-line income by delivering actual strategic worth and robust demand chain outcomes.

As with any important organizational change, an agile method – involving small steps, small failures, and quick restoration – can ship the fast outcomes that clearly display the worth of cutting-edge demand planning approaches, such because the implementation of AI.

With this in thoughts, A proof of idea method (POC) is extremely really useful. This permits enterprises to achieve a greater understanding of the prices and returns of automation, in addition to perceive the talents and alterations that shall be wanted to accommodate it. Ultimately, the earlier a company begins to regulate the best way it goes about demand planning, the earlier the advantages will develop into obvious.


By Jörg Junghanns, Head of Europe – Digital Supply Chain for Business Services at Capgemini

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