The use of digital twins represents one of the emerging trends in retail in 2022. Here’s how they can be used and what are the potential and limits of a tool destined to change the way we shop.
The inconveniences caused by Covid-19 are increasingly substantially affecting the retail sector. A recent American study states that with the pandemic, four out of five businesses have had to review how they manage their supply chain.
What Are Digital Twins?
Digital twins are a concept born in the world of engineering and, from there, extended to almost every realm of reality. It is a detailed digital model representing an accurate virtual replica powered by information updated in real-time. It can be applied to mechanical components, objects of all kinds, places such as entire cities, and processes or people in the flesh.
The affirmation of digital twins goes hand in hand with the explosion of the IoT, a fundamental support for obtaining the vast amount of information necessary to make event simulations and behavior projections and arrive at a design in which every aspect is planned and foreseen. If the conception of digital twins is familiar, the application in the retail world is still in its early stages. It represents an avant-garde for the sector, which has yet to discover and appreciate its potential.
Digital Twins: The Application In Retail
The enhancement and greater accessibility of sensors capable of detecting an enormous amount of data, and the progressive digitization of physical reality, are laying the foundations for the diffusion of digital twins in the retail trade, where they can find multiple applications.
Beyond their use in the production phase of an object, a process that can become dynamic and intertwine with user feedback in stores, digital twins in retail can mainly be exploited to improve the customer journey and make the supply chain more flexible. Attention to the consumer/prospect is fundamental in the retail sector.
Implementing such a powerful tool can allow testing new scenarios in real time without intervening in practical reality. To give a concrete example – specializes in 3D modeling – creates a three-dimensional digital copy of a store with Store Electronic Systems made alive by a constant feed of sensors reporting the prices of the labels on the products or the presence of promotions. To this data, you can then add any points of interest, crowding and conversion rates, as well as external information such as the flow of customers entering/exiting the premises, the availability of goods in the warehouse, and perhaps even the density of traffic in the surroundings and the weather conditions by time.
The Potential Effects
All this allows you to refine predictions on user behavior and incoming/outgoing flows, adjust prices, model discount policies, improve customer touchpoints, and provide faster responses to customer requests. The ultimate goal may be identifying different consumption patterns based on which to calibrate increasingly refined marketing segmentation processes.
The frontier of a similar joins with the consumer’s digital twin and prefigures a personalized and almost wholly automated sales model for each customer. The impact of these digital replicas can also find space in the internal processes of an activity: both in the layout of the exhibition spaces and as regards energy management (lighting, heating/cooling of the rooms) or the safety of the premises, the adoption of innovative checkout forms to avoid bottlenecks and gatherings (very undesirable in this period) as well as staff shifts and warehouse stocks.
Real-time updates on sales, checkout and inventory trends allow retailers to reduce out-of-stocks. The presence of information updated in real-time combined with a solid historical series of data can be exploited for more accurate supplies, targeted replacements, minimizing waste of space and goods, and optimizing cash flows. The great value of digital twins is that they allow you to imagine and try out different scenarios without taking all the risks that a live test would entail. The introduction of an innovative solution such as automated checkout could be simulated in detail, observing its integration with existing processes and structures. The change could be reviewed, tested, and refined before being implemented in-store.
The Necessary Conditions
On the one hand, the ability of digital twins to adapt dynamically by reflecting the changes of their natural counterparts allows us to become aware of situations that do not emerge only by observing the data. On the other hand, however, it must be noted that a digital twin is not a magic wand and its adoption, in the face of innumerable potential benefits, requires certain conditions, without which it is not advisable.
First, a constant source of digital data on the various aspects of the business and vast volumes of historical information on the activity is essential for “feeding” the Artificial Intelligence and machine learning processes. The possibility of applying complex decision-making reasoning that can include different outcomes and the presence of scalable and repeatable processes are also indicated as non-mandatory but valuable conditions.
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