The changing consumer landscape demands a new agility to keep up.
In the past, retailers had a well-defined, narrow supply chain path: the end goal was to move massive amounts of products en masse to retail stores.
But in today’s economy, consumers are in charge of their own paths to purchasing. Organizations are increasingly expected to adapt to an omnichannel model of commerce or become obsolete.
The ramifications of these changing paradigms affect every piece of the supply chain.
Changes in “business as usual” are often the forced result of disruption. It’s easy to point at things like Brexit or the COVID-19 pandemic and label them as disruptors – which they are – but business models forged by break-out companies are also industry disruptors.
Consider industry giant Amazon and how it changed almost everything about consumer expectations: we now expect unlimited choice, shop-anywhere convenience, and instant shipping.
None of these would be possible without advanced logistics and sophisticated distribution systems, but Amazon made it happen, setting a new bar of what’s possible for digital native companies.
This is how the omnichannel expectation began, and it’s forcing companies to make transformations. It’s very trendy to discuss “improving customer experience” but there’s often still a disconnect between those conversations and how supply chains impact the consumer.
Heightened expectations for choice and delivery speed cannot be met without significant changes to the entire supply chain process.
The main disruption to manufacturing business-as-usual is the expectation of increased consumer control; for example, many sneaker manufacturers now offer the option of a completely customized design.
The resultant needs are ways to optimize production and create on-demand processes. These shifts have a huge impact not just on the manufacturing itself, but on any sort of predictive planning.
Some companies have shifted to manufacture on demand only to cope with this change, while some use both traditional and on-demand production. As for planning, what could once be mapped out for a few weeks at a time has shifted to a need to plan just a few days or even hours ahead.
The changing consumer landscape demands a new agility to keep up.
In fulfillment, what once was a process to manufacture and ship in bulk is now disrupted by the expectation of single-item direct-to-consumer orders, complete with expedited delivery. This necessitates new assembly lines and work processes, and impacts the workforce.
Organizations must completely rethink their workflow and start innovating. The focus is not on replacing human labor – robots still haven’t caught up to human dexterity or cognition – but human/machine collaboration to speed up processes and make more accurate predictions of market demands.
The so-called last mile of retail refers to the very end of the process that lands product in the hands of the consumer. The disruptors here are new fulfillment options and speed of fulfillment.
Twenty years ago you had to go to the store to buy what you needed. Today you can go to the store, or order online with speedy shipping, or order online and pick up at the store. This new expectation of having so many options is referred to as “anywhere capabilities,” and to accommodate them, planning becomes more complex.
A supply chain capable of meeting these anywhere capabilities requires an increased data volume to track different fulfillment methods and packages, as well as potentially needing more physical locations (greater proximity to more customers) to keep up with demand.
Any algorithms in place need to incorporate additional outside forces (such as third party shippers) as well. Products must be provided to consumers when and where they want them or they will find other retailers who will.
Finally, the disruptors to demand planning are changing consumer expectations and product customizations. The solution is greater speed and agility in forecasting, and this is where artificial intelligence and machine learning come into play.
Demand planning used to rely on sales reports, which meant using the past to predict the future. Machine learning allows for forward-looking algorithms, enabling more accuracy and the ability to adjust in real-time.
Today there is more data available than ever before, and artificial intelligence means things like social media analytics can be used to predict buying trends.
Retailers are taking a multi-pronged approach to meeting new omnichannel demands.
RFID tagging is a must for accurate tracking of inventory moving around via multiple methods. The processes designed for warehouses don’t scale well to individual orders, so RFID is a cost-effective way for suppliers to understand where their product is.
With the advances in data integration, folding RFID information into business processes is much more seamless than it used to be. The result is not just greater visibility, but reductions in costs.
More and better data usage fuels adaptation and responsiveness. Companies often struggle to unlock the value of data available to them, and it’s common for that data to be siloed rather than interconnected.
The goal in processing data is to render it as insightful and visible as possible. Most of this data is already internal to the company but not being factored into analyses in a meaningful way.
Cognitive automation harnesses the power of artificial intelligence and advanced analytics to make supply chains more accurate, agile, and cost-efficient. It’s predicted by 2023, 50% of all large global companies will be using cognitive automation within their supply chain operations.
The process begins with digitization of data across systems to create an end-to-end vision of demand. The information is then fed into algorithms that predict needed adjustments to current processes and forecasts for the future.
As results either conform to predictions or vary, this data is fed back into the system to fine-tune the algorithms. In some cases this can result in fully touchless forecasting – completely machine driven operations – and in others, human planners are brought into the loop to accept or reject suggested actions.
Not only does cognitive automation improve outcomes, it shifts the business model from people using machines to do the work to machines doing the work aided by people.
This enables critical planners within the organization to switch from constant “firefighting mode” to maintaining a system routinely addressing all issues. With the time and cognitive space freed up by this automation, workers can focus on innovation and creating the next set of disruptors.
As cognitive automation becomes the norm, the workforce is being reshaped. Instead of separate Information Technology departments and Business departments, more data scientist positions in collaborative settings will be created.
Organizational structures are fundamentally changing as companies grapple with questions like “who owns the forecast?” when that forecast is largely machine generated.
Omnichannel commerce is here to stay, and it will only become more complex.
First came the automation of the manufacturing floor, next was the automation of parts of fulfillment, and now cognitive automation is the next horizon with decision-level automation.
Whatever the next disruptor in retail turns out to be, cognitive automation will allow for response to changes in the industry, even more product customization, and customer demand.
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