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The complexity of today’s supply chain network makes data capturing a challenging endeavor. Businesses need to classify the data broadly into three dimensions: Product, Market/Channel, and Marketing. However, there’ one thing common in each of these dimensions: a problem of plenty. The puzzle of building a model based on data captured from one location-based market and reusing it in another begs an answer: Is AI for Sales and Supply Chain?
It’s time we get an answer!
Artificial Intelligence and Automation have become the need of the hour. Unfortunately, unless businesses proactively adapt to the changes, they are likely to lose the game for good. And this is not just to scare you; it is just a matter of time before you find yourself left out in the mayhem.
However, jumping onto the AI bandwagon without understanding the various complexities of businesses processes is the biggest blunder to commit. One needs a ground reality-check of applying Artificial Intelligence in logistics or any value chain for that matter.
Here, we will explore how leveraging AI can help your organization overcome data challenges that you may face across the demand value chain.
Courtesy of Artificial Intelligence, every human action leaves behind a data footprint. More often than not, human actions or decisions start following a specific pattern, which can model those decisions on reverse-engineering.
This helps businesses, for instance, forecast sales figures for their products/services in the coming quarter. As the behavioral purchasing pattern is repetitive, future forecasts become easy, right? Maybe not!
Reasons are that the market is fragmented and highly diversified, with multiple players rolling out similar products in tune with their competitors. The problem of plenty puts tremendous pressure on businesses, as they need to evolve to stay ahead in the competition continuously.
The sooner they get a new product out, the sooner other similar products replace the former. How buyers respond to them compared to what alternatives they have at their disposal only complicates the data capturing process. Businesses’ inventories are left untouched or wasted to match the competition. Here, AI inventory management software might help, but does it answer the data capturing conundrum? Hardly!
The rate at which new products are introduced in the market was hard to imagine fifteen years back. Coupled with retailers rolling out private labels and mushrooming start-ups bringing innovation on the table have compounded matters for many big brands.
Thousands of new products roll out in the market every day, making it virtually impossible to identify a new product, let alone get a perfect match for it against your product portfolio. Confusions arise when you are a global corporation competing with regional brands, a few of which are mentioned below:
Using AI inventory management might not fetch the desired outcome because AI input still needs human validation before it can be rendered useful. Moreover, this approach is not time and labor effective. Hence, the question persists – how can you make AI work for you if you fail to make AI better in the first place?
How effective will AI be, especially when more than 80% of the data needed is outside the enterprise?
Building a supply chain strategy in Vietnam and using the same in the US shouldn’t be the desired approach for multiple reasons as there’s a huge gap in size and tools used in these two markets. Besides, the rise of online shopping made measuring ‘sell-out’ versus ‘sell-in’ the new mantra for success.
Handling a complex supply chain network of local/regional suppliers and distributors became a challenge. Sales reps had the onerous task of serving the millions of new outlets through thousands of local distributors even though you had no clue what the existing distributors were doing.
This is where a strong data foundation comes into play and is undoubtedly the first step in an organization’s AI journey.
Digital marketing spending is on the rise and is accelerating at a scale more than print or television media can match.
Hence, big brands spend nearly 20% of their revenues on promotions, wherein direct marketing or consumer promotions account for another 10%. How can one measure the efficacy of marketing spending without sufficient verifiable data?
AI in logistics, inventory, and supply chain networks using multi-variate algorithms can allow you to measure the impact of these on product-specific sales and understand the impact of one over the other. But, the absence of useful data makes it difficult to map them effectively, although most retailers acknowledge the value of collaborative planning. The latter willingly provide as much visibility of their sales to their suppliers as possible.
However, the main challenge lies in the distributor network, where such asks are generally greeted with suspicion. Then again, creating business incentives for distributors or making it a condition of their distributorship might fetch valuable data, however less granular it may be.
Though a buzzword, Artificial Intelligence or AI is still nascent. And it survives only in the presence of data. Then again, building a high-quality data foundation takes time, and not all business functions are amenable to AI modeling. Added to that are other issues, like shorter product lifecycles, sales and marketing methods explosion, fragmented and complicated supply chain network, and rabid focus on personalization.
For AI in the supply chain and other value chains to work properly, one must constantly feed new data. Therefore, you might have to gradually start small, learn, and expand automation to other areas.
The question ‘Is AI ready for Sales and Supply Chain?’ depends wholly on how well your organization navigates the changing market dynamics.
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