Vivek Wikhe, Domain Expert of Retail and Supply Chain at LatentView Analytics, discusses the future of the supply chain industry and the effect digital transformation is having on the sector
Why must companies rethink their supply chain strategies in the age of digital transformation?
There are major cascading factors contributing to companies rethinking their supply chain strategies in the age of digital transformation. First, the demand side has changed rapidly. Today, there are more channels and touchpoints than ever before, which all serve different needs along the customer journey. This has resulted in a migration away from the way that demand has traditionally been generated. Invariably, it is the ability to service and optimize these new channels that allows companies to differentiate and gain a competitive edge. Organizations are no longer sure of the costs and margins in each channel that touches consumers, and are still figuring out which channels they need to service and promote in the digital era. Ensuring profitable margins across channels requires a well thought out supply chain strategy according to a company’s customer base and an optimized channel mix. Ultimately, all organizations across industries must rethink their supply chain strategies as the digital era continues moving towards the diversification of channels.
What are some immediate steps that need to be taken in order for companies to maximize profitability in their supply chains?
Buying behavior is moving towards more nebulous attribute-based purchases. Instead of consumers focusing on a specific brand, which is easier to predict based on demographics, (for example, purchasing Nike sneakers), they will typically begin their shopping journey by searching online for certain attributes and features that they want (“stylish white sneakers”).
Organizations need to tune their supply chains to reflect this shift. Instead of serving a target market based on demographics, supply chains must take into account a larger market brought about by the digital era. Supply chains should evolve to fulfillment chains, which can serve multiple channels profitably. The first step to maximizing profitability is to get a clear picture of order costs incurred in every channel. This is a complex problem with multiple, co-dependent factors. It gets complex because the costs need to be predicted to ensure an enterprise has a profitable order fulfillment scenario. The analysis of the cost structure and visibility to them is the first step to maximize profitability for supply chains.
What are the challenges that enterprises face as they move to digitize their supply chain, and what are a few best practices to overcome these challenges?
The main challenge is that due to the changing nature of modern consumer supply and demand, supply chains need to get increasingly more agile and more in tune with short-term planning. Even traditional industries need to stay abreast of quickly developing consumer trends and desires. For example, food and groceries are a traditional and staple category. However, today, there are trends in food that pop up quickly, giving traditional consumer buying behavior a very short-term strength. Many categories overall are moving towards the shorter-term life cycles, and enterprises need to move to reflect that as well, and become leaner and more agile.
How does having better data strategy create greater supply chain efficiency?
So much of demand is influenced by what consumers are seeing online - you essentially can predict what consumers are going to buy by having strong insights into data on what influences customer behavior. For example, a few years back, Amazon became famous for predicting demand. In fact, they were so good at it, that they were shipping goods before the customer even purchased them.
All companies need to have a view of the latest technology for predicting customers purchasing behavior. As buying cycles continue to grow shorter, there is no longer time to procure and supply a product without advance preparation. Ultimately, in order to not miss out on profitable opportunities, and to have a more focused organization of the supply chain, a modernized data strategy that involves predictive analytics for both the supply and demand sides is necessary. A ‘better’ data strategy is one where enterprises have a single view of all data points and these are integrated to respond in sync with unit changes. An integrated data strategy helps move the fulfillment chain in three phases - increasing visibility thereby reducing variability and finally increasing velocity. All these three phases require a different yet integrated data strategy.
As enterprises continue through their digital transformation journeys, how are innovations in AI and predictive technologies specifically playing a role?
Most enterprises on digital transformation journeys go through several stages, as they learn to apply machine learning and artificial intelligence. These stages are: descriptive, prescriptive, and predictive. In the first, you can only see what the data does, and it can help inform decision-making processes. In the second stage, you can employ an AI technology to gain prescriptive intelligence to solve specific problems or gain insight into definitive opportunities - for example, AI can identify demand per channel, or identify which models are the most profitable. In the third and final stage, you reach an exalted state of sorts wherein the ability to predict trends in the data becomes so accurate that it’s possible to preempt action around the insights. This final stage will lead to a much more focused and streamlined supply chain, and allow for comprehensive preemptive planning for all relevant supply and demand factors.
Are there any particular industries which have the best opportunity to gain a competitive advantage by adopting this technology before the rest of their peers?
I can’t think of any industry that should not be investing in emerging technology solutions. In fact, it is no longer really a question of competitive edge, but rather of survival. If you’re not investing in emerging technology and at least exploring opportunities with AI, you’re making yourself vulnerable to other companies in the field that may have higher efficiency and greater analytical abilities (and thus a greater competitive advantage) in their supply chain.
What do you see as the biggest trends going forward related to emerging technology in AI and the supply chain?
Going forward, I see a number of ways that emerging technology will continue to influence the supply chain. The next step in using data in the supply chain will be merging all sources of customer data, including social media data. Down the line, we’ll be looking at more IoT data. In coming years, we expect to see the rise of the intelligent home assistant as the first point of understanding consumers and the supply side. Information on demand signals will no longer be coming directly from consumer data, but rather personal assistants inside the home.
On the logistical side, I expect we’ll also be seeing a greater ability to deal with smaller markets. Once analytics helps optimize supply chains to a greater degree, things such as home delivery models will become profitable, even for smaller markets and chains. The overwhelming trend will be intelligent assistants embedded in various enterprise chains interacting with each other to ensure regular chores are carried out without constant human intervention
Are there any recent projects LatentView Analytics has worked on related to supply chain analytics that you can discuss?
Currently we’re working on several interesting projects. We’re helping some big name retailers understand how in an omni-channel environment they can understand their net cost for every consumer channel. There are certain aspects where it becomes not just a supply chain solution. Once you understand the optimal channel mix, you also have to take into account downstream promotion, and make the data actionable and profitable.
We’re also doing some work in supply chain and predictive analytics. In the US market, over the past two years, there have been more occurrences of incorrect delivery windows, due to shortages of supply. This creates both a greater cost to the company, as well as operational inefficiency. We’re now looking at a predictive model that compiles and analyses data to help more accurately predict arrival times of packages for consumers.