Exclusive Q&A with LatentView Analytics
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 companies in the sector.
1. 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.
2. 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.
3. 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 constant 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.
4. 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.
5. 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.
6. Are any industries particularly early adopters of AI and other emerging technology in the supply chain? Do any industries stand out as still having the 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 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.
When you look at companies like Amazon - a supply chain company - they’re constantly innovating and looking for growth opportunities. Amazon was finding it difficult to crack the grocery chain, a more traditional industry. With their acquisition of Whole Foods, they simultaneously gained a new distribution and demand channel to supplement their supply chain in this field. Even for organizations not at the scale of Amazon, it’s important to consider how analytics and intelligence can inform decisions across the supply chain, for both the digital and traditional channels.
7. 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
8. Are there any recent projects LatentView Analytics has worked on related to supply chain analytics, relevant to this audience 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 analyzes data to help more accurately predict arrival times of packages for consumers.
By Vivek Wikhe, Domain Expert - Retail and Supply Chain, LatentView Analytics
NTT DATA Services, Remodelling Supply Chains for Resilience
Joey Dean, the man with the coolest name ever and Managing Director in the healthcare consulting practice for NTT DATA and is focused on delivering workplace transformation and enabling the future workforce for healthcare providers. Dean also leads client innovation programs to enhance service delivery and business outcomes for clients.
The pandemic has shifted priorities and created opportunities to do things differently, and companies are now looking to build more resilient supply chains, none needed more urgently than those within the healthcare system. Dean shares with us how he feels they can get there.
A Multi-Vendor Sourcing Approach
“Healthcare systems cannot afford delays in the supply chain when there are lives at stake. Healthcare procurement teams are looking at multi-vendor sourcing strategies, stockpiling more inventory, and ways to use data and AI to have a predictive view into the future and drive greater efficiency.
“The priority should be to shore up procurement channels and re-evaluate inventory management norms, i.e. stockpiling for assurance. Health systems should take the opportunity to renegotiate with their current vendors and broaden the supplier channel. Through those efforts, work with suppliers that have greater geographic diversity and transparency around manufacturing data, process, and continuity plans,” says Dean.
But here ensues the never-ending battle of domestic vs global supply chains. As I see it, domestic sourcing limits the high-risk exposure related to offshore sourcing— Canada’s issue with importing the vaccine is a good example of that. So, of course, I had to ask, for lifesaving products, is building domestic capabilities an option that is being considered?
“Domestic supply chains are sparse or have a high dependence on overseas centres for parts and raw materials. There are measures being discussed from a legislative perspective to drive more domestic sourcing, and there will need to be a concerted effort by Western countries through a mix of investments and financial incentives,” Dean explains.
Wielding Big Tech for Better Outcomes
So, that’s a long way off. In the meantime, leveraging technology is another way to mitigate the risks that lie within global supply chains while decreasing costs and improving quality. Dean expands on the potential of blockchain and AI in the industry.
“Blockchain is particularly interesting in creating more transparency and visibility across all supply chain activities. Organisations can create a decentralised record of all transactions to track assets from production to delivery or use by end-user. This increased supply chain transparency provides more visibility to both buyers and suppliers to resolve disputes and build more trusting relationships. Another benefit is that the validation of data is more efficient to prioritise time on the delivery of goods and services to reduce cost and improve quality.
“Artificial Intelligence and Machine Learning (AI/ML) is another area where there’s incredible value in processing massive amounts of data to aggregate and normalise the data to produce proactive recommendations on actions to improve the speed and cost-efficiency of the supply chain.”
Evolving Procurement Models
From asking more of suppliers to beefing up stocks, Dean believes procurement models should be remodelled to favour resilience, mitigate risk and ensure the needs of the customer are kept in view.
“The bottom line is that healthcare systems are expecting more from their suppliers. While transactional approaches focused solely on price and transactions have been the norm, collaborative relationships, where the buyer and supplier establish mutual objectives and outcomes, drives a trusting and transparent relationship. Healthcare systems are also looking to multi-vendor strategies to mitigate risk, so it is imperative for suppliers to stand out and embrace evolving procurement models.
“Healthcare systems are looking at partners that can establish domestic centres for supplies to mitigate the risks of having ‘all of their eggs’ in overseas locations. Suppliers should look to perform a strategic evaluation review that includes a distribution network analysis and distribution footprint review to understand cost, service, flexibility, and risks. Included in that strategy should be a “voice of the customer” assessment to understand current pain points and needs of customers.”
“Healthcare supply chain leaders are re-evaluating the Just In Time (JIT) model with supplies delivered on a regular basis. The approach does not require an investment in infrastructure but leaves organisations open to risk of disruption. Having domestic centres and warehousing from suppliers gives healthcare systems the ability to have inventory on hand without having to invest in their own infrastructure. Also, in the spirit of transparency, having predictive views into inventory levels can help enable better decision making from both sides.”
But, again, I had to ask, what about the risks and associated costs that come with higher inventory levels, such as expired product if there isn’t fast enough turnover, tying up cash flow, warehousing and inventory management costs?
“In the current supply chain environment, it is advisable for buyers to carry an in-house inventory on a just-in-time basis, while suppliers take a just-in-case approach, preserving capacity for surges, retaining safety stock, and building rapid replenishment channels for restock. But the risk of expired product is very real. This could be curbed with better data intelligence and improved technology that could forecast surges and predictively automate future supply needs. In this way, ordering would be more data-driven and rationalised to align with anticipated surges. Further adoption of data and intelligence and will be crucial for modernised buying in the new normal.
These are tough tasks, so I asked Dean to speak to some of the challenges. Luckily, he’s a patient guy with a lot to say.
On managing stakeholders and ensuring alignment on priorities and objectives, Dean says, “In order for managing stakeholders to stay aligned on priorities, they’ll need more transparency and collaborative win-win business relationships in which both healthcare systems and medical device manufacturers are equally committed to each other’s success. On the healthcare side, they need to understand where parts and products are manufactured to perform more predictive data and analytics for forecasting and planning efforts. And the manufacturers should offer more data transparency which will result in better planning and forecasting to navigate the ebbs and flows and enable better decision-making by healthcare systems.
Due to the sensitive nature of the information being requested, the effort to increase visibility is typically met with a lot of reluctance and push back. Dean essentially puts the onus back on suppliers to get with the times. “Traditionally, the relationships between buyers and suppliers are transactional, based only on the transaction between the two parties: what is the supplier providing, at what cost, and for what length of time. The relationship begins and ends there. The tide is shifting, and buyers expect more from their suppliers, especially given what the pandemic exposed around the fragility of the supply chain. The suppliers that get ahead of this will not only reap the benefits of improved relationships, but they will be able to take action on insights derived from greater visibility to manage risks more effectively.”
He offers a final tip. “A first step in enabling a supply chain data exchange is to make sure partners and buyers are aware of the conditions throughout the supply chain based on real-time data to enable predictive views into delays and disruptions. With well understand data sets, both parties can respond more effectively and work together when disruptions occur.”
As for where supply chain is heading, Dean says, “Moving forward, we’ll continue to see a shift toward Robotic Process Automation (RPA), Artificial Intelligence (AI), and advanced analytics to optimise the supply chain. The pandemic, as it has done in many other industries, will accelerate the move to digital, with the benefits of improving efficiency, visibility, and error rate. AI can consume enormous amounts of data to drive real-time pattern detection and mitigate risk from global disruptive events.”