Industry
Retail
Omnichannel CX and Intelligent Innovation – The Future of Retail
Solution
Consulting
TCS Consulting Services to Achieve Success in the Digital Era
Highlights
- With demand volatility showing no signs of abating, the lack of sophisticated forecasting systems can have a domino effect on all aspects of the supply chain.
- Anticipating disruptions, changing course fluidly, and engaging with suppliers can help retailers with dynamic management of disruptions during the holiday shopping season and beyond.
- AI-ML can identify opportunities across key supply chain dimensions to reduce the cost to serve, while enabling seamless communication with vendors to ensure product availability.
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Supply chain resilience is key for product availability
With demand volatility likely to be the biggest challenge during the holiday season, retailers will need to sharpen the focus on resilience.
While many retailers have effectively addressed the surplus inventory accumulated after the pandemic and due to the ensuing uncertainties and supply chain disruptions, they must remain vigilant in the current state of stability and avoid complacency.
While consumer confidence, geopolitical disquiet, economic uncertainty, and climate change can all very quickly disrupt supply chains, demand volatility is likely to be the biggest challenge during the holiday season.
Challenges in coping with demand volatility
A lack of sophisticated forecasting algorithms for predicting demand volatility has a domino effect on all aspects of the supply chain.
Demand volatility can be attributed to several exogenous factors. Consumers are more conscious about what they consume. Capricious trends triggered by social media influencers, combined with the more gradual and embedded behavioral changes brought about by climate change, are rapidly altering demand in all categories, from apparel to food and entertainment, impacting product availability. Even modern forecasting algorithms are not mature enough to predict consumer behavior season after season and the wild swings in demand based on innumerable stimuli and demand signals. Other factors include reliance on historical transaction data and lack of integration of external data.
Consequently, forecasts that are not accurate can have a domino effect on all aspects of the supply chain, including forecasting and logistics. All these impact the steady flow of goods and eventually results in piling of stocks.
Dynamic disruption management is key
The combination of infinite data, powerful data analytics tools, and rapidly maturing AI-ML capabilities is helping retailers take on demand volatility with confidence.
Chief supply chain officers (CSCOs) should focus on building the ability to handle disruptions dynamically and thrive in an evolving environment. This involves addressing the challenge from either end: on the one hand preempt disruptions, and on the other, infuse resilience into the supply chain to absorb disruptions to the extent possible. In other words:
- Make forecasting more intelligent to incorporate all elements of demand sensing for improved demand anticipation, demand management, and orchestrated consumption. Increase investment and commitment to machine-led demand preemption and demand shaping. This will help to directly address capacity challenges and labor shortages through improved demand anticipation, demand management, and orchestrated consumption. When demand is volatile, retailers should shape the demand based on available inventory through promotions and marketing.
- On the supply side, focus on autonomous recalibration of inventory, stock, and replenishment policies to minimize exception- driven approach to execution. If the forecast accuracy is high, a low safety stock may be adequate, but if demand is volatile, safety stock levels must be increased. Static inventory policies must give way to insights-driven, real-time, autonomous recalibration of inventory policies. This can be realized with digital supply chains that enable dynamic tuning of operational parameters such as stock levels, replenishment frequencies, delivery cycles, time window into stores, and time window out of depots based on sales velocity and demand signals, market conditions, and supplier delivery performance. Firefighting and manual reorchestration of the supply chain due to issues such as delay in shipments should be replaced with autonomous switching to alternative suppliers and routes. AI-powered solutions such as supply chain control towers offer full visibility into what is going on in every part of their operations and have built-in intelligence and alerting capabilities, allowing retailers to not only reserve firefighting for real exceptions but also operate with a relatively lean value chain.
Ace the supply chain game
Pre-empting disruptions, changing course fluidly, and engaging with suppliers closely can help retailers manage demand volatility.
Irrespective of their starting point, here are a few guidelines for retailers to navigate disruptions dynamically:
Anticipate disruptions: Supply chains typically have several moving parts. Ensuring a seamless flow of information, pre-empting disruptions, and building resilience to absorb supply chain disruptions to the greatest extent possible is now table stakes. Retailers must leverage AI-ML to anticipate all aspects of demand (from customer demand and the corresponding capacity requirements) and disruptions across the value chain (demand, supply, and flow disruptions), and cascade information to all parallel and downstream teams, avoiding firefighting at the last minute. With digital supply chains, stores can receive proactive alerts on potential delays in obtaining stock and can alert procurement teams to look for alternative suppliers.
In case of the global shipping container ecosystem, retailers can receive alerts on any customs-related delays or port congestions impacting expected time of arrival (ETA) and get a prioritized list of containers for pickup based on business demand, system constraints, and costs.
Change course fluidly: Apply machine-led insights to develop a forward-looking and technology-led system of autonomous interventions and course corrections for seamless execution. For instance, in the event of a disruption in supply from one supplier for a particular product, digital supply chains should autonomously identify and shift demand to the nearest alternative and amend orders and product flows to correspond to the new product. This requires retailers to gain full visibility into partners’ capabilities, capacity, and performance.
Engage with suppliers: With the growing realization about the critical role that suppliers play in ensuring resilient supply chains and customer experience, there is a dire need to bring them into the retailers' ecosystem for better collaboration, for better synergies in handling disruption, and for speeding innovation. The enterprise as well as external customers must be made a tighter part of the supply chain through proactive visibility, forward appraisement of changes, and a transparent approach to the relationship. Machine-led control towers within the enterprise are the need of the hour with the ability to provide 360-degree visibility, pre-emptive alerts, and actionable insights to ensure that flow of products is disruption free.
Focus on key dimensions for supply chain resiliency
AI-ML and robotic process automation (RPA) can help identify opportunities to reduce cost-to-serve and improve process efficiency.
By transforming three core dimensions of supply chains with new age technologies, retailers can unlock significant outcomes across all operations:
- Network optimization: Storage and carriage costs tend to be a sizable proportion of total revenue, and leakages in the network can quickly add up to a lot. Network optimization ensures all the nodes in the network are optimally aligned, there is full visibility to all movements and associated costs, and systemic capability for pre-emptive alerts and course correction as required. This gives CSCOs greater control over storage and carriage costs and access to AI-driven dynamic simulation tools to address these costs as required. For example, digital twins can give a simulated and capital-free view of storage capacity and pick-and-put-away paths.
- Operations optimization: The core elements of the network are supported by an array of often invisible operations and business processes that are critical to ensure seamless and uninterrupted execution. Deliberate focus is required to ensure these supporting operations are streamlined and periodically trimmed to pre-empt points of failure, reduce over dependency, and avoid unnecessary bloat of invisible cost. As more data becomes available, retailers have an opportunity to track additional KPI. For example, truck drivers may not leave the yard as soon as the truck is unloaded. Now, it is possible to track the ‘in time’, ‘docking-at-gate time’, ‘time when completely unloaded’ and ‘out time’. Based on a supplier score generated, the retailer can renew or renegotiate supplier contracts.
- Systems execution: Augments the above by creating the foundation of data and information that is required for continuous network optimization, proactive network orchestration, and realizing cost and operational efficiencies. Today, retailers can build the technical capability to store and process data with technologies such as cloud and data analytics platforms. The challenge lies in identifying data sets needed to generate insights, upgrading systems to capture transactional data, and managing the people aspect of change.
Concurrent optimization of KPIs across the value chain with AI
Reducing the cost of operations is the primary focus in today’s macroeconomic environment of wafer-thin margins, and supply and demand volatility. With advanced analytics, retailers can concurrently optimize multiple KPIs for optimal results.
Let us assume that based on the weekly demand forecast, a store places a replenishment order for eight case packs of cereals based on their on-hand availability and storage capacity without factoring DC operations such as picking, packing, and loading capacity among others. Similarly, when DCs ship to store, they generally do not consider the fill rate of the trucks. This siloed approach often results in suboptimization across the value chain.
AI can concurrently optimize multiple KPIs across stores, DCs, and transportation, balancing tradeoffs between product availability, cost, and CX, and recommend the appropriate replenishment quantity. In this example, AI recommends the store to place two replenishment orders, the first for four case packs and the next order mid-week based on up-to-date demand, thereby improving availability and customer experience while reducing cost-to-serve.
Making the most of the holiday season
Rather than taking a piecemeal approach to build supply chain resilience, retailers need to take a unified value chain approach.
The environment in which businesses are operating is becoming complex. Externally businesses are fragmented into online, domestic, international; internally businesses are reorganizing as multiple entities and brands. Customers have become complex: shopping online, in stores, on social media, in marketplaces, or at franchisees. Multiple complex business models also exist.
Being vulnerable to disruptions can impact product availability and also result in higher cost to serve, poor customer experience and lost sales, notwithstanding reputational damage. Taking a unified value chain approach can help retailers facilitate smooth collaboration with external partners for improving supply chain resilience. This will help reduce the overall supply chain cost, improve forecast accuracy and product availability, helping retailers improve sales and gross margins and make the most of the holiday season.
