Uncovering Complexities Impacting Actionable Visibility Across Retail Supply Chains
Uncovering Complexities Impacting Actionable Visibility Across Retail Supply Chains
Retailers face several layers of operational and tech challenges that hinder true end-to-end visibility. Data harmonization and improved collaboration with partners could be the answer.
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The last few months have been absolute cinema within the logistics industry, with stakeholders finding themselves amidst contrasting fortunes based on the freight modality they look to navigate. While the Red Sea crisis helped siphon off ‘excess capacity’ brought into the maritime market over the last couple of years, the US trucking market hasn’t seen such reprieve.
A hot maritime market has meant higher trans-Atlantic and trans-Pacific costs for importers — a chunk of which are retailers sourcing from China and SE Asia to fill up inventories in preparation for the approaching consumer peak season. Being a high-volume, low-margin business, the retail industry has much to lose with the volatility in the freight market, as it gets harder to anticipate rates and plan annual freight budgets.
That said, logistics is not the only challenge facing retailers. They also have to contend with the demand problem. Forecasting consumer demand is notoriously tricky, especially since the advent of social commerce. People are increasingly buying based on social trends, which spikes demand for certain product verticals — leaving retailers unprepared.
Forecasting consumer demand is notoriously tricky, especially since the advent of social commerce.
“If forecasts were entirely accurate, there would be no variability to address in production and supply to meet that forecast. Unfortunately, forecasts are rarely precise,” said Jason Tham, the co-founder and CEO of Nulogy, a supply chain collaboration platform. “Forecast accuracy has traditionally been off, but demand driven by social media influencers can sway inaccuracies even further.”
Machine learning solutions have helped bridge the gap by incorporating not just seasonality and patterns, but also ambient or environmental data factors into forecasting models. Besides forecasting demand, AI tools can help with outbound operations, suggesting buffer stock, or identifying risks in the supply chain. Using this data, shippers can meet on-time, in-full (OTIF) deadlines, and retailers can ensure they stay in stock when the orders pour in.
Regardless, Tham pointed out forecasting could only take companies so far. “Agility in supply chains often trumps forecasting. For instance, the cosmetic industry could see dramatic demand spikes due to fashion influencers, leading to unpredictability. The key is building agility into the supply chain to respond to such demand quickly,” he said.
“The opposite of agile is fragile. A fragile system is unpredictable and non-standardized, making it difficult to forecast or collaborate effectively. Companies must partner and synchronize data to build agility, reducing entropy within their supply network. High entropy leads to fragility, while reducing entropy through standardization and collaboration drives agility.”
In here, synchronizing data could be a major roadblock. While forecasting models and attempts at collaboration bank on external factors, companies suffer internally from ‘too much’ sophistication in the context of technology adoption. SMEs and larger organizations are especially prone to such complexity, as they invest in several tech solutions that bloat up their tech stack. For instance, it is not uncommon to see companies run dozens of ERPs across various departments, functions, and business units, leading to data silos and integration challenges.
While forecasting models and attempts at collaboration bank on external factors, companies suffer internally from ‘too much’ sophistication in the context of technology adoption.
“Companies also have numerous other systems aside from ERPs — like TMS, WMS, planning systems, forecasting systems, and witness even more heterogeneity when working with external supply chain partners like manufacturers, 3PLs, and contract packagers,” said Tham. “Companies increasingly outsource and specialize, focusing only on what they do best and relying on external solution providers for other segments of the supply chain.”
While technology is supposed to help, the data streams are not interoperable. Piecemeal solutions from different providers mean data streams do not adhere to the same standards, measuring and collating data across different formats. This is frustrating to companies as they are left with different pieces to the puzzle, but lack the wherewithal to put them together for consolidated insights.
“Despite all the technology, there’s still a lot of manual work, guessing, and latent work involving emails and spreadsheets when working with partners,” contended Tham. “Automated processes and common data models for interacting, transacting, and collaborating are infrequent. Without standardized data formats, it’s challenging to drive intelligence, make better decisions, and use AI or ML effectively with cleaner, high-fidelity, and timely data.”
In this context, data streams involve a mix of spreadsheets, ERPs, and homegrown systems — both internal and external. This ‘many-to-many’ network creates complexity. Tham explained that Nulogy was founded to solve this problem, by focusing on simplifying this complexity by driving standardization and synchronization across the heterogeneous network, to usher homogeneity to it.
For instance, think of a large retailer with hundreds of contract suppliers managing packaging, production, and shipment. This would require them to oversee inventory, production schedules, and plan capacity based on data coming in from different systems and manual processes, making end-to-end consolidated visibility the weakest link. Nulogy brings in this visibility, enabling companies to see and manage their external supply chain as if it were their own.
“There’s a saying in supply chain management that companies don’t compete, but their supply chains do. This means that even if a company has its data in order, it cannot effectively compete if it lacks visibility into its partners’ demands and supply signals,” said Tham. “Moving from this manual reactive state to a more automated ecosystem involves enrolling and activating suppliers and working closely with them.”
Even if a company has its data in order, it cannot effectively compete if it lacks visibility into its partners’ demands and supply signals.
Think of supply chain operations and partner collaboration akin to a LinkedIn network. Besides direct connections on the platform, people are also privy to intersecting communities that are one or two steps removed from their primary network. Similarly, the ‘social supply chain’ extends beyond a retailer’s direct partners to the ones in the second or third tier, alongside potential partners-to-be. The broader visibility builds agility, resilience, and reduces risk.
“Sharing information freely with supply chain partners, including forecasts and downstream requirements, and having a common data model allows for an end-to-end view of the supply chain, enabling better prediction and response to changes. This reduces the bullwhip effect, where delayed or inaccurate information leads to inefficiencies,” said Tham. “High-fidelity, granular data is crucial. Successful supply chains win together, not as individual companies.”
The Week in Snippets
Tighter regulations on US e-commerce imports from China could significantly impact the global air freight sector, warns IATA. Increased scrutiny on de minimis rules might dampen demand, especially on the Asia-North America trade lane. With e-retail sales projected to reach $6.3 trillion this year, any regulatory changes could challenge the robust growth experienced by air cargo, fueled by booming e-commerce and capacity constraints in container shipping.
Vessel backups similar to those seen during the COVID pandemic are making a return as Red Sea diversions are causing gridlock and soaring costs. Ports in Asia and Europe face growing congestion, with circumvention around Africa extending voyage times. Rates from Asia to the U.S. East Coast have reached $7,000, significantly higher than the normal $3,500, complicating logistics for importers and exporters.
Canadian National and Canadian Pacific Kansas City Southern workers, represented by the Teamsters Canada Rail Conference, have voted overwhelmingly in favor of striking unless a new labor deal is reached. Over 9,200 workers voted, with 98.6% supporting the strike. The union seeks better wages, improved working conditions, and flexibility in fatigue management. Talks with the railroads began in November, but no agreement seems to be on the horizon.
MSC now holds a record 20% share of the global container shipping market, driven by fleet growth through new building, second-hand acquisitions, and chartering. MSC's capacity surpassed 5 million TEU last year. THE Alliance carriers have an 11.6% market share, Gemini Cooperation (Maersk and Hapag-Lloyd) 22%, and Ocean Alliance nearly 29%. With the 2M Alliance with Maersk ending, MSC will operate independently but may enter selective vessel-sharing agreements.
Quotable
“If you come to us in October and ask for extra capacity, our answer to you will probably be no.”
- Tim Scharwath, chief executive of DHL Global Forwarding, while commenting on the need for shippers to sign air freight contracts as soon as possible, amidst rising prices.
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