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Covid liquidity pressures place supply chain finance in the driving seat

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Covid liquidity pressures place supply chain finance in the driving seat

The case for supply chain finance is as strong as ever

Not only did shipping and air freight supply chains come to a halt during the early days of the pandemic, but consumer demand also went through a slump. As a long-term consequence, supply chains have experienced strain, centered on working capital and ensuring business continuity across industry segments.

Today, the challenge is about demand, which exceeds timely supply, placing additional operational pressures on these businesses. This means supply chains are forced to stretch their working capital and make changes to how they finance and sustain their businesses.

According to the World Bank, there is a finance gap of about $5.2-trillion globally — wider in emerging markets where the availability of working capital has been limited or the understanding largely undervalued. As a result, we have experienced many product shortages, a prime example of how buyers and suppliers are facing the challenge to ensure the smooth exchange of products along the value chain.

Finance plays a big role in this continuity and in SA. While we lagged global markets in the adoption of supply chain finance models initially, the pandemic has strengthened the need for it. There has been a rising demand in supply chain finance locally — or reverse factoring as it’s commonly known — with some of the world’s largest businesses turning to this financing to help suppliers optimise their working capital.

However, supply chain finance is not a new concept. Globally, it has been used as a source of capital by many corporates as an alternative funding model to free up cash flow without affecting existing lending facilities.

Supply chain finance plays a pivotal role in markets in a state of flux, ensuring there is speed and efficiency in the payment cycle. Typically, a third-party finance provider will pay a buyer’s debt to the supplier at a discounted rate and much sooner than the buyer is able to do so if done directly.

This facilitates a positive cash flow for the business through the working capital cycle and ensures both buyer and supplier are better able to meet demand vs supply without the red tape of cash flow challenges typically experienced in a recovering market. It gives the buyer time to streamline cash flow, based on creditor cycles, where they pay the finance provider at a later date, allowing them room to ensure solid cash flow and build positive relationships with their suppliers.

It also offers a competitive advantage for the buyer and a financially savvy opportunity for the supplier to take advantage of mechanisms for early settlements and the related discounts that may apply.

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How a Unified Logistics Approach Drives the Customer-Centric Supply Chain

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How a Unified Logistics Approach Drives the Customer-Centric Supply Chain

How a Unified Logistics Approach Drives the Customer-Centric Supply Chain

No matter the hurdles of 2020, Logistics was up for the challenge. It kept production running and critical supplies flowing while adjusting to the shocks in demand and supply patterns and delivering essential goods.

The industry has already begun its transformation into a pull paradigm. To adjust to a new logistics footprint, operations are catering to smaller and more frequent shipments, while increasing its stronghold in eCommerce. With shippers and Logistics Service Providers (LSPs) attempting to increase their capabilities for market share gain, gone will be reactive bulk handing, serial execution, and long planning cycles.

Now it’s time for logistics to up its game — again.

How can it morph from inside-out, efficiency-focused to a model that’s outside-in and centered around the customer experience? We believe the future of logistics is unified logistics, where shippers and LSPs can seamlessly plan, optimize, and orchestrate across nodes and networks, resulting in consistently higher customer service levels and efficiencies.

Let’s discuss the aspects that make unified logistics a reality.

Boundaryless Orchestration

Existing logistics systems are usually configured with static, pre-setup actions, and often lack advanced visibility. Even if visibility exists, the functionality does not allow timely execution. Warehouse systems may not be able to consider transportation information and vice versa.

Upstream Supply Chain Knowledge

Traditionally, transportation systems lack order visibility and updated supply chain plan information. In the warehouse, traditional distribution and fulfillment operations rely on aggregate and longer-term forecasts to plan labor schedules. The inventory positions in warehouse systems are determined by historical patterns and longer-term forecasts, causing operations to be reactive.

Digital Ecosystem and Network

The historic approach to collaboration and point-to-point integration won’t create an easy path to real-time communications for carriers and LSPs. Now with access to the digital network, shippers can tap into carrier networks, take capacity into considerations for order promising, and select last-mile delivery providers. Before, the carrier selection process was highly manual and used static rates, and now shippers can perform Dynamic Price Discovery to view freight rate quotes from carrier marketplaces.

Unified Logistics, powered by our Luminate Logistics and Luminate Platform solutions, arms shippers and LSPs with the ability to seamlessly plan, optimize, and orchestrate supply chain execution. They can gain consumer confidence by truly delivering the right product, to the right place, at the right time — even if the lot size is small.

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How To Improve Supply Chains With Machine Learning: 10 Proven Ways

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Bottom line: Enterprises are attaining double-digit improvements in forecast error rates, demand planning productivity, cost reductions and on-time shipments using machine learning today, revolutionizing supply chain management in the process.

Machine learning algorithms and the models they’re based on excel at finding anomalies, patterns and predictive insights in large data sets. Many supply chain challenges are time, cost and resource constraint-based, making machine learning an ideal technology to solve them. From Amazon’s Kiva robotics relying on machine learning to improve accuracy, speed and scale to DHL relying on AI and machine learning to power their Predictive Network Management system that analyzes 58 different parameters of internal data to identify the top factors influencing shipment delays, machine learning is defining the next generation of supply chain management. Gartner predicts that by 2020, 95% of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions. Gartner is also predicting by 2023 intelligent algorithms, and AI techniques will be an embedded or augmented component across 25% of all supply chain technology solutions.

The ten ways that machine learning is revolutionizing supply chain management include:

  1. Machine learning-based algorithms are the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems.
  2. The wide variation in data sets generated from the Internet of Things (IoT) sensors, telematics, intelligent transport systems, and traffic data have the potential to deliver the most value to improving supply chains by using machine learning.
  3. Machine learning shows the potential to reduce logistics costs by finding patterns in track-and-trace data captured using IoT-enabled sensors, contributing to $6M in annual savings.
  4. Reducing forecast errors up to 50% is achievable using machine learning-based techniques.
  5. DHL Research is finding that machine learning enables logistics and supply chain operations to optimize capacity utilization, improve customer experience, reduce risk, and create new business models.
  6. Detecting and acting on inconsistent supplier quality levels and deliveries using machine learning-based applications is an area manufacturers are investing in today.
  7. Reducing risk and the potential for fraud, while improving the product and process quality based on insights gained from machine learning is forcing inspection’s inflection point across supply chains today.
  8. Machine learning is making rapid gains in end-to-end supply chain visibility possible, providing predictive and prescriptive insights that are helping companies react faster than before.
  9. Machine learning is proving to be foundational for thwarting privileged credential abuse which is the leading cause of security breaches across global supply chains.
  10. Capitalizing on machine learning to predict preventative maintenance for freight and logistics machinery based on IoT data is improving asset utilization and reducing operating costs.

Read more at How To Improve Supply Chains With Machine Learning: 10 Proven Ways

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10 Ways Machine Learning Is Revolutionizing Supply Chain Management

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Machine learning makes it possible to discover patterns in supply chain data by relying on algorithms that quickly pinpoint the most influential factors to a supply networks’ success, while constantly learning in the process.

Discovering new patterns in supply chain data has the potential to revolutionize any business. Machine learning algorithms are finding these new patterns in supply chain data daily, without needing manual intervention or the definition of taxonomy to guide the analysis. The algorithms iteratively query data with many using constraint-based modeling to find the core set of factors with the greatest predictive accuracy. Key factors influencing inventory levels, supplier quality, demand forecasting, procure-to-pay, order-to-cash, production planning, transportation management and more are becoming known for the first time. New knowledge and insights from machine learning are revolutionizing supply chain management as a result.

The ten ways machine learning is revolutionizing supply chain management include:

  1. Machine learning algorithms and the apps running them are capable of analyzing large, diverse data sets fast, improving demand forecasting accuracy.
  2. Reducing freight costs, improving supplier delivery performance, and minimizing supplier risk are three of the many benefits machine learning is providing in collaborative supply chain networks.
  3. Machine Learning and its core constructs are ideally suited for providing insights into improving supply chain management performance not available from previous technologies.
  4. Machine learning excels at visual pattern recognition, opening up many potential applications in physical inspection and maintenance of physical assets across an entire supply chain network.
  5. Gaining greater contextual intelligence using machine learning combined with related technologies across supply chain operations translates into lower inventory and operations costs and quicker response times to customers.
  6. Forecasting demand for new products including the causal factors that most drive new sales is an area machine learning is being applied to today with strong results.
  7. Companies are extending the life of key supply chain assets including machinery, engines, transportation and warehouse equipment by finding new patterns in usage data collected via IoT sensors.
  8. Improving supplier quality management and compliance by finding patterns in suppliers’ quality levels and creating track-and-trace data hierarchies for each supplier, unassisted.
  9. Machine learning is improving production planning and factory scheduling accuracy by taking into account multiple constraints and optimizing for each.
  10. Combining machine learning with advanced analytics, IoT sensors, and real-time monitoring is providing end-to-end visibility across many supply chains for the first time.

Read more at 10 Ways Machine Learning Is Revolutionizing Supply Chain Management

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The secret to making customers care about supply chain

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Imagine a world where customers care about how products are sourced, made, and delivered, understand what goes into pricing, and generally take great joy in the experience. A world where customers are fluent in the language of supply chain.

It’s not as farfetched as you may think.

Supply chains solve complex problems. And in the company of supply chain professionals, we use big words and complicated terms to talk about it. Words like multi-modal logistics and global transportation, mass-customisation and postponement, procurement and letters of credit, demand management, the cost of inventory and buffer stock, assurance of supply, warehousing, and the last mile.

We nitpick over the differences between distribution and fulfilment centres, debate the true definition of supply chain visibility and the role of control towers to support orchestration across a complex network of suppliers, trading partners, and carriers. And we’re still not sure if our industries are facing an apocalypse or simply working through the growing pains of transformation in the digital age.

It’s a mouthful. And as we dive into the technical details and jargon that comprise the modern language of supply chain, one can’t help but picture the average consumer’s eyes glazing over.

But that’s not necessarily the case. There’s mounting evidence people care more about supply chain than ever – they’re just not using our words for it.

Therein lies the secret.

The words used to describe supply chain were different at the recent Shoptalk Europe conference in Copenhagen, Denmark, a gathering of more than 2,500 retailers, start-ups, technologists, and investors all focused on the worlds of retail, fashion, and ecommerce. Though most attendees weren’t purely in the business of operations and supply chain, all were exploring how to reach, engage, and enlighten the customer wherever and whenever they might choose to shop.

Read more at Comment: The secret to making customers care about supply chain

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Big data analytics technology: disruptive and important?

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Of all the disruptive technologies we track, big data analytics is the biggest. It’s also among the haziest in terms of what it really means to supply chain. In fact, its importance seems more to reflect the assumed convergence of trends for massively increasing amounts of data and ever faster analytical methods for crunching that data. In other words, the 81percent of all supply chain executives surveyed who say big data analytics is ‘disruptive and important’ are likely just assuming it’s big rather than knowing first-hand.

Does this mean we’re all being fooled? Not at all. In fact, the analogy of eating an elephant is probably fair since there are at least two things we can count on: we can’t swallow it all in one bite, and no matter where we start, we’ll be eating for a long time.

So, dig in!

Getting better at everything

Searching SCM World’s content library for ‘big data analytics’ turns up more than 1,200 citations. The first screen alone includes examples for spend analytics, customer service performance, manufacturing variability, logistics optimisation, consumer demand forecasting and supply chain risk management.

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Is Flowcasting the Supply Chain Only for the Few?

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Flowcasting has often been referred to as ‘the Holy Grail’ of demand driven supply chain planning (and rightly so).

Driving the entire supply chain across multiple enterprises from sales at the store shelf right back to the factory.

So is Flowcasting a retail solution or a manufacturing solution? Many analysts, consultants and solution providers have been positioning Flowcasting as a solution for manufacturers.

They’re wrong.

While it’s true that some manufacturers have achieved success in using data from retailers to help improve and stabilize their production schedule, the simple fact is that manufacturers can’t achieve huge benefits from Flowcasting until they are planning a critical mass of retail stores and DCs where their products are sold and distributed.

For a large consumer packaged goods manufacturer, this means collecting data and planning demand and supply across tens of thousands of stores across multiple retail organizations, all of which have their own ways of managing their internal processes.

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Capitalizing on Cross-Docking

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Today’s marketplace is moving faster than ever, and companies are challenged to distribute their products more quickly, efficiently and cost-effectively; cross-docking can be a useful tool to help keep pace with customer demand.

While cross-docking is not a new phenomenon, this process of moving material from the receiving dock straight to the shipping dock is gaining traction as more companies recognize its value in today’s competitive business environment.

Why Cross-Dock?
Companies choose to cross-dock for a variety of reasons.

Common benefits include:

Increased speed to market – With high turn rates and reduced handling, cross-docking helps to increase efficiency and get products to market faster. While typically associated with durable goods, cross-docking can be effective for temperature-controlled, perishable and high-value/high-security products as well, thanks to its high velocity.

Reduced costs – Cross-docking requires a smaller footprint than traditional warehousing and often utilizes less labor as well. The practice also eliminates the cost of inventory and product rotation. Considerable freight savings can be achieved by consolidating LTL shipments into full loads.

Improved service levels – Because product is shipped in bulk and picked at the cross-dock, the practice offers great flexibility for changes to orders further down the supply chain. This helps to ensure a more accurate – and more responsive – process with shorter order cycles.

Prime Candidates for Cross-Docking
Just about any type of product can be cross-docked, but cross-docking is particularly effective for companies that are moving heavy volume on any given day and need to do it in a precise way where service is critical.

Read more at Capitalizing on Cross-Docking

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Seven Supply Chain Resolutions for 2015

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Map your Extended Supply Chain: Our collective supply chain eyes were opened in 2011 as a result of the earthquake/tsunami in Japan, and then severe flooding in Thailand that decimated key suppliers in the high-tech sector.

Model Your Supply Chain: A relatively small but growing number of companies maintain an active network model of their supply chains that they use for on-going decision-making, from inbound supply flows to what products to make where.

Develop a Talent Strategy: Do you really have a plan for finding and developing supply chain talent? A few leaders do – but not many. A few years ago, Pepsico took a look at this – and wasn’t happy with what it found.

Start Benchmarking: In general, we do far too little benchmarking in the supply chain. I am referring not just to maybe participating in some survey or service that allows you to compare your results (sort of) with those of others, but meeting with companies to see how they do things, and swap and compare ideas and practices.

Review Your Technology Portfolio: Do you know exactly what software you have where? Do you have any “shelfware,” meaning software you paid for but never implemented, either in total or at certain locations?

Paint a Vision for becoming Demand-Driven: In the early 2000s Procter & Gamble came up with the “consumer-driven supply chain” concept, which the then AMR Research morphed into its demand-driven supply networks.

Start Lunch Time Education Meetings: I know a few companies – Campbell Soup used to be one of them and maybe still is – that hold weekly or monthly Friday “brown bag” lunch days focused on education. Could be an internal team member presenting insight into their area of operation.

Read more at Seven Supply Chain Resolutions for 2015

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