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The pros and cons of ‘supply chain finance’

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Coca-Cola does it. So does the global consumer goods group Procter & Gamble and discount store chain Walmart. In Australia, Telstra and construction group CIMIC are into it.

All are using an increasingly popular scheme known as “supply chain finance” to pay the companies that provide them with goods and services.

The old-fashioned method of paying invoices is simple. A company orders goods from a supplier. The supplier delivers them and issues an invoice with a due date, such as 30 days’ time. The company pays the supplier within 30 days.

Suppliers who have delivered their goods but want to get paid earlier than 30 days have also for many years had another option: approach a bank and sell 80 per cent of the invoice (typically the maximum the bank is prepared to buy) before the due date. The bank later collects the invoice payment.

This is known as debt factoring; the bank or financier that buys the invoices is called a factor.

In recent years, a third option has emerged. With the help of banks and financiers, big companies take the initiative and suggest payment options to their suppliers, giving the companies more control over when and how they pay invoices.

This latter scheme is most commonly known as supply chain finance or, more specifically, “reverse factoring” – a technical term commonly used by ratings agencies to differentiate it from conventional debt factoring.

Reverse factoring compared to normal payment terms

Reverse factoring compared to normal payment terms

In reverse factoring, the big company hires a bank such as JPMorgan or a financier such as London-based Greensill Capital to make agreements with its suppliers. The supplier gets to choose exactly when it wants to be paid the full amount of money it is owed, with payment dates as soon as 10 days after goods and services are delivered.

Banks and financiers team up with technology groups such as Taulia and Oracle, which insert technology known as enterprise resource planning software into the accounting systems of their customers.

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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.

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Top 25 Risk Factors for Manufacturing Supply Chains

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According to a recent report from BDO USA, an accounting and consulting organization, manufacturers’ intellectual property, supply chain data and products have become prime targets for cyber criminals.

The 2016 BDO Manufacturing RiskFactor Report examines the risk factors in the most recent 10-K filings of the largest 100 publicly traded U.S. manufacturers across five sectors including fabricated metal, food processing, machinery, plastics and rubber, and transportation equipment.

The factors were analyzed and ranked by order of frequency cited.

Manufacturing Industry Serves Up New Risks

The manufacturing industry is getting mixed reviews.

The Institute for Supply Management (ISM) Index reported that activity was up in April after five straight months of declines.

Then, in late May, the Purchasing Manager’s Index reported the first reduction in output since September 2009.

In the trenches, manufacturers say domestic demand has been solid, while global business has been more challenging. And the end customer matters: in a recent earnings call, Caterpillar’s CEO noted, “Just about any market that’s away from oil is doing pretty good.”

“Pretty good” is a modest but realistic goal for manufacturers this year, and their top concerns echo this cautious optimism. The annual analysis of the most frequently cited risk factors found the supply chain remains at the top of the list – cited by 100 percent of manufacturers we analyzed – while emerging and growing risks in cybersecurity, competition, labor, pricing, regulations and international operations are also keeping manufacturers up at night.

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How Does Big Data Analytics Help in Decision Making

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Staying ahead in the game is paramount for any business organization to survive in this competitive world. The future poses challenges that need tackling in the present. Every decision made today has a significant impact on the future of that organization. The rate at which a company responds to challenges in the present and the future is what determines their rate of success. Data Science and Big Data analytics can help organizations in decision making and drive the company to a realistic future.

The Deciding Factor

It is paramount for businesses to understand the big data concept and how it impacts the organization activities. Discussed below are ways in which Big Data facilitates faster and better decision making;

Accelerating Time-to-Answer

The time cycle for decision making is decreasing rapidly. Companies have to make decisions more quickly in this period than in the past. Accelerating decision-making time is crucial for the success of any organization. The use of Big Data doesn’t change the urgency of decision making. Big Data analytics mitigates.

Customer reaction to a product is an important factor to consider when making a decision. Using data resources to understand the preferences of customers is one way of pointing out gaps existing in the market. However, the problem is how do you integrate and act in real time? The key is to know how to combine Big Data with your traditional Business Intelligence to create a more convenient data ecosystem that allows for the generation of new insights while executing your present plans.

Accelerating your time-to-answer is crucial for customer satisfaction. For example, if your answer time is usually in minutes, Big Data can reduce it to seconds. If it takes weeks for a client to have their problem tackled, then reducing it to days is more convenient for your customers. Customer retention is critical to the success of your organization.

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