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Supply Chain Analytics: challenges & opportunities

25 July 2018
Supply Chain

There are two major issues: the understanding of the supply chain as a complex system and the effective use of the supply chain data available.

Why are these problems? Let’s start with Supply Chain Analytics, which relates to the use of data and the complexity of today’s supply chains.

For most companies, the word analytics is synonymous with reporting. But despite thirty years of supply chain technology evolution, the most commonly used system for supply chain planning is a spreadsheet.  Companies cannot effectively model the trade-offs of growth, profitability, supply chain cycles such as procure to pay and inventory turns, and business operations complexity on a spreadsheet. As that complexity increases, most companies are unable to use supply chain analytics to improve operating margin and inventory cycles.

In the past few years, we’re hearing more and more about the use of data analytics in the supply chain & logistics function.

Some industry experts claim that the day for real-time supply chain practices has come -- and is on the verge of being more mainstream, thanks to a multitude of cloud data management tools and increased corporate adoption of new supply chain software systems coming to market. However, there's also acknowledgement that a necessary foundation for moving efficiently at real-time speed -- supply chain analytics -- is still very much at the beginning stages of development at many companies, and will take time to build out.

All businesses with a supply chain devote a fair amount of time to making sure it adds value, but these new advanced analytic tools and disciplines make it possible to dig deeper into supply chain data in search of savings and efficiencies.

Supply Chain Analytics: the challenges

With every economy becoming globalized and companies increasing their presence across countries, operations of global manufacturing and logistics teams are becoming complex and challenging. Delayed shipments, inefficient plants, inconsistent suppliers can stall and delay the shipments thereby increasing the company’s supply chain costs. Some of the major challenges that supply chain executives are facing today are:

  • The lack of visibility of global supply chain and logistic processes
  • Managing Demand Volatility
  • Cost fluctuations in supply chain

The increasing importance of Analytics and planning to overcome the supply chain challenges that executives are facing today, cannot be ignored. Most of the organizations are planning to increase their investments in Supply Chain Analytics with a bulk of it going to supply chain function because it holds the greatest potential for innovation and competitive advantage. With business analytics improving significantly in the last decade and offering decision support for the critical tactical and strategic supply chain activities, insights from these activities are helping the companies to reduce their costs and also helping in optimizing the supply chains.

Supply Chain Analytics-driven intelligence

Supply chain costs form a significant part in company’s costs and supply chain executives face a challenge in handling these costs. Supply chain costs significantly impact key financial metrics like working capital, the cost of goods sold, and cash flow.  Constant need to improve the financial performance should happen in industries which handle large amounts of inventories. Key areas where costs can be controlled with analytics-driven intelligence include:

  • Materials:  Analytical tools can improve visibility to the true total component cost of products rather than just price. A complete view of supply chain cost for any given material is necessary for making optimal purchase decisions on a should cost basis. By placing complete information at the fingertips of the supply chain managers, organizations can reduce the material purchases through improvements in supply chain practices and better price negotiation outcomes.
  • Logistics: Fluctuating demand patterns and an expanding base of suppliers and logistics partners have driven companies to continuously rethink their logistics network strategy. They can realize strong ROI improvements through analytics-driven planning activities such as route optimization, load planning, fleet sizing and freight cost reconciliation.
  • Sourcing: As businesses expand into new, volatile markets with diversified product portfolios, managing a multitude of suppliers around the world becomes challenging. There are considerable costs and potential risks when signing on each new supplier. While an industry might play several suppliers against each other to achieve the lowest price, without a proper balance of sourcing and related operational controls, the results could be counterproductive.Sophisticated analytics programs generate real-time supplier performance management data that supply chain managers can generously use to improve their sourcing strategy. This data empowers sourcing professionals by providing analysis from initial screening to ongoing risk management. Potential supplier risk is assessed through a combination of financial analysis and capability constraints. A strong fact-based supplier selection process rather than one based solely on cost is the result.

Supply chain Analytics. therefore, plays a key role in enhancing the performance of supply chain by improving supply chain visibility, managing volatility, and reducing fluctuations in cost.

The accuracy of your Supply Chain data

Data has to be both accurate and accessible in order to perform data analytics with its complex algorithms.

The accuracy of data provided is the most important factor. Supply Chain data riddled with errors does not provide your organization with the accurate view needed to make operational improvements. To help improve data reliability, companies are implementing automated data collection technologies. These tools help to reduce manual data entry, where most errors originate.

Typical problems with data integrity arise when there are multiple sources of data, and there is no single source of truth when it comes to master data. This is often the result of business growth, mergers and acquisitions, legacy systems, and fragmented upgrades to enterprise applications such as enterprise resource planning, product life-cycle management, and manufacturing execution systems.

In addition, when tasked with reporting on quality data, most manufacturers rely on manual processes, such as exporting quality data into Excel spreadsheets, which requires time and increases the possibilities for inaccurate data as mentioned before. Typically IT is burdened with data synchronization and ensuring everyone internally (e.g., engineering, manufacturing, supply chain) is looking at the same data, a process that can impact productivity.

Supply Chain & Big Data

Supply chains have for a long time now been driven by statistics and quantifiable performance indicators. But the sort of analytics which are really revolutionizing industry today – real time analytics of huge, rapidly growing and very messy unstructured datasets – were largely absent.

In any global company, the supply chain is one of the largest sources of big data. It carries and produces information that affects almost every other area of the business. However, most businesses do not tap into this potential treasure-trove of information effectively, despite the fact that they recognize the potential value of doing so.

In 2013 the Journal of Business Logistics published a white paper calling for “crucial” research into the possible applications of Big Data within supply chain management. Since then, significant steps have been taken, and it now appears many of the concepts are being embraced wholeheartedly.

Applications for analysis of unstructured data has already been found in inventory management, forecasting, and transportation logistics. In warehouses, digital cameras are routinely used to monitor stock levels and the messy, unstructured data provides alerts when restocking is needed.

Forecasting takes this a step further – the same camera data can be fed through machine learning algorithms to teach an intelligent stock management system to predict when a resupply will be needed. Eventually, the theory is, warehouses and distribution centers will effectively run themselves with very little need for human interaction.

Predictive supply chains instead of reactive

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates predictive analytics.

The supply chain is a great place to use predictive analytics to look for a competitive advantage, because of its complexity and also because of the prominent role supply chain plays in a company’s cost structure and profitability. Supply chains can appear simple compared to other parts of a business, even though they are not. If we keep an open mind, we can always do better by digging deeper into data as well as by thinking about a predictive instead of reactive view of the data.

What companies should try to do is derive insights which are both more predictive – they should allow you to see what is going to happen – and prescriptive – now you know something, what should we do about it? Which are possible actions we could take and what are the consequences?

DHL, the world’s leading logistics company, launched its latest white paper highlighting the untapped power of data-driven insight for the supply chain. The white paper has revealed that most companies are sitting upon a goldmine of untapped supply chain data that has the ability to give organizations a competitive edge. While this wealth of supply chain data already runs the day-to-day flow of goods around the world, the white paper has revealed a small group of trailblazing companies are utilizing this data as a predictive tool for accurate forecasting.

The predictive enterprise: Where data science meets supply chain” is a white paper by Lisa Harrington, President of the lharrington group LLC that was commissioned by DHL to identify the opportunities available to companies to anticipate and even predict the future. It encourages companies to get ahead of their business and direct their global operations accordingly.
Data mining, pattern recognition, business analytics, business intelligence and other tools are coalescing into an emerging field of supply chain data science.

These new intelligent analytic capabilities are changing supply chains – from reactive operations, to proactive and ultimately predictive operating models. The implications extend far beyond just reinventing the supply chain. They will help map the blueprint for the next-generation global company – the insight-driven enterprise.

The Future of Supply Chain Analytics

There is also another reason why organizations need to invest in analytics. There is an anticipated push for “smart automation” in the future, which means reducing the level of human intervention and making better decisions. This can only be done by advanced analytics that can predict future scenarios, or analyze real time data and make complex, profitable decisions, sometimes on the spot.

The end-goal is leaner, faster, more transparent and most importantly, self-orchestrated supply chains driven by data and consumer behavior.

Suply Chain Analytics: the ROI case

To help determine if an organization should adopt analytics, the next step is building the business case for analytics investments. To build a strong business case for analytics, strategists seek to gauge the potential return on investment (ROI). In a recent Gartner survey of supply chain strategists, when asked about the ROI in analytics, on average, 29 percent of organizations said they have achieved high levels of ROI by using analytics, compared with only 4 percent that achieved no ROI.

However, there are still many constraints and realities that need to be eliminated to achieve the potential of analytics. A good business case must always be clear about what might get in the way.