The Promise of Advanced Supply Chain Analytics
January 4, 2011
Historical algorithms and supply chain management models based on past demand, supply and business cycles may prove partially insufficient in today’s environment—and completely inadequate tomorrow. For nearly all companies today, regardless of industry, the speed of business is outpacing the level of insight into the supply chain. The fundamental problem with current supply chain visibility and analytical models is that discovering or learning what happened in the past no longer provides a competitive advantage. History lessons may be great for business school students to ponder in case study groups. But they do not equip supply chain professionals to effectively manage the supply chain of today, and most importantly, the critical issues that will decide success and failure tomorrow.
In today’s climate, we should turn our attention to modeling and predicting the future, enabling centralized insight coupled with decentralized action. Such a change will allow for the gathering and synthesis—not to mention the analysis—of broader sets of internal and external information, including both structured and unstructured data, at a much more rapid pace. Analytics and the ability to drill into data remain critical. But other considerations need to be taken into account when moving to a real-time environment where data acquisition, synthesis, cleansing, processing, alerting and visualization play just as critical a role in supply chain analytics.
Speed-to-analysis matters more than ever. Significant currency swings, changing demand forecasts and supplier-specific challenges have impacted nearly every organization—from those with the best managed supply chains to the worst. Even perennial top supply chain performers have faced embarrassing stock-outs during periods of unanticipated demand in recent years. Our analysis indicates that businesses are not going to become any less volatile in the coming 12 months. We should all put the memories of normal demand and supply cycles in the rear-view mirror.
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Historical algorithms and supply chain management models based on past demand, supply, and business cycles may prove partially insufficient in today’s environment—and completely inadequate tomorrow. For nearly all companies today, regardless of industry, the speed of business is outpacing the level of insight into the supply chain.
The fundamental problem with current supply chain visibility and analytical models is that discovering or learning what happened in the past no longer provides a competitive advantage. History lessons may be great for business school students to ponder in case study groups. But they do not equip supply chain professionals to effectively manage the supply chain of today, and most importantly, the critical issues that will decide success and failure tomorrow.
In today’s climate, we should turn our attention to modeling and predicting the future, enabling centralized insight coupled with decentralized action. Such a change will allow for the gathering and synthesis—not to mention the analysis—of broader sets of internal and external information, including both structured and unstructured data, at a much more rapid pace. Analytics and the ability to drill into data remain critical. But other considerations need to be taken into account when moving to a real-time environment where data acquisition, synthesis, cleansing, processing, alerting, and visualization play just as critical a role in supply chain analytics.
Speed-to-analysis matters more than ever. Significant currency swings, changing demand forecasts, and supplier-specific challenges have affected nearly every organization—from those with the best managed supply chains to the worst. Even perennial top supply chain performers have faced embarrassing stock-outs during periods of unanticipated demand in recent years. Our analysis indicates that businesses are not going to become any less volatile in the coming 12 months. We should all put the memories of normal demand and supply cycles in the rear view mirror.
Within this climate, companies will continue to face not only greater variability and uncertainty but also rising risk. News headlines from the past few years paint a steady stream of incidents that highlight the behavior of how suppliers—even lower-tier suppliers—can create additional risk throughout the supply chain. Indeed, even companies with largely regional supply chains face rising revenue, brand, customer, legal, and market-based supply chain risks, to name only a handful of exposure areas.
Even with the current generation of analytical tools, ranging from Excel and statistical modeling packages to company-wide Business Intelligence (BI) platforms, the speed of analysis is often failing to keep up with the market’s momentum. In our experience what is needed is a new approach and mindset to examine information—a new approach that we call advanced supply chain analytics.
Advanced Analytics Defined
Advanced supply chain analytics represents an operational shift away from management models built on responding to data. Emerging capabilities in this area introduce a proactive management model, equipping supply chain professionals with the ability to continually sense and respond as business changes around them.
Moreover, advanced supply chain analytics can help supply chain professionals analyze increasingly larger sets of data using proven analytical and mathematical techniques (for example, regression analysis, stochastic modeling, linear and non-linear optimization), thus allowing them to spot patterns and correlations that may have been missed in the past. This enables companies to look at the business in new, previously unimagined ways. Advanced supply chain analytics is more than applying “old math” to existing problems. Instead, it applies new techniques and methods to ever-expanding data sets.
Advanced supply chain analytics is moving the industry from historical point-in-time snapshots to real-time data access that pushes analysis and visibility to stakeholders across the organization. The concept of a centralized data warehouse or one-off database will become anomalous in a few short years. Advanced supply chain analytics will increase, by orders of magnitude, the number of individual data elements and records considered (think billions, not millions). Focus will shift from the internal cross-functional sharing of data—which happens in ideal circumstances today—to greater coordination and shared understanding of the data flows across value chain partners. Individual silos within the supply chain, suppliers, procurement, operations, sales, the customer, and consumer will be torn down; instead, a single, broader supply chain will emerge.
The question remains, what are organizations actually doing to tap the power of advanced supply chain analytics? The following case examples provide some insight.
Case Example: Total Cost Visibility and Risk Analysis
This manufacturer’s supply base for its relatively short product lifecycle consumer durable item remained entirely offshore, concentrated primarily in China and Southeast Asia. There were few, if any, near-shore options to either manufacture the product or work with suppliers to provide parts and components. Despite the length of the required supply chain for this item, the manufacturer has been able to develop a strategy and view into total landed cost by applying advanced supply chain analytics. This approach not only helped to forecast and manage margin in the near-term, but also helped management react to market changes to ensure available inventory and reduce overall supply risk and brand exposure, ahead of the competition.
Before applying an advanced analytical approach, the company had a comparatively robust view into cost information. Yet this data remained in silos throughout the organization, with different groups conducting their own disparate analyses, all of which impacted total cost. The impacts were felt in make vs. buy analyses; procurement strategies; ocean and air freight options (and expediting costs); domestic freight approaches; optimal warehousing or distribution models; and, of course, inventory requirements at different points in the supply chain. The company also had made early attempts to identify supply risk factors in its analyses (floods, typhoons, lower-tier supply disruptions, and so forth). As with the rest of the information collected, though, the company found it difficult to bridge the functional gaps to gain a timely view into both current and predicted total cost scenarios.
Advanced supply chain analytics was the breakthrough that enabled this organization to develop a single view into total cost on a continuous basis. By aggregating different internal sources with external third-party data sets on a constant basis, this manufacturer was able to create a truly “live” total cost model that was predictive of margins, revenue exposure and business risk, while optimizing for working capital in the process. Using advanced analytics, team members now know how to better understand and continuously optimize order volumes based on both internal and supplier cost structures, balancing volume price breaks against inventory. The manufacturer then linked this and other information into supply chain forecasting and modeling tools to better understand the ideal way to flow products (for example, direct to store, regional vs. national distribution, third-party distribution) on a month-by-month basis to retailers, while maximizing revenue based on promotions, seasonality, and other factors.
Case Example: Flexibility and Breakthrough S&OP
A consumer packaged goods company has been able to achieve supply chain flexibility and breakthrough S&OP (sales and operations planning) with advanced analytics. This organization attributed significant margin improvement in some of its highest volume-moving SKUs to the competitive edge afforded by advanced supply chain analytics. Before embarking on its advanced analytics journey, the company had moved forward on certain traditional S&OP activities. It did not shy away from value chain analysis and examined the best ways to maximize flexibility based on seasonality, regional order volumes/mix, and overall adjustments to demand forecasts.
Initial cross-functional S&OP planning focused on gathering, integrating, and analyzing basic information, including forecast validation accuracy, in-transit times, inventory levels, tier-one supplier lead-times, and retail sales information. The model integrated these basic fields, tying together sales forecasting, demand planning, and supplier planning/management to align supply and demand, giving full consideration to the financial impact of different scenarios. As in most companies, the S&OP process took many weeks, with the majority of the time spent gathering and bringing together internal and external data from multiple sources.
Despite the early progress made through traditional S&OP, it was advanced supply chain analytics that helped the organization transform its financial performance. By moving to a model that gathered information in a near real-time manner, the company spent far more time analyzing its data and interpreting the insight derived from the analysis as opposed to simply looking for the various data sources. The result united the entire organization with a shared view of data that allowed everyone from the CFO to brand managers to take the pulse of the supply chain as activity flowed through the system. Executives, marketing, sales, manufacturing, and finance were able to work together more closely than ever.
Indeed, the output proved not only an IT and supply chain enabler, but also a transformative model that impacted the entire company. It also directed attention to where the business demanded it most. By moving to an exception-based planning model—which added what some of the workflow experts described as “pre-exception processes”—the organization gained predictive awareness of where it needed to focus efforts weeks or months in advance of previously available warning signs. In addition, the level of granularity of information rippling back from the aisles and shelves helped the organization achieve a constantly evolving view of ideal inventory levels based on the number of shipments required relative to inventory carrying costs. On a broader level, the approach provided a stepping stone to integrated financial planning, aligning financial applications with treasury, procurement, and broader supply chain systems and strategy.
Team members also were able to ask and answer questions as new opportunities and threats presented themselves—or when triggers suggested they had the potential to present themselves. Typical areas of inquiry included:
- What happens when demand falls? How do we preserve margin?
- What happens when we have excess capacity? How do we drive incremental revenue?
- If the business is up and we have a shortfall, what options/scenarios will maintain SKU profitability while avoiding stock-outs in critical locations?
For this company, the ability to compress S&OP information-gathering cycle times from three to four weeks down to five to seven days meant that far more time could be allotted to addressing these questions and far less time spent gathering data. Yet the impact of this “speed-to-insight” also extended beyond S&OP, creating a truly demand-driven culture. The company developed an enhanced ability to sense and respond to changes based on shifting demand data. With this information, it was able to avoid stock-outs, even during promotions. The company also now could customize promotions based on traditional marketing/customer metrics as well as opportunistic elements in the supply chain such as disruptions that impacted a competitor.
Case Example: Sourcing and Finance Goals Aligned
Using advanced supply chain analytics, a tier-one automotive supplier bridged the finance and procurement gap to create shared metrics, develop a common understanding, and set mutual goals. The result: a hybrid strategy that gives equal weight—and visibility—to year-over-year cost savings, working capital improvements (which are not built off the backs of suppliers alone), reduced commodity risk, and a tax-optimized supply chain. Having both procurement and finance at the table with aligned objectives had not been possible previously not only because of information silos, but also because of what many perceived to be conflicting goals between the functions. That is, the sourcing teams focused on unit cost savings while finance concentrated on working capital.
These conflicting goals did more than create a culture of misalignment internally. They also left suppliers out in the cold, as relationships deteriorated in spite of rationalization programs that gave remaining vendors a greater share of spend. Curiously, the procurement/finance/supplier disconnect appeared to get even worse after the company went through a multi-wave strategic sourcing program, an overall procurement transformation, and a source-to-pay systems implementation. Despite the increasing availability of data, there were no means of bringing this information together to jointly align goals and strategy, and to analyze combined data sets to balance the goals of all parties, including suppliers.
The introduction of an advanced supply chain analytics approach helped to quickly create a shared internal understanding of value and spend management, factoring in total cost savings goals and working capital impact. This visibility also enabled a new type of sourcing approach that leveraged commodity risk management techniques to aggregate and manage raw material inputs at multiple levels in the supply chain. With the help of finance and with the visibility into aggregate demand information, the procurement team hired a commodity trading professional to implement and manage a hedging program that leveraged a combination of forward buying strategies and financial instruments (futures, forwards, options, and swaps as well as exchange traded funds).
The next steps taken aimed at optimizing the supply chain tax impact based on the location of resources, facilities, and different buying groups (as well as a small, centralized “buying company” for the organization in Luxemburg). Pooling and analyzing all data highlighted additional tax opportunities—for instance, taking full advantage of transfer pricing opportunities and developing supply risk-driven vertical integration. With the latter, the company found that its rigorous supply risk analytics program enabled prediction of supply failure for a vendor in Ireland based on a combination of supplier performance data and financial data. Using this information with unstructured data gleaned from discussions with the supplier’s executive team, the company decided to purchase the supplier to take advantage of a €20 million euro tax loss—and, in the process, avoid a potential supply disruption.
The result of the advanced supply chain analytics program included shared incentives and goals across functions as well as visibility into total cost and working capital requirements for the overall business. The company was able to move into a real-time view of cost information, getting out of the point-in-time paradigm that produced savings in the indirect materials supply chain, but did little for the direct supply chain that operated the business. Using new technologies such as data-hub capabilities that allowed real-time analysis of internal and external supplier-system information combined with unstructured external market information and insights, the organization shifted the technology focus from building systems of records based on retrospective reporting toward analytical tools that drive analysis and outcomes.
Technology Foundation for the Future
As these case examples make clear, a range of technologies will serve as the underpinning that enables the next generation of supply chain analytics. Many of these solutions are beginning to enter the market today or are already used by more innovative companies, albeit in select supply chain areas. The fundamental change is not necessarily to the types of analytics performed, though that will continue to evolve. Rather, the fundamental change is to the quantity, frequency, and types of information analyzed and how it is shared.
Perhaps the most significant emerging and continually evolving relevant technology is a next generation business intelligence and self-structuring data logic that will bring together different data sets, putting data into a context to enable rapid insight. These new business intelligence and self-structuring data logic applications will allow users to ask questions of data all the time, rather than on a weekly, quarterly or yearly basis. So the business users will be asking these questions with their own set of tools rather than the IT department facilitating a data-warehousing-led information hunt. Moving forward, systems-based intelligence will identify patterns in structured data accordingly that allows the users to identify trends, anticipate future events, and draw rapid insight in a near real-time manner.
Even though we should assume that future business users in these environments would have at least elementary mathematical and statistical acumen, advanced supply chain analytics tools will mask the complexity of the business rules and underlying algorithms. Indeed, to drive widespread adoption, these tools will need to be as intuitive as a combination of a powerful internet search engine and database software. As important, users will need to quickly combine and analyze different data elements and information (for example, xCBL for trading-partner related document exchange such as purchase orders [POs], Advanced Shipping Notices [ASNs], and invoices). Identifying the types and sources of data necessary to obtain insight is critical and often overlooked. The complexity of this task can be simplified by mapping the type and source of each data element and how it is structured. The EI (External/Internal) Data Matrix in Exhibit 1 gives an illustrative example for indirect procurement and typical strategies for how the data is acquired.
To help make quick business decisions based on these new insights, advanced tools will suggest to the user the most appropriate visualization (for example, 3-D plots, topology charting, heat maps, geospatial mapping) based on data, rather than relying on user-initiated charting approaches. Like a satellite navigation system that re-routes the user dynamically based on traffic patterns, advanced supply chain analytical tools will proactively recommend courses of action based on built-in intelligence that does not require user intervention. Supply chain professionals will shift from collecting data and building disparate analyses to interpreting results and the suggested course of action across multiple parts of the organization.
This level of insight already exists in many markets, often in real-time. For example, certain nationalized healthcare systems and regional healthcare information organizations (RHIOs) can enable a physician or nurse to look-up all relevant patient information during an emergency room visit. In advanced applications, this enterprise master patient index (EMPI) technology may include the ability to query and pull a combination of prescribing details as well as past specialist, internist, and emergency room (ER) visits—even if this information is not integrated into a single patient record. These systems assume the worst when it comes to data quality, correcting on the fly for misspellings, social security numbers, birthdays, and more. Much as the most advanced healthcare systems are delivering new visibility to clinicians today, advanced supply chain analytical tools will focus on delivering similar capabilities built for functional business users.
Unlocking the Value Today
Accomplishing these tasks may seem unreachable in the near term. Yet many of the building blocks and technologies may already exist within your organization to make some of these ideas a reality in the next 12 months, rather than in the next decade. To avoid being left behind, organizations should identify their most critical current and future business issues and prioritize their analytical investment accordingly. Our view is that supply chain, procurement, finance, sales, and IT organizations of any size can start working together to build the capability related to advanced supply chain analytics. To get started quickly, target specific areas including procurement, logistics and sales/demand planning to pilot and discover the types of breakthroughs that are possible.
Companies seeking to develop a competency in advanced supply chain analytics should first evaluate their current capabilities not only from a supply chain perspective, but also from an IT perspective. With this knowledge, they can plan their own approach for advancement, ensuring that current handicaps—such as a lack of integrated planning systems, different ERP instances, and BI initiatives in functional silos—do not get in the way. Third-parties can help assist in this assessment as well as recommend initial and future steps and initiatives.
From a skills perspective, many supply chain organizations do not possess enough business experts with functional knowledge of different supply chain areas. Finding this diversity and breadth of experience is paramount, as is emphasizing analytical competency and expectation in the hiring and promotion process. In addition, companies should not ignore the IT skills challenge. To best leverage available technology and resources given constrained knowledge, capacity and limited hiring budgets, organizations should consider working with outside specialists that can bring a technical understanding and know-how to making advanced supply chain analytics a reality for the business.
This article contains general information only and Deloitte is not, by means of this article, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This article is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte, its affiliates, and related entities shall not be responsible for any loss sustained by any person who relies on this article.
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