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Fueling Supply Chain Transformation

By Can A. Dogan, Frode Huse Gjendem, and Jade Rodysill | July/August 2011 | 21 | 4
Advantages of analytics

Using the advanced computing power of analytical tools, operations and supply chain managers are

  • producing more realistic and detailed models, coping with missing and substandard data, and enabling complex methods and algorithms
  • securing quicker proof of value—meaning, they can more rapidly build momentum behind larger transformation efforts requiring process, behavior, technology, and organizational change
  • creating a core analytical framework around which new processes can be built and institutionalized
  • undertaking cross-domain analysis of supply chain issues to enable complex and interrelated problem solving.

Predictive analytics energizes dynamic networks

As companies fight for competitive edge, it is those businesses that bring products to market faster, more cost-effectively, and with greater customer intelligence that will survive and thrive. Superior supply chain performance is essential—particularly in a rapidly globalizing marketplace, where the ability to adapt to and predict fast-changing conditions is essential. To secure the required capabilities, operations and supply chain management professionals need to leverage insights from the vast amounts of data available across their organizations and employ analytical techniques.

Supply chain predictive analytics— using an integrated framework that employs quantitative methods to derive insights from data—could be the key differentiator in rapidly building and sustaining a high-performing supply chain in the decade ahead. Accenture research highlights the scale of the challenges to be overcome. The 2008 report “High Performance Through Supply Chain Planning” revealed that most companies are struggling with at least some aspects of forecasting and supply. For instance, the median forecast accuracy at the stockkeeping unit (SKU) level was just 75 percent.

Additionally, we found that companies have little visibility into what will affect them at either end of the supply chain—from the flow of customer orders to the operating condition of their key suppliers. This is in large part due to insufficient levels of collaboration with suppliers and customers. Lacking the requisite linkage of systems, common processes, and readily available commercial software tools, many companies are ill-equipped to carry out effective advanced planning and scheduling, analytics, optimization, sales planning, and cross-functional collaboration.

We know the leaders in this field are using prescriptive (forward-looking) analytics to understand what’s coming next. They have moved from descriptive analytics—understanding “what?” and “why?” and even “so what?”—to insights into what is the best that can happen.

For those businesses that commit to broad and deep transformations, the end results can be significant.

Using predictive analytics, professionals can look upstream and downstream to evaluate operational impacts of their decisions—spanning plans and schedules, carrier and asset use, quantity and quality variation, cycle times, and landed costing. Armed with such insights, experts believe these decision makers are as adept at shaping demand as they are at sensing it. Compared to their competitors, they are more than four times as likely to achieve minimum accuracy levels of 80 percent in demand forecasting and more than twice as likely to rate their ability to shape demand as “good” or “excellent.”

The advantages extend to managing supply, as well. Compared to laggard companies, successful organizations are more than four times more likely to quickly respond to disruptions, partly because they involve suppliers in planning. Furthermore, these masters are twice as likely to have explicit links between new product introductions and planning processes, which is ever more important as cycles compress and introductions increase. They also have up to 50 percent less finished goods inventory than laggards.

Predictive analytics can play a vital role in fueling the supply chain transformations that enable organizations to compete at this level. There is no shortage of opportunity. However, a 2010 Accenture survey of 600 senior managers at more than 500 blue-chip businesses shows that, for most companies, analytics capabilities are a long-term goal rather than a reality. Currently, just 6 percent of businesses are making significant use of analytics in their supply chains. And, half the respondents believed their organizational structures prevented data and analytical talent from generating enterprise-wide insights.

Advanced performance
Supply chain transformation focuses on building and sustaining the world-class capabilities needed to improve and sustain performance, which drives cost competitiveness, balance sheet flexibility, operational excellence, profitable growth, resilience, and sustainability. Supply chain transformations embrace operations, as multiple initiatives are used to address each of the key domains: planning, sourcing and procurement, fulfillment, manufacturing, product life cycle management, and service management. An assessment tool can be used to prioritize these initiatives by identifying capability gaps, quick wins, and major benefits.

Successful supply chain transformations address all primary levels in the shareholder value tree. (See Figure 1.) When properly executed, these initiatives should deliver 1-to-5 percent revenue enhancement and 5-to-25 percent improvements in other areas. For those businesses that commit to broad and deep transformations, the end results can be significant— strategically, on the bottom line, in areas of risk exposure, and across the environmental footprint. The proviso is that, having invested in such large-scale transformation projects, decision makers should continuously seek ways to improve primary levels and sustain whatever benefits have been achieved. 


Five success factors
We have identified five key success factors for supply chain transformation, all of which should constitute the foundation of any program.

1. Develop a value creation agenda. Define the supply chain’s role in delivering overall business strategy, as well as its value by individual product and product segment. It is important to understand the vulnerability of alternative supply chain models to changing market conditions and the total cost of ownership (TCO), as well as what it takes to deliver value—in other words, where is money left on the table due to dysfunctional supply chain models?

2. Configure supply chain processes around value delivery. Supply chains should be configured to deliver TCO benefits and target customer value by segment (customer, product, and geography), with supply chain strategy rigorously focused on optimizing TCO across network configuration, operational parameters, and processes. Professionals must understand the root causes of whatever inefficiencies are identified across functional boundaries before moving swiftly to reduce costs in core and noncore supply chain management processes. Resources and investment need to be focused on integral processes that drive the overall value proposition, with metrics and ownership defined by process type.

3. Use information technology (it) to optimize supply chain processes. IT capabilities must link to planned business impacts. This means establishing the expected value of technology implementation, connecting solution capabilities to business effects, defining requirements by segment, and determining commonality. It’s also important to identify where supply chain analytics will bring the most value. For this process to succeed, close collaboration between IT and supply chain professionals is essential.

4. Establish the organization and people agenda. Leaders must understand and buy in to the overall supply chain vision, while being aware of how its objectives will affect behaviors, competencies, and attitudes. Key decision makers should be identified and empowered. Skill gaps must be addressed and rewards introduced to motivate change and ongoing performance by segment. As cross-functional teams play a vital role in driving end-to-end business process outcomes, identify and plan for any training needs.

5. Manage the change and implementation journey. Company decision makers must set clear milestones for the transformation, with enough flexibility to ensure the end state can be continuously reevaluated. They also should identify early what actions will be needed to drive decision making, competency, behaviors, and rewards. Although it is essential to closely align implementation approach and organizational culture, proven change management methods can be employed as needed to foster, absorb, and sustain transformation.

Analytics enables the deeper analysis companies need to prepare themselves for a range of market conditions.

Why analytics now?
Burgeoning complexity, shortening product life cycles and business cycles, amplified price and volume volatility, and increasing regulatory threats throughout the extended supply chain mean that managers need acute, real-time insights into what will affect them on the demand and supply sides. The time is right for high-performing companies to investigate the value that predictive analytics can bring to supply chain transformation.

To secure the acute insights they need, organization leaders must move decisively to harness and leverage the data at their disposal. The good news is that technology no longer lags aspirations in this area. Sophisticated tools now are integral to the latest enterprise resources planning, decision support, financial, and customer relationship management software. Cloud computing has transformed the ways in which vast quantities of data can be collected, stored, and processed. And open-source software has democratized the analytics capabilities needed to drive meaning and insight from data.

Analytics, by definition, provides advanced techniques that go far beyond regular methods employed in supply chain transformations. Such extra sophistication will provide better results when properly deployed. But the benefits are much broader. By providing new approaches and methodologies, analytics enables the deeper analysis companies need to prepare themselves for a range of market conditions, allowing them to hedge against current market risks and future volatility.

Take inventory target setting, for example. A traditional service level model for an individual SKU might provide a viable inventory target for the product, while ignoring significant interrelationships (and impacts) across the supply chain network. Powered by advanced analytics, a multi-echelon inventory optimization solution would drive further benefits and improved inventory levels by scrutinizing network-wide matrices of demand and lead times.

Further benefits are possible. For instance, where supply lead times follow random patterns, an analytics-based solution could simulate supply chain network behaviors to provide valuable information on inventory levels. By probing deeper, additional insights could be obtained, providing the foundation for a broader and far more realistic inventory management strategy.

Analytics in action
None of this is theoretical. Real-world companies are enjoying significant benefits with analytics every day. For example, consider a global office products company that aimed to improve its inventory performance. A supply chain mastery assessment revealed an opportunity to improve analytics both within the supply chain and enterprise-wide. Using root cause analysis, it was determined that inventory processes were sound, but upstream collaboration processes with merchandising and sales were inadequate. This meant that inputs often were late or inadequate.

The company created analytics infused demand planning and sales and operations planning capabilities that improved collaboration with business units and enhanced the quality of the numbers within the process. Its integrated planning organization now is supported by an analytics capability, which increases specialization and overall accuracy. Statistical tools and models drive results.

Decision makers at an international oil and natural gas service company wanted to support international growth objectives. Using manufacturing analytics, this business was able to realign its supply chain with growth markets, thus reducing cost and lead times and improving scalability. Based on the insights obtained, the company also was able to shift supply to more cost-effective manufacturing and sourcing locations, embedding greater accountability for cost, delivery, and supply chain performance; improving planning and inventory management capability; and reducing its footprint. Project annual savings were between $100–140 million.

Leaders at a major agribusiness company sought to identify, challenge, and evaluate supply chain improvement initiatives. There were four priorities:
  • Identify key SKUs through sales volume analysis. 
  • Identify optimum levels of master data parameters for safety stock determination.
  • Analyze the business impacts of poorly maintained parameters. 
  • Analyze current forecasting methods and forecast accuracy key performance indicators.

Supply chain planning analytics were used to identify the root causes of performance problems, and the methodologies were applied to forecasting, inventory, and cost-to-serve analysis. As a result, the company now is equipped to design a supply chain model that aligns directly with specific business needs. Benefits include a 20 percent

These real-world examples underline the power of superior supply chain analytics. Of course, not every business needs to build a high-performing analytics supply chain. Indeed, because it calls for extensively accurate data, special skills, and substantial technology and training investments over time, many organizations opt to buy analytics outcomes instead of developing these capabilities themselves. The analytics services are not entirely outsourced. Instead, the functional- and technology-intensive tasks are given to third parties that deliver them across a larger client base.

Regardless of which model a company selects, all supply chain transformation To comment on this article, send a programs can benefit from analytics-powered insights. The fundamentals remain message to feedback@apics.org. unchanged. That is to say, successful transformations will continue to call for a rigorous focus on value, process, innovation, and behavioral change. With this foundation in place, the following basic steps should be taken to move the organization toward an analytical mindset:

  • Initiate proof of value in targeted supply chain domains. 
  • Cultivate a culture of analytical rigor. Educate the workforce on supply chain analytics capabilities. 
  • Source external skills as needed, and do not allow internal skills restrictions to become a barrier to advanced analytics. 
  • Recognize that it can be a challenge to sustain analytics beyond a specific project. Explore the option of buying it as a service.
Can A. Dogan is a senior executive in the supply chain management service line of Accenture. He leads Accenture’s Global Innovation and Talent Network, which includes Accenture’s offshore supply chain planning and analytics capabilities. He may be contacted at can.a.dogan@accenture.com. 

Frode Huse Gjendem is a senior director in Accenture Analytics. He has experience in the pharmaceuticals, electronics and high technology, chemicals, and consumer goods industries. He may be contacted at frode.huse.gjendem@accenture.com

Jade Rodysill is a senior manager in Accenture’s North American Supply Chain Strategy group. His work focuses on supply chain strategy, fulfillment, logistics, and risk management. He may be contacted at jade. rodysill@accenture.com.

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