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Maturity is the key to effective analytics: although organizations have embraced supply chain analytics, few are highly satisfied with their ability to use data to make decisions.

Many organizations have adopted analytics initiatives because of the need to aggregate vast amounts of data and automate the identification of patterns and trends. The supply chain alone produces a large enough data set that analytics can be applied to help identify areas for process and performance improvement. Data generated through internal operations, as well as transactions with suppliers and customers, can be used to determine small changes that can make a big impact on an organization with regard to efficiency gains and even cost savings.

Many supply chain professionals report that their organizations have increased their investment in analytics over the last three years, according to a recent APQC survey. This survey looked at the analytics practices of organizations, as well as the structure of these efforts. APQC surveyed supply chain professionals from a variety of organization sizes and regions and from 36 industries. APQC's analysis found that organizations have several areas of focus for their supply chain analytics efforts, and that most organizations have a formal analytics structure. However, the payoff of these efforts may not be at the level organizations would expect.

Analytics inputs and outputs

Supply chain organizations focus on a variety of goals for their use of analytics. As shown in Figure 1, when asked about seven possible goals or activities, a majority of respondents highly agree or agree that each goal or activity is an area of focus. This indicates that organizations want to see a variety of results from their supply chain analytics efforts.

Not surprisingly, the largest group of survey respondents highly agree or agree that a reduction in cost is a focus area for their organizations' analytics efforts. However, the next largest group of respondents highly agree or agree that their organizations want analytics to provide visual tools to help individuals within the organization obtain information in a way that is easy to digest. In addition to supply chain performance improvement, organizations are looking at analytics as a means of better disseminating information.

In fact, organizations are pulling data from across the supply chain to feed their analytics activities. Figure 2 indicates the top 10 supply chain activities for which organizations are using analytics. Although these activities span the supply chain, the top three activities for which organizations use analytics (scoring models to assess vendors, demand forecasting and safety stock level recommendation) focus on the procurement and logistics areas.

In addition to supply chain activities on which they will focus, organizations must determine the types of analytics they will use. In its survey, APQC defined three types of analytics:

1 Descriptive analytics, which uses business intelligence combined with existing data to determine what is currently happening within a business.

2 Predictive analytics, which determines what drives a specific business outcome. This form of analytics uses historical data and various algorithms to create scenarios that can help predict future events or trends.

3 Prescriptive analytics, which involves quantifying how predictions will affect a process or goal and using optimization or embedded decision rules to find out what should be done in a certain situation. This form of analytics uses insights from predictive analytics to recommend business decisions or actions that are likely to produce a specific result.

Respondents to APQC's survey indicate that descriptive analytics is the most commonly used form across all areas of supply chain, including quality management, procurement, process management, logistics, supply chain planning and manufacturing. These results align with many organizations' efforts to evaluate current supply chain performance, as they often use this most basic form of analytics to track measures such as median costs, average satisfaction ratings and cycle times for processes.

There is some indication that organizations are adopting more complex forms of analytics. Thirty-six percent of survey respondents indicated that their organizations use predictive analytics for their supply chain planning functions, and 30% indicated that their organizations use predictive analytics in procurement.

On a smaller scale, organizations are also making use of prescriptive analytics. The largest group of respondents indicated that their organizations use prescriptive analytics for supply chain planning (15%), followed by quality management (12%). That organizations use prescriptive analytics most in these two areas is not surprising given that recommendations for what should be done would benefit these areas most. However, it is worthwhile for organizations to consider how prescriptive analytics could benefit other areas of the supply chain.

Structure and organizational attitudes

Through its survey, APQC also sought to examine how organizations are structuring their supply chain analytics efforts. As shown in Figure 3, 23% of respondents indicated that their organizations do not have a formal analytics program or structure. This indicates that, although organizations are making efforts to analyze the data produced within their supply chains, some still rely on isolated analytics activities. However, nearly one-third of respondents indicated that their organizations have a centralized analytics function for the supply chain, and just under 30% use a combination of a centralized and decentralized structure.

Those supply chain functions with a centralized analytics structure may reside within already data-driven enterprises. Formal program structures often result from senior leaders' appreciation for analytics across functions. In fact, a majority of survey respondents indicated they strongly agree or agree that analytics is an expected activity in their organizations whenever building a business case or conducting an improvement project.

Despite the progress organizations have made in adopting analytics programs and the degree to which they use analytics for supply chain activities, the survey respondents had a variety of responses regarding the effectiveness of their organizations' efforts in using analytics to solve strategic supply chain challenges. Only 5% of respondents consider their organizations' use of analytics in this area to be very effective. Twenty-eight percent of respondents consider their organizations' efforts to be effective, and 44% (the largest group) consider their organizations' efforts to be average. This may reflect the fact that many organizations are still focused primarily on descriptive analytics when it comes to the supply chain rather than the more mature predictive and prescriptive analytics.

In a related question, APQC asked survey respondents to indicate their level of satisfaction with their organization's ability to access and analyze relevant supply chain data for timely decision making and reporting. Although a majority of respondents' organizations have a formal structure for supply chain analytics, only 2% of respondents are very satisfied with their ability to access and analyze data. Twenty-one percent indicated that they are satisfied; a majority (61%) indicated that they are only moderately or slightly satisfied. These results indicate that organizations still have progress to make when it comes to the implementation of their analytics efforts. Simply adopting analytics activities is not enough if there is not widespread access to data that can yield results.

Steps to improvement

Many organizations have room to improve the effectiveness of their analytics programs in the supply chain as well as the maturity of their analytics capabilities. To drive analytics efforts forward, one key step APQC recommends organizations take is to further develop their capabilities via an analytics team or program. Organizations should carefully consider whether it is possible for them to adopt a centralized structure for analytics programs. Doing so can provide strategic alignment, as well as central governance and accountability for analytics efforts. At the very least, organizations should establish analytics teams that function as service providers. This can increase buy-in and eliminate the potential for territorial behavior by other business units.

The analytics team should include well-appointed resources with the skills needed to serve overarching organizational goals. These resources can include analytics experts who know both the limitations and possibilities of analytics, and data management experts who know where to get the data and what it means. Organizations should also include domain experts who can define problems and know how analytics insights should be used for maximum impact.

Engagement and communication play important roles in ensuring that analytics efforts are embraced by those within the organization. Communication through leadership can ensure that direct reports are well informed on how data is being used to improve supply chain processes and can create transparency that makes employees feel they are part of the analytics effort. Perhaps most importantly, communicating successes related to analytics can help convince those within the organization that an analytics program is worth any process changes needed to obtain and evaluate data.

APQC also recommends organizations take steps to ensure their analytics efforts remain relevant. Organizations should continually refine their analytics program's alignment with organizational goals so that the results of analysis are relevant to any problems the organization wants to address. They can also provide opportunities to build on previous successes and refine data needs as projects change. Organizations should keep reporting simple by focusing on key measures, and they should evaluate measures at regular intervals. This provides the opportunity to consider whether they need to shift focus to accommodate changes in the business. Through regular evaluations, organizations can consider whether their analytics programs are working efficiently and provide value for the supply chain.

About APQC

APQC helps organizations work smarter, faster, and with greater confidence. It is the world's foremost authority in benchmarking, best practices, process and performance improvement, and knowledge management. APQC's unique structure as a member-based nonprofit makes it a differentiator in the marketplace. APQC partners with more than 500 member organizations worldwide in all industries. With more than 40 years of experience, APQC remains the world's leader in transforming organizations. Visit us at apqc.org, and learn how you can make best practices your practices.

Becky Partida is senior research specialist supply chain management, APQC
FIGURE 1
Areas of focus for supply chain analytics

(% highly agree/agree)

Reduce cost                                77%

Provide visual tools like dashboards to    75%
help people in my organization obtain
information in an easy to digest format

Improve customer satisfaction              73%

Improve productivity                       70%

Provide more accurate forecasts            68%

Reduce and mitigate risk                   65%

Contribute to supply chain optimization    61%

Source: APQC

Note: Table made from bar graph.

FIGURE 2
Use of analytics for supply chain activities

(% that use)

Scoring models for vendor                 69%
quality, cost, and stability

Detailed demand forecasting at the        58%
level of point of sale (store level,
retailer, distribution channel roll-up)

Safety stock level recommendation         56%

Optimize fulfillment logistics to         48%
account for handling, storage, or
warehouse constraints

Creating predictive models of             47%
different failure conditions using
sensor data

Optimizing shipment schedules             47%

Inventory budget optimization             47%

Integrated planning at the retailer,      45%
distributor, and channel level

Deviation analysis of forecast            44%
versus actual at the SKU level

Optimizing routes including backhaul      43%

Source: APQC

Note: Table made from bar graph.

FIGURE 3
Supply chain analytics structure

Centralized analytics function     32%
Decentralized analytics function   15%
Hybrid (combination of             29%
  centralized and decentralized)
No formal analytics                23%
  program or structure
Other                              2%

Source: APQC

Note: Table made from bar graph.
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Title Annotation:BENChMARKS
Author:Partida, Becky
Publication:Supply Chain Management Review
Date:Mar 1, 2017
Words:1805
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