From chaos to insights Transforming disconnected data into actionable intelligence

From chaos to insights | Transforming disconnected data into actionable intelligence

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IntroductionDisorganized and disconnected product data can be a major deterrent to effective product development and supply chain operations within consumer packaged goods organizations. As a result, significant opportunities for deriving actionable insights from data can be missed, thus suppressing the ability to achieve further margin improvement and improved product development cycle times.

From chaos to insights | Transforming disconnected data into actionable intelligence

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Insi

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Figure 1. Foundations to becoming a viable, digital organization

Consider a $3B+ organization with more than 30,000 products, managing product data manually through spreadsheets and Word documents. Does this sound alarming?

It should. Unfortunately, this is more common than not within the consumer packaged goods (CPG) industry.

On average, CPG companies have reported an 8 percent increase in technology budgets over the past three years, indicating an increase in digital technology investments. However, many of these companies are missing a cohesive data management strategy: a component critical to supporting digital technology investments. With the lack of attention to the current state of data, insightful results often cannot be developed, and the promised benefits of going digital can diminish.

How should organizations focus their efforts and help ensure they are taking the right steps to achieve improved returns on their investment?

• Develop a data strategy and governance program across enterprise initiatives and make data a mandatory first step toward any transformation program

• Invest in data transformation tools to aggregate and digitize data for prioritized business use cases within the larger organizational transformation

• Implement technology to house future product data within a single source of truth

• Utilize advanced analytical tools and techniques to derive insight from newly transformed and accurate data

To build a strong foundation for digital transformation and enablement, companies should follow these steps as depicted in figure 1.

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3

2

1

Utilize advanced analytics tools to derive insights

Implement technology as single source of truth for product data

Invest in data transformation tools

Develop enterprise data strategy

Chao

s

As an outcome from companies focusing efforts and investments on improving product data, significant R&D and supply chain benefits can be realized,2 including, but not limited to:

• Achieving end-to-end visibility to core product information in hours and realizing savings in months

• Improving product cycle times and time to market, resulting in revenue acceleration

• Achieving end-to-end product traceability to reduce cost of quality metrics (for example, internal and external failures such as scrap, rework, and recalls) through integration of processes

• Reducing compliance, regulatory, and quality risk by integrating systems and processes to ensure specification and bill of material (BOM) data is accurate and complete

• Reducing proliferation of ingredient and packaging material and therefore rationalizing redundant parts and suppliers under management

• Minimizing new product development and direct material costs through consolidation of preferred suppliers at negotiated prices

• Increasing internal product development resource capacity by moving resource responsibilities from conducting daily data management tasks or redundant tasks to more value-added activities, such as innovation and growth projects

• Providing quick identification of sales opportunities and revenue contributions, since real-time data and feedback can be used to develop new product lines or customized solutions for customers

• Improving internal collaboration across the supply chain, breaking down functional silos

From chaos to insights | Transforming disconnected data into actionable intelligence

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From chaos to insights | Transforming disconnected data into actionable intelligence

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Most CPG organizations are thinking about being digital, but may not grasp the totality of steps necessary to act. The ability to manage product data is a key component in driving digital adaptation within any industry.

As depicted in figure 2 below, CPG companies today are still lagging in the stages of “passively digital” and “exploring digital,” but may already have lofty enterprise-wide strategies and goals to “become digital.” A major deterrent to executing faster digital transformations and reaping maximum benefits in “becoming digital” can be an organization's lack of data management capabilities that would deliver strong product data quality and accuracy.

An organization that is in its early stages of digital maturity may have chaotic product data scattered across 10 to 20 different online and offline systems with little systems and

business process integration. With scattered, offline data, internal R&D and supply chain resources are then required to spend most of their days manually working around business processes, rekeying data inputs across unintegrated systems and repeatedly verifying inputs from different functions. Resources are tasked with non-value-added activities rather than focused on higher-level strategies, such as enterprise growth and innovation.

Organizations that attempt to “go digital” without first developing and implementing an enterprise data strategy, governance, and readiness program to manage and fix this chaotic product data will most likely struggle to realize the benefits promised from further digital technology investments.

CPG organizations have just started on the digital transformation, trailing other industries

Figure 2. Digital maturity model for CPG (consumer packaged goods) companies

If you arePASSIVELY DIGITAL...

. . . your strategic choice is to

REACT

If you areEXPLORING DIGITAL...

If you areDOING

DIGITAL...

. . . your strategic choice is to

INTEGRATE

Relying on paper forms or manual rounds for asset

monitoring

. . . your strategic choice is to

ANTICIPATE

Testing IoT monitoring on assets, activating

site-wide sensors and control systems

Providing workers real-time data on

assets performance and product specs

If you areBECOMING DIGITAL...

. . . your strategic choice is to

COLLABORATE

Sharing assets and product data with

customers and suppliers, synchronizing demand

If you areBEING

DIGITAL...

. . . your strategic choice is to

ORCHESTRATE

Autonomous operations "digital supply networks"

From chaos to insights | Transforming disconnected data into actionable intelligence

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The CPG industry currently lags behind other industries, with major organizations maintaining massive amounts of data scattered across the globe. Many multinational CPG companies are likely managing zettabytes of product data with poor data governance and quality management.

Poor data quality could lead to significant problems, including:

• Increased product development lead times and time to market, resulting in significant project churn and lower customer satisfaction

• Proliferation of specifications, bills of materials (BOMs), suppliers, materials, and ingredients, resulting in higher risk for downstream errors and increased operational costs

• Duplicated information and resource efforts, resulting in wasted time and funding and internal confusion

• Creation of a company culture that lacks accountability of data ownership among various functions, both regionally and globally

• Higher risks for quality, regulatory, and compliance conformance, resulting in more rework and potential recalls

The focus for all major CPG organizations should be to build a strong data foundation, to enable digital transformation and to support future innovation.

From chaos to insights | Transforming disconnected data into actionable intelligence

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From chaos to insights | Transforming disconnected data into actionable intelligence

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Steps to begin an enterprise data transformationTo help achieve benefits such as true cost visibility and substantial realized savings, companies should make data management the top priority within their organization and the first step to any transformation program. Data management and transformation begins with a data readiness program that assesses your unstructured data across the enterprise and identifies business value opportunities.

Steps to support transforming chaotic data and gaining insight:

• Bring unstructured data into a staging database, where the data can be cleansed and enriched with critical attribute data, be made free of duplication, and rendered in consistent, standardized language

• Move data to a single source of truth, where it can be made searchable, accessible, and available for analysis to drive insights

• Utilize analytical tools to determine insights that can lead to cost reduction, higher product quality, increased profitability, and the ability to accelerate product delivery

Enablers for transforming the chaotic product data into insights

Cognitive and analytics tools are critical to any data management strategy, as they provide insights from usable product data. These tools accelerate the digitization of unstructured data by enriching the data and improving data quality. Once the data is digitized, enriched, and harmonized, these tools can provide critical, automated metrics, such as the percentage of recycled packaging components for a product.

Deloitte case study: Data transformation

A multibillion-dollar food and beverage company has embarked on a multiyear global program to deliver end-to-end product traceability, establishing the ability to track and trace food items through all phases of supply, manufacturing, and distribution from farm to consumer.

Situation and key findings

• Data was largely unstructured and scattered, residing in a nonstandardized system

• Less than 6 percent of specification data existed in a standardized enterprise platform

• Translation of specs from old to new system and review and cleansing of data was highly complex

• Prior data capabilities were slower; resource capacity was constrained; and manual, off-line tools were being used (such as Excel for migration)

Key results

• Deloitte helped accelerate data transformation using a creative framework and modern, world-class data solutions, and additionally designed and built a new data hub for product traceability

• Project deployments were moving from a nine-year roadmap to a three-year roadmap, and spec data transformation was accelerated 10x

Useful tools

Deloitte’s cognitive database, Deloitte Product Data X-ray (DPDx), helps accelerate digitization of unstructured data to improve data quality and product data harmonization, as well as to derive insights from unsupervised learning algorithms.

Deloitte’s DesignSourceTM platform then leverages the digitized product data and combines it with procurement data to deliver insights about material cost savings.

From chaos to insights | Transforming disconnected data into actionable intelligence

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Deloitte case study: Generating insights

A 6 to 8 percent reduction in direct material spend was identified through multiple material consolidation opportunities for a $22B food and beverage company.

Through the application of Deloitte’s DesignSourceTM analytics tool, the organization’s technically equivalent materials were identified by analyzing specification and procurement spend data, uncovering that:

~80% of maintained specifications were inactive

~24%of ingredient and packaging materials were not linked to any specifications

In addition, increased data integration and accuracy from their PLM system resulted in $28.4M of identified savings. An additional savings opportunity of $6.5M was identified from consolidating to low-cost ingredient specifications.

In addition, supplementary insights can be derived from the newly digitized, enriched, and harmonized data. The data is merged with supplier, procurement, and third-party benchmark data to clearly identify cost and complexity reduction opportunities across the enterprise. For instance, when combining this digitized product data with procurement data, significant material cost savings opportunities can be identified. Improved bill of materials (BOM) data quality can also lead to better demand planning and forecasting.

Leveraging these cognitive and analytics tools in the case of direct material cost reduction analysis can further:

• Assess design and specification parameters and report material and ingredient redundancies

• Provide visual recommendations, showing equivalency and mapping against all potential suppliers

• Enrich data, with abilities like intelligent extraction from drawings and leveraging internal or third-party data to benchmark raw material costs

• Drive cost reduction through rationalization where no form, fit, or function difference occurs by quickly identifying duplicate and identical raw materials, as well as close substitutes

• Promote reuse by creating design standards and incorporating attribute data into PLM and ERP systems

• Take product data across the supply chain, integrating point-of-sale and product data to segment customers and improve products

Conclusion

The CPG industry has fallen behind compared with other industries in terms of deriving insights from data and digital transformation. To achieve maximum benefits of 'going digital', organizations should attain senior leadership commitment to begin investing in product data transformation enablers. Manual data management and processes without governance and high-quality data can be expensive and inaccurate. Once companies take these first steps to build a strong data foundation, they are well prepared to invest in network technologies to drive an End to End digital transformation. Enabling accurate product data by creating a single source of truth, integrating systems, and designing complementary business processes, frees up internal resources to focus on enterprise innovation and growth.

Potential next steps:

• Develop a data strategy and governance program across enterprise initiatives and make data a mandatory first step toward any transformation program

• Invest in solutions with improved digital technologies for prioritized business use cases, along with the larger transformation programs

• Receive senior leadership commitment to take individual business units into the future with insightful data

Companies that fail to invest in product data readiness and transformation will soon fall short of their industry competitors.

From chaos to insights | Transforming disconnected data into actionable intelligence

From chaos to insights | Transforming disconnected data into actionable intelligence

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Endnotes1. Ellen Eichhorn and Stephen Smith, “2019 CIO Agenda: Consumer Goods in a Climate of Change,” Gartner, June 22, 2018.

2. Based on Deloitte’s own consultative experience of working with clients on product life cycle management assessments and implementations.

Learn more

Carmine IzziPrincipalDeloitte Consulting LLPcizzi@deloitte.com+1 212 436 2305

Venkat RavichandranSpecialist LeaderDeloitte Consulting LLPvenravichandran@deloitte.com+1 212 618 4090

For more information, please contact:

Special thanks to Nick Greos, Linda Zhang, and the editorial team for their valuable contributions to this point of view.

Stavros Stefanis PrincipalDeloitte Consulting LLPsstefanis@deloitte.com+1 617 437 2352

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