In my last article, I talked about broad themes around why enterprise data management is an impediment as well as a strategic opportunity to unlock business value depending on how you address it in today’s dynamic and fast-converging landscape. My next few articles will explore specific business value enablement areas to help establish direct linkages with data management, starting with a quick look at how product data is critical to provisioning the 21st century hyperactive supply chain networks for manufacturers.
Why Enterprise Data Management Matters for Supply Chain Integration
It’s not easy being a manufacturer these days. Developing the best products, executing on sound marketing and channel strategies, and improving manufacturing processes are not nearly enough; flawless supply chain execution is the name of the game for companies across many industries.
Imagine the following confluence of market and competitive forces for high-tech manufacturing:
- Powerful margin pressure from declining application service providers, requiring continuous improvements to manufacturing yields and overall supply chain performance.
- Very large plant, equipment and working capital needs in the face of boom and bust cycles.
- A relentless pace of technical innovation, with product lifecycles spanning six to 18 months – simultaneously accelerated by competitors and demanded by consumers.
- Increasing product complexity with multi-core and multiple function chips, the proliferation of personal electronic gadgets, customers that increasingly demand customization and an ever-increasing demand for smaller, faster and cheaper chips.
- The convergence of computing, wireless and media, leading to a future state that is difficult to predict and must involve thoughtful strategic options to mitigate risk.
- Competition with outsourced manufacturing companies that can focus their resources on developing design competencies without requiring huge capital investment.
- Increasing and complex collaboration demands from partners and customers for supply chain execution, demand synchronization, design-win cycles and procurement planning.
- Stringent environmental regulations – including restriction of the use of certain hazardous substances and waste from electric and electronic equipment, which require increased traceability and accounting for products.
Many manufacturing companies don’t have to imagine this scenario - they live and breathe it everyday. And they are acutely aware of the importance of an efficient and flexible supply chain to survive and thrive in this demanding environment.
Multiple supply chain data integration issues and bottlenecks around demand and supply planning, order management, sourcing and procurement, fulfillment, inventory management and manufacturing complicate the operational enablement further. Product data is the lifeblood of any engineering, manufacturing and supply chain operation. Simply put, if improvements are made to other aspects of the supply chain information assets but your product data is a mess, you will have only improved your ability to provide suspect and often misleading data faster and more efficiently, both inside and outside of the company. Therefore, without effectively addressing product data consistency and integrating product lifecycle management with supply execution, the overall transformation vision of an integrated, extended and hyperactive supply-chain with end-to-end visibility benefits cannot be realized.
Typical product data management challenges are complex, diverse and pervasive, creating a variety of supply chain headaches, including missed orders, long lead times, inefficient logistics and excessive inventory, which ultimately contribute to reduced profitability. Common challenges include:
No single source of truth: The proverbial organizational silos best describes the state of supply chain operations at many manufacturing firms. Product design, inventory management, supply planning and enterprise resource planning each have their own systems and processes, and there is no consistent process for managing product and materials data across the enterprise - resulting in inconsistent bill of materials data, product nomenclatures, attribute names, types and domain values. Working around all of these system and data problems requires a huge amount of manual effort.
Lack of product model flexibility and traceability: Part numbers are “intelligent” and overengineered; in other words, every digit in the part number has a coded meaning. For example, if any attribute for a particular part changes, but it does not affect form, fit or function of the part, the part number may still need to be changed if the attribute is included in the part number nomenclature. Therefore, instead of revising the existing part, a new part is created. This cripples the ability to track the revision history and turns data managers away from using revisions effectively to manage parts.
Mismatched supply and demand: Account managers create individual forecasts in Excel and the forecasts are manually consolidated. Unfortunately, the product structures used for forecasting often don’t match supply execution data, which makes it nearly impossible to align supply with demand. This results in batch data processing, workload spikes and fire drills.
Partner collaboration demands: Whether it is product design and engineering, manufacturing or logistics, partners expect a tight collaboration process and timely data exchange in agreed-upon format and protocols. Certain industries such as consumer products and retail have stringent requirements to comply with industry standard data exchange protocols such as 1SYNC, UCCnet and Transora. In addition, partnerships with large enterprise customers, contract manufacturers and vendors also put pressure on the operations manager for tailored data preparations and transfers from static apps.
The “our company or industry is different” mentality: When it comes to supply chain data management, many companies tend to believe they are unique for the following reasons:
- Custom, innovative point solutions built to work around inherent operations or data problems,
- Complex BOM connections from inverted, multilevel product structures,
- Frequent product data changes resulting from the rapid pace of innovation,
- Nonstandardized application data structures and integration with customized applications, and
- Unique and custom product design, assembly, packaging and its modeling.
Merger and acquisition complexity: The issues highlighted above are further complicated by frequent M&A transactions. M&A comes at a cost, including additional data management, integration, system and organizational complexity. Most of the companies have different systems, processes, data models and organizational constructs for managing products and materials.
For this reason, many incorrectly assume it is impossible to completely resolve supply chain data issues. It actually requires a paradigm shift and significant change management challenge. However, effective data management and integration is a must to enable strategic supply chain capabilities – be it item traceability standards and technologies, demand and supply matching, sales and operations planning, supply chain intelligence or extended supply chain integration. An integrated, multidisciplinary approach to manage your data across your extended supply chain enables these strategic capabilities as well as key transformation initiatives such as tight design-win cycle and partner collaboration, cost reduction and inventory rationalization, demand-driven supply execution and product lifecycle management to integrate engineering changes with rest of supply chain execution. Key tenets of such data management disciplines include:
Master data management: proactive management of key master data objects including product, materials, configuration, routings, pricing, locations and vendors with appropriate process, tools and people capabilities to ensure the integrity and consistency of data across the supply chain.
Harmonized supply chain data architecture and bill of materials: the development of a single, rationalized product data model and industry standard BOM for the entire company, with a consistent part number nomenclature and standardized system interfaces for product data exchange based on an attribute-based product data structure rather than nomenclature.
Standardized lifecycle processes: company-wide product data and lifecycle management processes, with defined stage gates for standardizing the product definition, setup, and change management workflows. Clearly defined and implemented lifecycle stages govern the set of business actions allowed for products across the supply execution. This includes adoption of industry standard frameworks and leading practices for process structures and workflow discipline, while also allowing for flexibility to address time-to-market pressures.
Integrated technical architecture: a data flow architecture built around flexible data services and a publish-subscribe model for core data objects. This is important in establishing a single version of truth and federated architecture to control the enterprise data definition, while allowing localized flexibility to manage supply chain attributes.
Proactive governance: Tactical alignment of product data specialists to provide centralized governance and control over enterprise core master data including creation, updating, management, and reporting for overall supply chain. Product data ownership formalized with specified business data owners for each master data attribute.
Example Operational Metrics
Other Benefit Examples
Out of Stocks:
3-5 percent reduction expected
Reduced complexity - Significant SKU reduction results in reduced cost of managing product data.
Shorter lead times - The average lead time for finished goods is expected to drop significantly.
Lower inventory costs - Cycle stock reduction is expected to reduce inventory costs by millions on an ongoing basis through better parts traceability.
Demand-supply matching – Improved ability to implement demand-pull processes and match supply execution with demand forecasts.
Greater collaboration – It is easier for different partners to collaborate through the design and supply execution.
Supply chain visibility – This is improved through standardized data and common definitions.
Speed to Market:
A few weeks reduction for New Item Introduction
Distribution / Store
7-10 percent reduction in basic logisitcs labor costs
Reduced Handling of basic item info queries
Invoice Dispute and Reconciliation:
5-10percent account management and accounting labor savings
Reduced invoice write-offs
Purchasing Data Integrity:
On average, 8 percent of POs have errors
0.2 – 0.7 percent reduction in
outbound logistics costs
0.5 - 2 percent reduction
Though the extent of benefits enablement vary based on industry-specific supply chain issues, commonly observed tangible business benefits realized by such initiatives include:
Most global manufacturing companies that have a high stake in supply chain execution success and invess heavily to continue to scale and innovate are facing these common bottleneck issues. While everyone strives for agile, adaptive supply chain networks, there are four key supply chain characteristics that enable the desired end state – integration, collaboration, efficiency and elasticity. And, effective data management and streamlined architectures are key to enabling the required capabilities to continue to enhance on these key parameters. Leading companies have started to adopt appropriate standards and manage product data harmonization across their supply chain. These organizations are realizing that data management provides the foundation to their supply transformation initiatives. Supply chain problems are complex and it’s typically a gradual transformation journey to scale and innovate. While data alone is not going to solve your supply chain operations issues, it is often at the core of many challenges and can certainly facilitate applying corporate resources to process and operations re-engineering, if addressed in advance.
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