The CPG supply chain frequently experiences a paradox-being swamped with data, yet lacking actionable information. Even though CPG companies have ever increasing access to demand signals from retailers, research indicates that U.S. retailers average approximately 8 percent out-of-stocks (OOS) during non-promotional periods and almost double that during promotional periods.

While some OOS issues can be addressed by increasing inventory levels, this approach can be costly and does not help the company to introduce new products that consumers desire, nor does it help to take quick corrective action when products do not perform as expected. In order to truly service consumers, CPG companies must be in constant touch with changing consumer tastes and preferences, and true consumer demand.

Powered by Data Sharing

CPG companies that can effectively sense true consumer demand earlier than their peers can make significant improvements in their inventory and service levels, and hence increase market share, revenues and profit margins. Research indicates that these companies have found ways to effectively share and use data provided by retailers.

Effective data sharing with retailers helps CPG companies plan manufacturing and replenishment based on true consumer demand rather than the less accurate demand forecast. It enables them to produce, replenish, and stock the retail shelf with the right product in the right quantity at the right time. The overall effect is fulfilling true consumer demand with lower inventory, reduced risk of OOS, and lower overall supply chain costs.

Retailers gather tremendous amounts of data from consumers through point-of-sale (POS) technologies and share them with their suppliers. To translate this data into visible results at the retail shelf, CPG companies must transition raw data to information, to business insights, and finally, to actionable steps. 

Doing this requires an organized strategy with many complex considerations. These include:

Process Considerations: A core consideration is whether to implement a true consumer-centric supply chain (CCSC) driven by actual store-level consumer demand or a pseudo CCSC wherein a retailer's warehouse withdrawals provide a surrogate for actual store-level consumer demand. Other important considerations include how to translate data analyses into business insights as well as the measurement of how well the extended supply chain is servicing strategic retail partners.

Data Considerations: Maintaining master data accuracy and ensuring data synchronization with data providers is a prime consideration. Additionally, the integration of POS data from proprietary systems and syndicated channels as well as the subsequent cleansing, harmonization and normalization of data to facilitate analyses is critical. Lastly, because insights are time-dependent, the crucial aspects of shared POS data are timely availability and usability.

Technology Considerations: The technology chosen has to be scalable and cost effective. Sizing should take into account richness of data as well as the necessary rollout across different product groups, data types, retailers, geographies, etc. The decision to host internally or externally should take into account capital expenditure, implementation expenses, custom development expenses, ongoing maintenance, etc.

Integration Considerations: Integration with external data providers as well as internal enterprise systems must be addressed. Factors that need to be taken into account include integration requirements for each external data provider (network adapters, EDI, etc.) as well as data requirements (type, format, time, etc.) for various internal enterprise downstream systems.

Driven by Demand Signal Repository

Timeliness is the key in data sharing, yet research indicates that it takes the majority of CPG companies more than two weeks to sense changes in true channel demand. Unless CPG companies can access and analyze data quickly and meaningfully, it has no operational value.

In order to quickly and meaningfully utilize data provided by different retailers, CPG companies must map data in varying retailer formats to a common format. A strategic approach that utilizes a central database to manage all incoming demand data and makes it available in the appropriate format to all internal departments and processes is warranted. This database, called a demand signal repository (DSR), would receive large volumes of downstream demand data (POS, EPC, RFID, etc.) from multiple sources (retailers, syndicated channels, etc.). It would cleanse and harmonize this data prior to making it available to various enterprise systems as needed.

CPG companies can leverage DSR to quickly transform downstream demand data into actionable information that can drive profitable growth for both the retailer and themselves.

Have DSR, Shared Data-Now What?

A CPG company needs to do much more than just implement a DSR to achieve consumer-centricity. While a DSR makes available near real-time downstream data in an easily usable form, the insights and actionable information derived from the analysis of this data actually pave the road to consumer-centricity.

Consumer needs should be the focal point of the extended supply chain. The discussion below provides a glimpse of how data from a DSR can be leveraged by some of the major supply chain processes to facilitate consumer-centricity.

Supply Chain Planning: The inability to sense shifts in true consumer demand on a timely basis is one of the key shortcomings of a traditional CPG supply chain. DSR provides a foundation to overcome this by providing near real-time data. Insights derived from the analyses of DSR data provide a CPG company with the feedback to fine-tune its demand management processes.

DSR also facilitates improvements in supply planning processes. Root-cause analyses conducted on retailer data and resulting insights can drive a wide range of improvements that address product data, order accuracy, retailer inventory accuracy, store/shelf replenishment practices, shelf space allocation, planogram compliance, etc. Collectively, these improvements lead to a sustained reduction in OOS.

Retailer data can also be analyzed to pinpoint specific demand spikes, such as the day of the week and time of day when sales peak at different stores. This would enable the CPG company to adjust its production and delivery schedules to ensure product availability at stores accordingly.

Supply Chain Execution: The ability to sense true channel demand in near real-time enables a CPG company to execute to actual consumer demand.

One example of such analysis is real-time forecasting. This technique utilizes advanced statistics to fine-tune near-term forecasts based upon POS data, channel shipments, order patterns, and POS forecasts. More accurate near-term forecasts, along with retailer inventory data and other supply chain parameters facilitate generation of more accurate order forecasts and associated shipment forecasts. Accurate shipment forecasts can drive production schedules to optimally utilize critical production resources and distribution plans for effective execution of near-term demand fulfillment.

POS data and forecasts also provide the ability to flag item, store and week forecasts provided by the retailer for inaccuracies that exceed a certain threshold. Subsequent collaboration to address the root cause of the inaccuracy helps in reducing future POS forecast inaccuracies. It also further strengthens the bilateral relationship by reducing OOS at the retailer and reduces lost revenue for both parties.

Another example is postponement and hedging facilitation for short lifecycle products (e.g., high-tech, fashion apparel) where the traditional over-commitment to finished products is replaced by a balance of lowered commitment to finished products and commitment to raw materials and production capacity. In such instances, POS data early in the season facilitates decisions on identifying the winners from the rest and committing raw materials and production capacity to ensure their timely availability using risk optimizing techniques.

Management of New Product Introductions and Promotions (NPIP): With major retailers having the capability to transmit POS data multiple times a day and the increasing adoption of RFID technology, data from a DSR provides near real-time feedback to more effectively monitor and manage NPIP by identifying: performance across the targeted market, stores which prefer the new product or promotion, and availability of the new product or promotion at the store-level.

Further analyses can also be conducted to identify OOS, sales voids, and distribution voids. Proactive responses to such store-level situations maximize NPIP effectiveness.

Conclusion

A consumer-centric supply chain is powered by downstream demand data. The ready availability of such data in a usable form can be facilitated by a DSR. However, DSR is a necessary but not sufficient condition for enabling a consumer-centric supply chain. Data from the DSR needs to be analyzed subsequently to develop insights and generate actionable information.

Effective use of actionable information enables a CPG company to align its sales, marketing and promotional efforts with consumer consumption insights, and efficiently match its supply-side capabilities with consumer demand. Consumer-centricity is the ability to then effectively bridge the chasm between the CPG company's planning and execution activities and the consumer's first moment of truth-availability of the right product, at the right shelf, at the right store, at the right time.

Sunil Desai is principal and Anil Pahwa is senior principal with the Retail, CPG & Logistics Practice at Infosys Consulting (www.infosys.com).