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A Smarter Path to Efficient Retail Replenishment | Retail Velocity

A Smarter Path to Efficient Retail Replenishment

Back in the “good old days,” it was much easier for consumer goods (CG) companies to sell into and replenish retailers’ stock.

Once products were manufactured, shipped, and invoiced, it was the retailer's responsibility to warehouse products, stock shelves, and sell the product. Over the past 20-30 years, however, the relationship between suppliers and retailers has become more complicated as retailers adopted Just in Time inventory or QR/ECR (Quick Response/Efficient Consumer Response) processes and deployed more advanced systems.

CGs have also been accustomed to forecasting consumer demand indirectly from historical orders and shipments or by purchasing data from syndicated data providers such as NielsenIQ and Circana (formerly IRI and NPD Group). However, that data would be weekly or even monthly and didn’t represent true demand. The data would also not account for inventory in retailers’ warehouses and stores.

Today, retailers are demanding that suppliers ship more frequently in smaller quantities and within narrower delivery windows. On top of that, they demand that suppliers collaborate with them to minimize store inventories while simultaneously eliminating out of stocks (OOS). Although CGs recognize revenue when they ship inventory, their revenue isn’t truly committed until consumers buy their products because of the burdens retailers place on their suppliers to deliver, manage, and sell through their inventory. With online sales and company stores, that’s already happening.

Retailers now provide suppliers with their proprietary sales, inventory, and merchandising data to collaborate on sales planning and store replenishment planning and execution. Instead of emailing weekly top line sales numbers to account teams, retailers offer automated feeds of distribution center (DC) and store-level sales, inventory, and merchandising data to facilitate more efficient replenishment practices.

CG supplier VS. RETAILER PRIORITIES and challenges

Retail Replenishment Priorities

Retailers and CGs have different priorities for collaborating with this data for store replenishment. Retailers want to reduce carrying costs and retain shopper loyalty by minimizing inventories in their warehouses and stores while maximizing consumer availability. Sharing point of sale (POS), inventory, and merchandising data enables their suppliers to be more proactive in fulfilling their replenishment orders and monitoring retail trends and inventories.

CGs want to minimize costs and maximize brand loyalty by optimizing production, minimizing their inventories, and maximizing retail availability. For suppliers, retail data is invaluable for improving forecasts, eliminating bullwhip effects due to consumption latency, allocating more effectively during fulfillment, tracking promotional performance, and predicting out of stocks.

Retail Replenishment Challenges

Naturally, both CGs and retailers have common objectives for store replenishment:

  • Improve inventory planning, distribution, and availability for shoppers.
  • Eliminate retail out of stocks, CG cuts, and cancellations.
  • Improve demand and sales forecasts.

Attempting to meet these objectives, though, has changed responsibilities and processes throughout a CG enterprise. As a result, multiple departments need to work collaboratively to optimize store inventory replenishment while maximizing in-stock levels and ensuring shelves are consistently stocked. However, this comes with several challenges:

  • Manufacturing no longer generates demand planning forecasts solely from historical orders or shipments. Incorporating consumer demand is essential for accurate demand planning and forecasting, requiring access to weekly, if not daily POS data.
  • Distribution and logistics planning requires not just retailers’ orders but also their current inventory levels and capacities to act proactively to their needs.
  • Account teams are charged with tracking retailer forecasts, orders, POS, inventory, days of supply, and in-stocks in addition to merchandising metrics.

This poses several questions that CG companies must answer:

  1. Do marketing forecasts, account team forecasts, and manufacturing forecasts align?
  2. Are forecasts generated from historical orders or from historical POS? POS data is a more accurate gauge of consumer demand than orders but can be harder to implement if you don’t have a reliable automated data collection, cleansing, and harmonization solution.
  3. How accurate are CG warehouse inventories vs. retailer inventories plus orders?
  4. How will product cuts affect retailer OOS?  
  5. Can allocations be adjusted to each retailer’s “need” (i.e., order plus inventory) to minimize OOS?
  6. Will production plans satisfy retailers’ current and future needs (i.e., total POS forecasts plus current inventories)? Will there be surprises or will underproduction/overproduction be accurately predicted and corrected?

OPPORTUNITIEs for effective store replenishment—and its benefits

Enterprise Collaboration

Conquering those challenges and effectively meeting retailer replenishment needs requires new data management processes and systems within a CG enterprise. Business intelligence tools and data warehousing need to be easily shared and accessed across departments so teams can better collaborate to make strategic, data-driven decisions that drive profitable growth.

To accomplish that, all retail data needs to be clean, harmonized, and normalized to produce a single version of the truth that can be shared among all departments so they can plan, execute, and measure most effectively. For example, product hierarchies and quantities need to be converted for each department based on its needs but then still shared across departments in one language.

The sharing of harmonized data benefits the following groups and areas:

  • Marketing (brand, advertising, national promos, etc.)
  • Sales (account management, sales planning, trade promotions, replenishment)
  • Manufacturing (forecasting, production)
  • Logistics (CG warehouses, allocations, shipping)

Armed with clean, accurate, and timely retail data, CG companies and key business teams will be able to do the following:

  • Collaborate with retailers on stock replenishment from the time of production to consumption.  
  • Manufacturing can develop forecasts based on historical POS data and not on orders or shipments.
  • Sales forecasts can be aggregated across retailers from their retailer forecasts or from internal sales plans based on POS data.
  • CG warehouse inventories can be based on orders, retailers’ current inventories (i.e., “on-hand inventory”), and total capacities (“order up to”) at each DC and store, in-transit shipments, and forecasted POS (“future Weeks of Supply”).

Inter-enterprise Processes/Collaboration

New inter-enterprise data collection and management processes are also required between each retailer and/or distributor. Every retailer has different systems and provides different fields of information in different formats. In order to incorporate this data into a CG’s internal systems, it requires automating the acquisition, cleansing, and harmonization of the data for daily consumption by each department. In addition, retailers’ proprietary data must be properly secured between teams and be only accessible on a “need-to-know” basis.

CONCLUSIOn

Each CG needs both a data management philosophy and a solid data strategy to operate in the new retail environment, not just for dealing with online retail but also to adapt to the entire evolving retail landscape. This requires executive-level commitment and a sound investment in common, sophisticated systems and methods used across the company.

The results of this investment can be plentiful:

  • Reduce forecasting errors and eliminate bullwhip effects.
  • Optimize warehouse inventories to match retailers’ inventory needs by tracking retailer in-stocks and carrying capacities at DCs and stores.
  • Calculate shelf capacities and inventories to predict orders before they occur.
  • Gain full demand chain visibility at an item and store level.
  • Accurately track sales trends throughout each selling season.
  • Track sales and store replenishment from new product launch to product discontinuance.
  • Track seasonal sales from retail reset to markdown.
  • Accurately measure trade promotion effectiveness within retailers and cannibalization across retailers.
  • Measure consumer trends by channel: retail, e-tail, company stores, and wholesale.
  • Improve brand development and pricing strategies.

Retail Replenishment Key Metrics

Here are the key metrics that CGs should focus on to improve store replenishment, based on the timeline of inventory flow:

  • Demand Forecasts (CG production forecasts by retailer and retailers' POS forecasts)
  • Orders, Cuts, and Cancellations
  • CG Warehouse Inventory
  • In-transit Product
  • Receipts (DCs, stores)
  • Inventory (DCs, stores)
  • Retailer Returns
  • Markdowns
  • POS (currency sales, units sold, units returned)
  • OOS (retailer KPIs)

Even though the "good ol' days" have passed, it doesn't mean that CG suppliers need to suffer through the retail replenishment process and risk lost sales, revenue, and consumer loyalty. As previously mentioned, there is substantially more valuable retail data available from retailers today than any other point in time—the key is you need to be able to easily collect it (preferably at the SKU-store level), clean it, unify it, and share it both internally and externally with your retailer partners.

If you can do that, such as with a proven retail data platform like VELOCITY®—combined with a solidified, company-wide data management philosophy—you'll have the most reliable data, analytics, and granular insights needed to make smarter, data-driven decisions that will ensure sufficient inventory is always available.

 

Want to learn more how Retail Velocity can help your company keep its products and profits moving? Contact us today.

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