Are Your Omni-Channel / E-Commerce Sales Really Profitable? Part 4

Richard Sharpe Analytics & Big Data

 

This is the final posting of this series focused on Omni-Channel / E-Commerce profitability.  The focus of this posting is on the impact of product returns.

We have defined the four components of the Total Cost To Serve (TCTS) for Omni-Channel / E-Commerce orders to be:

  1. The cost to purchase or manufacture the products, often referred to as the product’s Standard cost
  2. The costs to position inventory to be ready for order fulfillment activities
  3. The costs to actually fulfill the Omni-Channel consumer order, and
  4. The cost of product returns

After defining these costs, we offered the straight forward profitability equation of:

Omni-Channel Order Profit = Net Revenue – (A+B+C+D)

Today, we are going to specifically focus on the cost category above.

The problem

When consumers buy a product sight unseen, there is often a level of uncertainty about whether the product will be what they actually want. Retailers often offer free returns for customer satisfaction, an offer that consumers use to their fullest advantage. But how does that affect the overall profitability for the retailer?  Seem simple?  The answer is often not so obvious.

Let’s start with the revenue part of the equation.  A returned product turns a positive into a negative because the actual revenue received for the transaction has been returned to the consumer.  The loss associated with the order also has to account for all of the costs associated with the returns process.  These returned product costs can be more significant than most people realize.  Let’s break down product return costs in more detail.

Original Order Fulfillment Costs (sunk costs) – since the product(s) are being returned, the original order fulfillment costs are now not being covered by the revenues associated with the order.  Therefore, these are now sunk costs that need to be absorbed.

Inventory Carrying Costs – the length of time that a consumers holds the product can have a significant impact on inventory carrying costs.  When an initial order is filled, the typical inventory replenishment process applies which can mean that new replacement inventories have been ordered.  Therefore, in reality the seller of the product now has working capital tied up in inventory sitting at the consumer’s location, inventory in-transit as it is being shipped back to the seller’s receiving locations as well as new replenishment inventory.  This can significantly increase the levels of working capital tied up in product inventories.  Forecasting returns can help but most companies end up replenishing inventory to ensure there are no lost sales, given their lack of confidence in their data.

Return Transportation Costs – this category of cost is dependent upon whether the consumer pays the shipping fee to return the product.  If not, then return transportation costs can add significant increases to profit losses.

Secondary Handling Costs – assuming the returned product is placed back into storage or on the shelf, there are costs associated with the receiving, inspecting and put-a-way activities for the returned product.

Disposal Costs – if the product is not going to be placed back in general inventory and it is to be discarded or destroyed, then there may be a disposition cost associated with the returned product.

All of these costs are demonstrated in the visual below and can have a significant impact on the profitability of an Omni-Channel / E-Commerce channel.

Blog026_diagram_ecommerce_return

The solution

So how do retailers selling in the Omni-Channel / E-Commerce tackle this issue?  The solution starts by again recognizing the wisdom in the adage “one size does not fit all”.  Treating all customer product returns the same way is simply a formula for failure.

The solution starts by segmenting Omni-Channel / E-Commerce customers by understanding their overall net profit contributions over time.  This requires having specific and accurate facts regarding exact profit performance for Omni-Channel / E-Commerce customers including the frequency and impact of their product returns.

This form of segmentation enables the creation of tailored product return policies that help manage the negative impact on profitability.  Informed policies regarding how products are returned, if there are shipping fees, if charges apply for returned products or if there are defined time windows for products to be returned.

Of course this may drive some customers to shop with another retailer.  But that may not be such a bad thing from a competitive advantage perspective!

I would love to hear your thoughts.

All the best,

Richard

Richard Sharpe

Richard Sharpe is CEO of Competitive Insights, LLC (CI), a founding officer of the American Logistics Aid Network(ALAN) and designated by DC Velocityas a Rainmaker in the industry. For the last 25 years, Richard has been passionate about driving business value through the adoption of process and technology innovations. His current focus is to support CI’s mission to enable companies to gain maximum value through specific, precise and actionable insights across the organization for smarter growth. CI delivers Enterprise Profit Insights (EPI) solutions that enable cross-functional users to increase and protect profitability. Prior to his current role, Richard was President of CAPS Logistics, the forerunner of supply chain optimization. Richard is a frequent speaker at national conferences and leading academic institutions. His current focus is to challenge executives to improve their company’s competitive position by turning enterprise wide data from a liability to an asset through the use of applied business analytics.

on the RADAaR™ – June 2016

Competitive Insights, LLC

on the RADAaR™

a convenient resource for current Integrated Business Planning (IBP) information




are your omni-channel /
e-commerce sales really profitable? part 4

the focus of this posting is on the impact of product returns

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this is contrary to what they have been telling investors who have been pressurizing them to turn profitable


we appreciate your feedback, please respond to our five minute survey here
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Competitive Insights, LLC

4200 Northside Parkway NW, Bldg. 12 • Atlanta, Georgia 30327 • 770.922.4400
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Press Release: Competitive Insights listed as a Representative Vendor in Gartner’s 2016 Market Guide for Supply Chain Cost-to-Serve Analytics Technology




Competitive Insights listed as a
Representative Vendor in Gartner’s 2016 Market Guide
for Supply Chain Cost-to-Serve Analytics Technology

Atlanta, Georgia USA (May 26, 2016)--Competitive Insights, the market leader for Enterprise Profit Insights (EPI) solutions, announces it has been cited in Gartner’s Market Guide for Supply Chain Cost-to-Serve Analytics Technology as a Representative Vendor. Competitive Insight’s Net Landed Cost to Serve offers specific and actionable visibility into the total cost to serve for each product or Stock-Keeping Unit (SKU) to each end delivery location. Each EPI customer solution is tailored using the transactional data that resides in their multiple operating systems. The precise insights gained enable the formulation of specific strategies that drive fact based improvements in operational profits.

According to Gartner, “This research provides an overview of the supply chain cost-to-serve analytics market. Supply chain strategists can use this research to understand the definition, trajectory and representative players in the market.”

Competitive Insight’s President & CEO Richard Sharpe shared that “we appreciate and value the intelligence provided by Gartner through this important research. Competitive Insights takes great pride in receiving this mention and feels its validation of the value that we bring to our customers.”

Gartner, Market Guide for Supply Chain Cost-to-Serve Analytics Technology, Stan Aronow, 26 May 2016

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

About Competitive Insights, LLC

Competitive Insights, headquartered in Atlanta, was founded in 1998. The firm provides both supply chain Software-as-a-Service (SaaS) and professional analytical services to enterprise customers including some of the nation’s most recognizable brands. CI’s proprietary mix of software and services collects and processes customers’ transactional data from multiple sources to support cross-functional, fact-based decisions that support the reduction of operating costs, maximizing profits, and mitigating operating risks.

For more information, contact:

Tami Kitajima

Competitive Insights

[email protected]

+1 770.922.4400, Ext. 306

on the RADAaR™ – May 2016

Competitive Insights, LLC

on the RADAaR™

a convenient resource for current Integrated Business Planning (IBP) information




are your omni-channel /
e-commerce sales really profitable? part 3

E-Commerce order fulfillment activities simply do not have the economies of scale of traditional supply chain operations

join us

EyeForTransport’s
3PL Summit &
Chief Supply Chain Officer Forum

Workshop: Driving Sustainable
Business Value from
Supply Chain Data

Facilitator: Richard Sharpe
CEO, Competitive Insights, LLC

June 20, 2016 • 1:00 – 5:00 pm CT
Radisson Blu Aqua – Chicago, IL



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complexity reduction ::
supply chain optimization as a competitive advantage

study by Georgia Tech shows that a company’s stock price drops by 8% when the company experiences a glitch in its supply chain


customer buying patterns ::
measuring the true profitability of products, services and customers

from increased transparency comes actionable insight


we appreciate your feedback, please respond to our five minute survey here
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Competitive Insights, LLC

4200 Northside Parkway NW, Bldg. 12 • Atlanta, Georgia 30327 • 770.922.4400
www.ci-advantage.com

Case Study: ConAgra Foods

food processing($18 billion revenue)

makes and sells packaged foods available in supermarkets, restaurants & food service

business challenge

to identify the more efficient network design in order to consolidate 14 separate networks



We were very pleased with the deliverables for this project. The results were not just theoretical but grounded on the real needs of the business.
Stephen Tibey • Senior Vice President , Integrated Logistics • ConAgra


solution

network optimization

descriptive • prescriptive analytics
supply chain • finance • sales

data governance • model actual costs associated with each component of the product flows and running the Distribution Centers




  • 7 digit

    savings was identified with consolidated network



  • improved

    servicing customers with regional insights



  • insight

    to specific regional strategies




The results illustrated in this case study are specific to the particular situations, business models, data input, and computing environments described herein. Each Competitive Insights (CI) customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. CI does not guarantee or represent that every customer will achieve similar results.

Case Study: Fresh Food Distribution





















industry

fresh food distributor

($2 billion revenue)

business challenge

to identify the primary factors that were driving costs to climb and margins to erode






solution

product analysis

descriptive • diagnostic analytics
supply chain • finance • sales

data governance • organize and tie together data by each supply chain component • analyze profitability of each SKU

  • reduced

    costs by re-architect Customer Delivery routes



  • explored

    conversion of some Free-On-Board (FOB) Inbound Transportation to managed transportation



  • addressed

    negative margin position of specific SKUs



  • identified

    supply chain-related Data Quality issues




The results illustrated in this case study are specific to the particular situations, business models, data input, and computing environments described herein. Each Competitive Insights (CI) customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. CI does not guarantee or represent that every customer will achieve similar results.

Case Study: Hershey Company







food processing($7 billion revenue)

chocolate manufacturer

business challenge

to determine best product mix by production line and to support projected future growth



As Hershey pursues a strategy to go more global, there were some assumptions about how the supply chain worked and what the right decision would be that needed to go through the filter of objective analysis.
Greg Kaiser • Vice President, Global Logistics and Customer Service • Hershey Company


solution

network optimization

descriptive • prescriptive analytics
supply chain • finance

data governance • model extremely complex operations from manufacturing through customer deliveries




  • $3 million

    savings opportunities by changing product mix



  • 5%

    capacity gain opportunity identified



  • identified

    methods to handle increased demand




The results illustrated in this case study are specific to the particular situations, business models, data input, and computing environments described herein. Each Competitive Insights (CI) customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. CI does not guarantee or represent that every customer will achieve similar results.

Case Study: Monsanto

agribusiness ($15 billion revenue)

producer of genetically engineered seed and herbicide

business challenge

to improve alignment among the brands by either increasing utilization of existing distribution capabilities or developing new go-to-market strategies




The business insights that we obtained from CI’s approach in solving this problem were outstanding. They were able to take a very complex situation and bring clarity to our operational options.
Scott Dantuono • Distribution Manager • Monsanto


solution

network optimization

descriptive • diagnostic • prescriptive analytics
supply chain • finance • sales • marketing

data governance • model extremely complex operations from manufacturing through customer deliveries



  • reduced costs

    of inter-facility transportation with better product positioning



  • reduced storage costs

    with better facilities utilization



  • decreased

    operational complexity




The results illustrated in this case study are specific to the particular situations, business models, data input, and computing environments described herein. Each Competitive Insights (CI) customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. CI does not guarantee or represent that every customer will achieve similar results.

Case Study: Non-Metallic Mineral Manufacturing





















industry

nonmetallic mineral manufacturing

($500 million revenue)

business challenge

to identify near term, specific opportunities to reduce transportation costs






solution

transportation analysis

descriptive • diagnostic • predictive analytics
supply chain • finance • sales

data governance • segmented analysis into inbound, inter-facility and outbound to dive deep into transportation costs for savings opportunities

  • $2 million

    savings opportunity identified from low cost carrier selection



  • $1.3 million

    savings opportunity identified from carrier consolidation



  • $2.1 million

    savings opportunity identified from applying discount to high volume lanes



  • $400 thousand

    savings opportunity identified from shipping directly from plant instead of going through a Distribution Center




The results illustrated in this case study are specific to the particular situations, business models, data input, and computing environments described herein. Each Competitive Insights (CI) customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. CI does not guarantee or represent that every customer will achieve similar results.

Case Study: Hospira





pharmaceuticals($5 billion revenue)

provider of injectable drugs and infusion technologies

business challenge

to gain greater detailed performance insights into the operating network to determine if there is a better approach for servicing customers




I continue to be impressed by the system.
John Elliot • Senior Vice President of Operations • Hospira


solution

network optimization &
performance analysis

\ descriptive • diagnostic • prescriptive analytics
supply chain • finance • sales

data governance • cross-functional agreement on detailed performance information • tracking ongoing performance




  • $20 million

    savings opportunity identified



  • insights

    for SKU-level strategies



  • data refreshing

    revelaed profit opportunities that can be tracked



  • cross-functional

    use of profit insights




The results illustrated in this case study are specific to the particular situations, business models, data input, and computing environments described herein. Each Competitive Insights (CI) customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. CI does not guarantee or represent that every customer will achieve similar results.