DPP for profitability of food retailers

Direct Product Profitability

The goal of a Direct Product Profitability (DPP) project is to:

Improve sales and gross margin by changing: product assortment, article presentation in the store and consumer prices

Reduce costs by changing: process (logistics, store handling) and product characteristics (package size, item size)

Results of a DPP analysis of 5 different products.

Item A is a slow-moving product with high gross margin. Item E is a fast-moving product with low gross margin. Items B, C and D are medium-moving products with average gross margins and different prices.

Using a traditional profitability ratio (gross margin per week) and even gross margin per week per m3 shelf space, the fast moving product E appears to be the most profitable.

Item C is most profitable. Using the DPP model and after assigning direct product costs to these products, item E shows to be a mega looser, the high gross margin product A also shows a net loss, and only the other 3 products appear to deliver a net profit.

Direct Product Profitability tableDPP analysis had a considerable impact on assortment tactics and pricing strategy.

 

The Seven Step DPP process

The DPP model is capable of calculating net profitability of individual items of fast moving consumer goods. Working with the DPP model is a seven-step process:

  1. DPP model fine tuning: the classical DPP model is adapted to specific product characteristics of your industry
  2. Input of process characteristics: process characteristics of the logistics chain are entered as activity drivers in the DPP model (examples: delivery frequency, productivity ratios)
  3. Input of general ledger resource costs: resource costs of the central depot, transportation and the store (examples: transportation cost per km, costs per working hour)
  4. Calculation of activity costs: activity costs are calculated in the DPP model
  5. Input of product characteristics: all characteristics of individual products are entered as cost drivers
  6. Calculation of direct product costs: activity costs are allocated to products
  7. Calculation and presentation of direct product profitability ratios

 

Suggested project approach

A. Agree on project plan during initial meeting:

  • Project scope: which assortment, which pilot stores
  • Project organisation, involvement of consultant
  • Project timing
  • Project budget

B. Execute the seven step DDP approach

C. Suggest improvement plans and calculate cost- and profitability effects in 5 areas:

  1. Process changes
  2. Product characteristics
  3. Assortment
  4. Pricing structure
  5. Article presentation in the store

D. Suggest measures to implement improvement plans

E. Final report

 

Example: resource costs versus activity costs

2% cost reduction. Resource costs are normally well documented and controlled. Because resource costs are continually reported and controlled, there is often no opportunity (<2%) for substantial cost reduction. See the example for transportation resource costs in the next diagram.

Transport resource costs

20% cost reduction. Activity costs are normally not reported by classical administration systems. To show these costs, a special activity cost driver model is employed. The advantage of analyzing activity costs is the possibility to substantially (>20%) reduce costs by improving the process.

In the transportation example the activity cost reduction possibilities are: improve load factor, reduce delivery frequency, reduce waiting times, etc.

Transport activity costs

 

Please contact RetailEconomics

 

drs Joost van der Laan
RetailEconomics.com
J.W. van der Laan Marketing & Logistics BV
Prins Clauslaan 3
3852 DA Ermelo, Netherlands
Tel.: 0031-6-53846927
Chamber of Commerce 320 56641, VAT number NL802737213B01

 

Category Management for Supermarkets

Category Management

Category Management is the management of product categories as strategic business units, were assortment, pricing, inventory levels, shelf-space allocation, promotions and buying are all managed as a whole.

This intensive training course is all about implementing Category Management in your organization.

Workshop program

1. Development of Category Management

  • The pioneers: Coca-Cola, Procter & Gamble and Wal*Mart
  • The impact of Efficient Consumer Response
  • The impact of retailer concentration and globalization
  • Future development trends

2. Category Management from Retailer Perspective

  • Store format optimization
  • Assortment, pricing, presentation, logistics
  • Sales, gross margins, costs
  • Out-of-stocks, availability
  • Consumer happiness

3. Category Management from Manufacturer Perspective

  • Trade marketing versus retail marketing
  • Aligning category management and sales targets
  • Activation of different distribution channels
  • Eight trade marketing strategies to cope with category management

4. Win-Win Strategies for trading partners

  • Retailers improve retail marketing
  • Manufacturers improve trade marketing and brand value
  • Trading partners improve cooperation
  • International opportunities for manufacturers
  • How to handle confidential information

5. Successful Category Management Process

  • Definition: consumer segmentation and category structure
  • Role: cross category analysis and profitability evaluation
  • Assessment: identifying sales and profit opportunities
  • Balanced scorecard: performance targets
  • Strategies: assigning strategies to each category segment
  • Tactics: planning activities for results
  • Plan implementation: effective action
  • Review: measuring against benchmarks

6. Manufacturer Response to Specific Category Strategies

  • Traffic building
  • Point-of-Sale output
  • Category profitability
  • Cash generation
  • Creating excitement and customer triggers
  • Image enhancement

7. Importance of Effective Use of Data

  • Consumer behaviour (GfK household panel data, specific consumer research)
  • Market share and causal data (Nielsen data etc.)
  • Geographics
  • Retailer sales, margins, cost indicators, store locations, space management, promotions, introductions
  • Manufacturer turnover, margin indicators, promotional budgets, promotion results, introduction budgets and results

8. Effective Analytical and Planning Models (“enablers”)

  • Space management
  • Direct product profitability
  • Account profitability
  • Activity Based Costing
  • Pricing strategies, brandpricing, price elasticity

9. Management of Specific products

  • Impulse presentation of profit makers
  • Pricing strategy and presentation of private labels
  • Availability and shrinkage of theft sensitive products
  • The critical success factor of Wines
  • The cat-food mystery
  • Timing of fresh foods
  • Lean logistics of mega-losers
  • Packaging of slow-movers

10. Cutting-Edge Strategy for Category Management Projects

  • An effective and detailed approach
  • The role and commitments of each participating company
  • Team members and their work
  • Project planning
  • Avoiding and solving pitfalls
  • Internal and external communication

11. Case Study 1

  • A case of category management failure
  • What went wrong and the solution out

12. Case Study 2

  • Analysis of a successful category management case
  • Critical success factors

13. Retailer Buying Models and Manufacturer Response

  • Pure buying and negotiation
  • Merchandising and below the line focus
  • Format management and manufacturer investment in trade marketing
  • Decision making unit (DMU) and input of production and logistics
  • Integrated category management and retailer-manufacturer teams

14. Manufacturer Organization of Trade Marketing and Category Management

  • Four basic organization models of trade marketing
  • Tasks of category management, account management and brand management

15. Conclusion: Bring Everything to Action

  • Category management as part of the commercial organization
  • Day to day working between retailers and suppliers
  • How to share costs and benefits

The Category Management workshop is multi-company, single company or for two retailer/supplier partners. A full Category Management Workshop takes two days. It is possible to make a selection (“menu”) and organize a workshop of one day.

 

Please contact RetailEconomics for your Category Management workshop

 

drs Joost van der Laan
RetailEconomics.com
J.W. van der Laan Marketing & Logistics BV
Prins Clauslaan 3
3852 DA Ermelo, Netherlands
Tel.: 0031-6-53846927
Chamber of Commerce 320 56641, VAT number NL802737213B01

 

Supermarkets high productivity

Retail Store Productivity

Since net margins of retailers are in the range of 2-5%, 10% reduction of labor costs will double net profit!

Productivity improvement by “Internal Benchmarking” has been developed on the shopfloor, to improve productivity with the consent of employees!

Labor cost management has three levels of productivity drivers:

  • Strategic level: the marketing mix of the store format drives the activities in the store
  • Tactical level: productivity planning and human resources drive the standard time per activity and labor costs per hour
  • Operational level: store management executes the plans in a dynamic environment of fluctuating sales, customer and employee demands, logistics problems and other deviations from plan.

Retailers must use the best productivity planning method. See: “Internal Benchmarking”.

 

Objectives of a successful productivity project are:

  • Measuring and analyzing labor productivity and labor cost ratios of a retail chain, in particular the relative performance of individual stores in a multi outlet retail chain
  • Optimal productivity standards for each store and department (sales per work hour)
  • Optimal labor cost standards (costs per work hour)
  • A functional labor cost planning system
  • Minimal 3% decrease of total labor costs

For a retailer with 500 million turnover per year and 12% labor costs, the potential savings are 1.8 million per year. A project costing 200.000 will have a pay-back period of 2 months.

 

A productivity project will focus on control of labor “cost drivers”:

  • The right activities for optimal customer satisfaction (no weakening of the store format)
  • High productivity standards per department and per activity
  • Tactical planning of working hours in line with turnover fluctuations
  • Prevention of “idle time” (service departments)
  • The right activities executed by the right employees
  • Control of costs per hour per function

The project will take into account all external factors that have an effect on labor costs: service or self-service, turnover shares of product groups and departments, average price per article etc.

 

Suggested project approach:

  1. Discussion of suggested project plan with company leadership
  2. Informing the organization
  3. Detailing the plan with operational managers
  4. Defining departments and activities
  5. Measuring and analyzing present situation
  6. Setting new standards for productivity and costs per work hour, using our efficient “Internal Benchmarking” method
  7. Assessment of deviations of standard per department per store
  8. Implementation of productivity improvements and cost reduction in the stores
  9. Implementation of planning system
  10. Organizing continuous improvement

Each retail company should do this work every three years, to keep the organization lean and sharp. We are glad to help you with this profitable action.

 

Please contact RetailEconomics for your Productivity Improvement.

 

drs Joost van der Laan
RetailEconomics.com
J.W. van der Laan Marketing & Logistics BV
Prins Clauslaan 3
3852 DA Ermelo, Netherlands
Tel.: 0031-6-53846927
Chamber of Commerce 320 56641, VAT number NL802737213B01

 

Automatic replenishment

Automatic Replenishment

Automatic Store Replenishment is a powerful strategic weapon for retailers. Seven benefits of Automatic Store Replenishment (or Computer Assisted Ordering) and cycle-counting.

New features are: Nice Shelf Stock to improve commercially attractive shelves, Counting by Exception to improve data integrity and store productivity and Store Order Leveling™ to improve logistics productivity.

Automatic Store Replenishment is: “the preparation of orders by a computer integrating information about product movement (as recorded by point of sale equipment), outside factors that affect demand (such as seasonal changes), actual inventory levels, product receipts, and acceptable safety stock levels. Inventory data integrity is maintained by cycle-counting. (Source : ECR US – Automatic Store Replenishment, Computer Assisted Ordering, Sales Based Ordering, Automatic Ordering)

The seven benefits of Automatic Store Replenishment and cycle-counting are:

  1. Lower out-of-stocks and higher sales
  2. Lower inventory costs and higher margins
  3. Lower labor costs by cycle-counting
  4. Shrinkage reduction
  5. Store Order Leveling™ and truck-load optimization
  6. Reduction of price mark-downs
  7. Reduction impulse-buying of loss-leaders

How to Realize Maximum Profits from Automatic Store Replenishment

This article shows how to benefit from each aspect of Automatic Store Replenishment. How to build high data integrity. What are pitfalls to avoid. How to manipulate ordering parameters. What inventory choices to make: do you prefer minimum inventories or full shelves. And what are benefits of warehouse production leveling and truck-load optimization.

 

Benefit 1. Lower Out-of-Stocks and Higher Sales

A rule-of-thumb is that 1 % out-of-stock results in 0.5 % lower turnover. When products are out-of-stock, retailers suffer from customers buying elsewhere or buying not at all. Manufacturers suffer from brand substitution and also from non-sales. Strong brands show more store-substitution and weak brands show more brand substitution. Effects are different for stores with large assortments than for stores with small assortments. And sales effects of out-of-stocks are limited for both retailers and manufacturers by package-size substitution and delay of purchase.

Ordering mistakes by store employees are a main cause of out-of-stocks. Other main causes are delivery delays of manufacturers, out-of-stocks and order-picking mistakes of the central warehouse and the quality of product introduction and deletion. But in-store manual ordering mistakes amount to at least 50 % of out-of-stocks. No manual ordering mistakes is one of many benefits of Automatic Store Replenishment.

Why do store-orders improve by Automatic Store Replenishment? Inventory levels at time of delivery are estimated by a forecasting system. The system takes into account any shipments underway and turnover during lead-time from order to delivery. This forecasting is almost impossible by classical manual ordering. But there are some conditions for Automatic Store Replenishment to work correctly.

Conditions for correct store-orders are:

  • Low non-scanning rates to generate correct sales data
  • Discrete (separate) scanning of varieties of products (colors, flavors)
  • Regular match of physical and database inventory levels through cycle-counting
  • Correct input of minimum and maximum shelf-stock levels

If these conditions are met, lower out-of-stocks will have a positive impact on net profitability. See the next calculation.

 

Realistic net profit estimate for out-of-stock reduction by Automatic Store Replenishment, based on following assumptions:

  • Out-of-stock level by store ordering mistakes = 2 %
  • Real out-of-stock reduction by ASR = 1.5 % (75 % of out-of-stocks caused by in-store ordering)
  • 1 % out-of-stock reduction results in 0.5 % sales increase
  • ASR is used for 60 % of sales
  • Average direct product profitability of ASR assortment = 8 %

Potential net profit of out-of-stock reduction by Automatic Store Replenishment = 180.000 for every 500 million of total sales.

 

In my practice some retailers already have low out-of-stocks by solving every order-picking / ordering mistake by extra deliveries, shipments and returns. This solution proves extremely expensive. In these cases net profits from Automatic Store Replenishment are up to 350.000 for every 500 million of sales.

 

Benefit 2. Lower Inventory Costs and Higher Margins

Automatic Store Replenishment makes it possible to choose between many different ordering techniques. Most important are “fill-to-minimum” and “fill-to-maximum”. Fill-to-minimum is ordering just enough to stay above minimum stock levels. Fill-to-maximum is ordering for full shelves. Advantage of fill-to-minimum is low average stocks, but disadvantage is a good chance of unattractive looking shelves. Advantage of fill-to-maximum is attractive full shelves, but disadvantages are higher average stocks and increased possibilities of product shrinkage.

Highest profit from Automatic Store Replenishment – and every solution to any problem! – is obtained by maximizing advantages and minimizing disadvantages. For store ordering this means: choosing a middle road between fill-to-minimum” and “fill-to-maximum”. You can work with a percentage of each ordering technique, or you can define so-called “nice stocks”.

“Nice Shelf Stocks” are minimum shelf stocks to give shelves an attractive look. “Nice Shelf Stock”-levels are often larger than safety-stock levels, which are maintained to avoid out-of-stocks when turnover is extra high. A simple way to define “nice stocks” is: one or two items per facing.

Other inventory benefits of Automatic Store Replenishment are “sales anticipation” and “on-order information”.

“Sales anticipation” means that the ordering system makes a forecast of turnover during lead-time between order and delivery. This forecasted turnover is added to the order, so shelf-stock is just right when delivery is put on the shelf.

“On-order information” takes into account that during ordering a former delivery has not arrived yet. In classical manual ordering this problem is often tackled by lowering order frequencies. Automatic Store Replenishment has no problem with deliveries on route. These are just added to in-store stocks when calculating an order.

 

Realistic net profit estimate for stock reduction by Automatic Store Replenishment, based on assumptions:

  • Ordering from fill-to-maximum to ASR (fill-to-minimum and nice stock)
  • Average shelf-stock level fill-to-maximum = 2 weeks
  • Average shelf-stock level ASR = 1.4 weeks
  • ASR is used for 60 % of sales
  • Average net buying costs ASR assortment = 62 %
  • Capital costs = 6 %

Potential net profit of stock reduction by Automatic Store Replenishment = 130.000 for every 500 million of total sales. This is excluding opportunities for gross-margin increase.

 

Space for new assortment is created by Automatic Store Replenishment, because fast-moving items often need less facings when stock levels go down. New assortment creates more choice for customers and higher sales. Facings of slow-moving items are not effected by lower stock levels, because facings of slow-movers totally depend on case-size. See article on optimal case-size.

Automatic Store Replenishment makes it possible to substitute low-margin sales by high-margin sales. Fast-moving items – especially A-brands – often have very low gross-margins and negative net product profitability. Lower stock levels reduce impulse buying of these loss-leaders. New assortment can be chosen from high margin niche groups. Or you give high margin private brands extra facings to induce impulse-buying. Resulting average gross-margins will rise. If free space is used optimally, resulting net profitability will be higher than net profit from stock reduction.

 

Benefit 3. Lower Labor Costs by Cycle-Counting

Automatic Store Replenishment reduces labor costs, because manual ordering is substituted by automatic ordering. It is true that new tasks are introduced to maintain correct stock data, but these tasks require fewer and cheaper labor.

Cycle-counting is the technique of choice to maintain correct stock data. Instead of checking shelf stocks at each order cycle, stock levels are counted every 4 to 8 weeks and compared to database information. Labor cost savings are substantial:

  • Cycle-counting frequencies for ASR are much lower than manual ordering frequencies
  • Cycle-counting is a simpler task than manual ordering and therefore cheaper
  • Timing of cycle-counting is not time-critical and can be done at quiet moments
  • ASR techniques as “Counting by Exception” improve efficiency and data-integrity

Counting by Exception is an important feature of Automatic Store Replenishment, which makes cycle-counting even more efficient. Exceptional stock levels trigger counting: zero physical stocks, zero database stocks, zero turnover and vulnerable products. Effect: obvious problems are tackled with a minimum of work.

Zero physical stocks are checked to detect order picking mistakes and theft. In takes a few minutes to walk the isles after every shelf filling cycle, and to check empty – or almost empty – shelf spaces and corresponding database stock-levels. Zero physical checks are important: if database stocks are above minimum stock levels and physical stock levels are zero or very low, the ordering system will not generate necessary replenishment orders. Sales will drop to zero.

Checking zero (or negative) database stocks is method of choice to manage shelf space for unexpectedly successful products, deleted products, new product introductions and products temporarily out-of-stock. A list of zero stocks is compared with physical stocks every day. This also takes little time. Deviations require some analysis and adjusting of shelf space.

Zero turnover checks make it possible to detect problems with product availability and visibility of fast-moving products. If a product is normally purchased 20 times a day, it only takes a few hours to produce an automatic trigger for store management to check if anything is wrong with products on shelf. Zero turnover detection requires sophisticated statistical software, but is a powerful tool to improve attractiveness of the store.

Vulnerable products are articles which have a high probability of shrinkage and other causes of stock level deviations. Examples are perishable goods, or small expensive items like cosmetics, cigarettes, batteries and razor blades. These products (or product groups) are pre-defined or are detected by frequent stock level mutations. They are cycle-counted more frequently than normal. The effect is that counting frequencies for “normal” products are limited to a minimum level, and average counting frequency remains low.

 

Realistic net profit estimate of labor cost reduction by Automatic Store Replenishment, based on assumptions about manual ordering and cycle-counting:

  • Traditional ordering requires 5.0 % of total man-hours of a store
  • Traditional ordering costs € 12 per man-hour
  • cycle-counting and order management of ASR requires 2.8 % of total man-hours of a store, and costs € 9 per man-hour
  • Total man-hours of a store = 1 hour per € 135 of sales
  • ASR is used for 60 % of sales

Potential net profit of labor cost reduction by ASR = € 775.000 for every € 500 million of total sales.

 

Benefit 4: Shrinkage Reduction

High frequency cycle-counting of vulnerable products will detect shrinkage problems in an early stage. When store management addresses these problems, cycle-counting makes it possible to measure effects and evaluate solutions.

Shrinkage of vulnerable products can be resolved in many ways, for example:

  • Increasing visibility of small and expensive items by placing them next to point-of-sale, but maintaining self-service presentation
  • Reducing shelf stock to a minimum of a few hours of sales
  • Reducing “sweeping” of multiple products by placing high priced items in racks
  • RFID tagging
  • Placing products behind a service counter to reduce theft by customers and employees.

Each solution has advantages and disadvantages. For example: placing products behind a service counter will result in lower sales and higher costs. This is a high price to pay for shrinkage reduction. It is better to develop solutions that increase sales, at the cost of some closely monitored shrinkage.

Placing small expensive items next to point-of-sale has two distinctive advantages. We measured the effects in two interesting shrinkage studies. First benefit: increased visibility for POS workers and lower shrinkage. Second benefit: increased visibility for customers and higher sales. Increased visibility is very interesting for high margin batteries and razor blades. These products generate high gross margins (up to 60 % of an average price of € 4,50), and they have a high visibility elasticity or high impulse factor. Customers buy these products when they see them. Batteries and razor blades are the best products to place on a counter or next to point-of-sale. Shelf profitability of this high-opportunity store real estate is much higher then for the usual low-price/ low-margin candy bars.

We were able to obtain these results only through the availability of item-level inventory and cycle-counting.

 

Benefit 5. Store Order Leveling and Truck-Load Optimization

Turnover is not equally distributed over days of the week. More customers shop on Fridays and Saturdays than on other days of the week. These two days together may even constitute half of total weekly sales, as in the next diagram.

ASR turnover per day

When stores order every day, they will generate large orders around the weekend and small orders in the middle of the week. This disrupts the entire supply chain:

  • Unbalanced workload at distribution centers and suppliers
  • Unbalanced truckloads and average lower utilization of trucks and truck space
  • Unbalanced workload at store

This problem is not new and retail organizations have developed different solutions:

  • Ordering and distributing slow-moving products only on quite days
  • Pre-producing fast-moving items on quite days
  • Maximizing daily orders

But every solution creates a new problem. Ordering slow-movers on quite days makes the ordering and distribution system more complex. Pre-producing fast-movers on quite days creates extra inventories in the pipe-line. Maximizing daily orders makes ordering more complex and is cause for increasing out-of-stocks on busy days.

The best solution is Store Order Leveling™ by simply manipulating input parameters of the automatic replenishment system. The system uses free shelf space of set-to-minimum order algorithms, or redundant stock levels of set-to-maximum algorithms. Orders can be leveled to an ideal distribution over days of the week. The next diagram shows flat orders fro Monday to Friday and reduced orders on Saturday.

Store Order Leveling

We have made many simulations for set-to-minimum and set-to-maximum order algorithms, and these simulations are tested in real-life situations. It is now possible to generate almost any order leveling distribution for any actual turnover distribution. It is also possible to shift automatic orders further to compensate for manual – non-leveled – orders.

Estimated financial results of Store Order Leveling™ are very interesting:

 

Realistic net profit estimate of labor cost reduction and truckload optimization by Store Order Leveling™, based on assumptions about average turnover distribution over days of the week:

  • Saving of 2 full-time equivalents in distribution center = € 50.000
  • Saving of 2 trucks = € 250.000
  • Saving of 8 full-time equivalents in stores = € 160.000

Potential net profit of labor cost reduction and truckload optimization by Store Order Leveling™ = € 460.000 for every € 500 million of total sales.

 

Benefit 6. Reduction of Price Mark-Downs

Fashion retailers plan their budgets every season for item procurement and distribution to the stores. Logistics is mainly push oriented. Traditionally the leftovers are discounted at the end of the season with high mark-downs. These mark-downs are high because the season is over and leftovers are the least desirable items for regular customers.

A system with item-level turnover planning and inventory control makes it possible to monitor sales development from day one. After a few weeks it is clear which items are slower then expected and will not entirely be sold during the season. If price mark-down is started during the season, overall mark-downs will be lower. This is called “quick response”. The earlier a store starts its mark-down program, the lower the overall price reduction will be. See the next table.

ASR suggested markdowns

Profit effects depend on seasonality and fashion characteristics of the assortment. If 20 % of items is normally marked-down on average by 50% at the end of the season, and “quick response” makes it possible to lower average mark-down to 40%, the net profit effect is a staggering 10 million on every 500 million of turnover. There will be some substitution effects in the season, but early mark-down will also improve the price image and customer attractiveness of the store.

One additional aspect of “quick response” is the reverse of early mark-downs: additional production and distribution of fast-moving items. If sales of certain items are higher than expected, producers should be flexible and quick enough to produce and distribute these items to the stores in a very short time.

 

Benefit 7. Reduction Impulse-Buying of Loss-Leaders

Often there is no match between logistics requirements and commercial requirements. For example: logistics managers prefer low distribution costs and low delivery frequencies, but commercial managers prefer low store inventories, low out-of-stocks and high delivery frequencies. An easy solution is a compromise with a heavy weight on store requirements.

A better solution is to choose for high delivery frequencies to the stores, in combination with concentrated deliveries by one channel. In other words: minimal “direct store deliveries” and maximum deliveries via the central warehouse. This creates a win-win situation for the stores and for logistics.

But high delivery frequencies are not sufficient to prevent a costly problem in the stores. There are always 100 – 150 super fast moving items – with low gross-margins – that still require much shelf space in order to meet customer demand. Large shelf space means high visibility, and high visibility means high impulse sales. And here is the problem: super fast moving items are often loss-leaders: net product profitability is below zero. You do not want to sell these products, but you have to. Extra impulse buying works very negative on the stores profitability and must be avoided. But these items are traffic builders and they must never be out-of-stock, otherwise the store will lose customers to the competition.

Item-level inventory control – as part of Automatic Store Replenishment” – makes it possible to maintain low inventory levels and low visibility on the shelf, in combination with an extra backroom stock. The computer will take the two stock locations per item into account, for cycle-counting and ordering purposes. Unwanted impulse buying of loss-leaders is prevented and there is even shelf space available for additional high-margin assortment.

Net profitability effects are not easy to calculate, because ”space-elasticity” is different for every item. We also have to make estimates about substitution from low margin to high margin products. But this improvement concept is intuitively promising and can be tested in practice.

 

Conclusions and Recommendations

Potential results of Automatic Store Replenishment are high: 1.5 million hard benefits and another 1 million soft benefits for every 500.000 million of turnover. For fashion retailers potential benefits are much higher, because these retailers have the potential to cut back in expensive price mark-downs.

These benefits have a long life span. If you are making an investment decision for SCM software licenses and implementation, use at least a 5 years time span. At 10% DCF, the NPV of hard benefits is 5.5 million. This is about 10 times the cost of a SCM software licence and these benefits may cover the investment of an entire ERP system.

There are three important critical success factors for Automatic Store Replenishment:

  • At start: data integrity
  • During project: develop correct forecasting and order algorithms and avoid manual order corrections
  • After project: use Automatic Store Replenishment information for maximum benefits

These critical success factors must be managed to realize their specified results. This requires assistance from experienced implementers and users of Automatic Store Replenishment.

 

Strategy and Tactics for Optimizing Results:

RetailEconomics offers consultancy services to realize full potential benefits of Automatic Store Replenishment, by focusing on critical success factors.

Start an Automatic Store Replenishment project

  • Execute a quick scan and write a business case for Automatic Store Replenishment
  • Select and implement your Automatic Store Replenishment system
  • Develop right conditions for correct inventories and automatic orders (data integrity)

Execute an Automatic Store Replenishment project

  • Develop simple and effective order algorithms
  • Develop correct ordering parameters for every category
  • Develop effective system of Counting by Exception
  • Provide training
  • Monitor order corrections and develop structural solutions

Use Automatic Store Replenishment information for maximum benefits

  • Analyze shrinkage reduction opportunities
  • Propose Store Order Leveling™ parameters
  • Propose alternative assortments for higher net profitability
  • Propose alternative shelf presentation for higher net profitability
  • Develop procedures to “lock” quality and for continuous improvement

We are happy to deliver a detailed plan, based on your specific questions.

 

Please contact RetailEconomics for Automatic Store Replenishment

 

drs Joost van der Laan
RetailEconomics.com
J.W. van der Laan Marketing & Logistics BV
Prins Clauslaan 3
3852 DA Ermelo, Netherlands
Tel.: 0031-6-53846927
Chamber of Commerce 320 56641, VAT number NL802737213B01