Medical Distributors, Inc. (MD) recently measured the “total” costs for both back-orders (B/Os) and credits. The results were astonishing: on $20MM in annual warehouse sales volume, the cost of all B/Os (and the related, one-line, special orders from suppliers) was estimated to be approximately $1MM or about 5% of sales. (The cost for credits was another $400K or 2% of sales; transforming that cost into profits will be another “quantum profit case study”¹.)

Is your company (unknowingly) burning a magnitude of 5% of sales on B/O costs? If so, is it tempting to think about all of the tactics – in priority order– that you could pursue to transform at least half of the cost (loss) into profits? For (15) how-to opportunity ideas, follow MD’s (abridged) train of thought and action plays below, and then discuss the applicability of those ideas to your business. (See Exhibit 1 for a summary of the opportunities)

“5% OF SALES? NO WAY!”

MD has been a subscriber of Waypoint (Analytics’) Quantum Profit Management Service (QPMS) since the beginning of 2009.² MD initially pursued “high-leverage account, selling plays” supported by QPS’s “5 x 5 Sales Rep Dashboard” reports.³ When management recently began to focus on B/O costs, everyone initially thought the 5% figure was too high. After some dynamic, what-if, cost-allocation re-modeling efforts with Waypoint consultants, the general conclusions were:

1. Modifying cost-modeling scenarios made little difference to the total cost figure which could be made to vary from 4-6% of sales.
2. Creatively reducing B/O costs – whatever they were truly were – by at least 50% was a big profit improvement goal.
3. Distribution businesses have a lot of hidden (“overhead”) costs that everyone (reps especially) should be aware of in order to be part of all “cost-to-serve” solutions.
4. These hidden costs are not “fixed”, but rather quite variable and can be flexibly reshaped in line with new, customer-niche and supplier-line, profit improvement plays.
5. Each new, insight from the QPMS seemed to raise a new round of questions/opportunities for which the service could then be used again to find the next-level, deeper and eventually root-cause answers. It was exciting to be pioneering in such a lucrative new frontier!

BREAK A BIG PROBLEM INTO SMALL ONES TO SOLVE SEPARATELY

Because QPMS can calculate costs and profits at the line item level and then sum those increments to category levels, MD looked at ranking reports for back-ordered lines divided by total lines ordered from a number of viewpoints including by:

1. Every customer
2. Each pool of customers within different industry-segment/size bands (A-D “strata”)
3. Individual Stock Keeping Units (SKUs)
4. Grand totals for all SKUs for each supplier on a report that also included columns for “total sales” and “total net profits” for the supplier.
5. Special orders that were also small orders (under $500) by supplier
6. Sales Rep Territory
7. Most popular SKUs (ranking report) for a customer niche (pool of customers) with columns for:
8. percent of customers that bought the item (the ranked factor);
9. total picks;
10. percent backorder rate;
11. average investment in each item; and the…
12. cumulative investment in all of the items.

(All of these reports – and the thinking that went into their design and use – are now instant, documented, upgrades available to all QPMS users.)

INSIGHTS FROM REPORTS “A-G” ABOVE?

To derive meaningful, action plays from the reports above, MD blended a combination of: their own customer and supplier insights with some distribution-channel-ideas from QPMS consultants in short, as-needed, “go-to-meeting sessions” in which all shared the same on-screen MD data/reports. Although each distribution firm and branch within a distribution chain will discover different, unique-to-local-context opportunities, here are some of MD’s conclusions generically described.

From report “a”: Customers with the lowest B/O rates as a percent of total line items bought (at the bottom of report “a”), tended to be accounts that:

• Bought a narrower range of most popular, commodity items on a very steady, repeat basis,
• They, in turn, were doing a focused, high-volume, standard output for their customers (and often had a better understanding of process efficiency including the cost of a purchase order, so they had larger, average order sizes).
• Had lower average margin percent rates, but were still quite profitable.
• Why? How? Although buying in bigger volumes of more commodity items would naturally lower the margin rate, the total cost-to-serve per order for these accounts was much less than the total gross margin dollars in the order. These accounts averaged 2-4 times the return on sales as the company average.

 Opp. #1: What should MD do to: protect, grow and find more accounts like these ones?

High-back-order customers – at the top of the report – were often custom-work, job-shops that provided (semi) unique solutions for their customers. These unique outputs often required small quantities of odd items from MD creating more, small, B/Os. These B/Os, in turn, often required a dedicated purchase order to special order the item from a supplier on an expedited basis. The total-channel, people and paperwork costs for these small, special orders were many times the margin dollars (even at a high margin percent markup) in the item/order.

 Opp#2: What supply chain, business-model innovations might MD explore with suppliers that have the highest incidence of small, rush special orders?

(MD did have about 2% of all of their suppliers – 10 out of 200 – that were big profit losers, some of which had many small, big-money-losing, special orders.)

The job-shop customers – a now identifiable niche – were typically small and slow-growing at best, because custom-work does not typically scale easily. Their margin percentage rates were high, but they were typically still net profit losers, because their total margin dollars were not enough to offset the high transactional costs of their small orders, back-orders and special orders.

To become profitable:

Opp. #3, this niche of customers will have to get- from MD- a new set of pricing, terms and unbundled services (like doing special orders for a fee),

This action may cause some customers to choose to switch to a supplier that will unknowingly subsidize their service needs. (Note: MD is not “firing” any customers, nor are they “refusing to do business” with any customers. MD just wants each customer to be profitable on a standalone basis and not be subsidized by all other company stakeholders.)

If some customers do leave, then for every dollar of margin lost, MD will free up more than $2 of cost-to-serve slack. MD will then have to decide what to do with the measurable slack (thanks to a QPS activity-tracking report). Should they:

1. Redirect the slack to providing better service to super-profitable (target) accounts?
2. Lay off more slack costs than lost margin dollars to boost profits? Or…
3. Some combination of both?

 From report “b”(customer niche) and “g” most popular SKUs per niche:

MD had already done extensive, customer-profitability-ranking report analysis which included looking at the aggregated profits from all of the customers within different “niche pools” that were defined by two dimensions: “customer/industry segment” and 4 levels of volume (“A-D” with A accounts being big enough – more than $400 in margin per month – to support outside sales force coverage.⁴) Out of 12 different customer-niche pools, two A-strata niches generated 130% of the internal profits to offset the 30% combined losses from the other ten. (MD’s traditional, full-service, wholesale model was generally too expensive for the small, average, order-size flow from the B-D strata customers. This was a business model innovation opportunity.⁵)

The B/O percentage for the #1 niche was, however, below the overall average. Why? Theory: the cumulative demand from this large, homogeneous pool of customers’ had helped to unknowingly shape and tune the fill-rates for the most popular, common items to a high, consistent level.

How to test this theory? Report “g” summed up all item activity (re-ranked by the column which reported the percent of customers in the pool that bought each item). By going down the column for percentage of line-picks back ordered, MD could find the higher-back-ordered, lower-fill-rate items. Most of these items came from a few supplier lines for which MD buyers made infrequent (6 times or less) line-buys on an annual basis.

Opp.#4: By looking at the cumulative investment in the most popular items, the company hypothesized that by selectively beefing up the most frequently back-ordered items, there would be a significant positive trade-off amongst these factors:

1. Mild negative: the investment in tweaking some of the top 5% of all active items was instantly knowable. The additional carrying cost of the incremental investment was low, because the item investments were so precisely targeted, and they were proven winning, turning items.
2. Big positive: the back-order costs would drop (to be tracked and measured)
3. Biggest (hidden) positive: high flow-through of the incremental-margin dollars in better filled orders to the bottom line. When more volume is added to an already profitable order (and most #1 niche customers already averaged, profitable-sized orders), then 50% of the incremental margin dollars in that line will flow-through to the bottom line. Why? Because all of the costs assigned to the transaction have already been incurred and covered by the current-order, margin dollars, the only extra costs for bigger or more line items were for: carrying costs, perhaps a line/pick and variable, sales compensation. The most profitable marketing programs possible for distributors are: selling more of already-stocked-items to the same, existing/active customers on a larger-order-sized basis.⁶
4. Good positive: customer satisfaction/loyalty with better service should support better retention rates and more last-look + pricing premiums. (No immediate way of tracking these in contrast to what is happening with competitors.)

As a by-product of this analysis, MDS was able to define – to a sufficient degree of accuracy – “the critical mass of inventory and sales volume for a customer niche pool.

• Critical mass inventory is: the SKU breadth and depth of aggregate inventory investment” needed to serve each target pool with the highest effective fill-rate in the local, competitive marketplace.
• Critical mass sales is the annual level of sales needed to get the inventory turning at a high enough rate to make a good, turn-earn ROI.
• Critical-mass inventory investment and sales volume for a “niche pool” are both an invisible service edge for a distributor and barriers to entry for competitors. 

 Opp.#5: MD subsequently created new goals for capturing between 50 to 80% of the net profits from each niche pool that they pursued.

Opp.#6: They are also seriously considering exiting some niche pools in which they are too far below the critical mass numbers that entrenched competitor(s) already have.    

 From supplier-centric reports, “c-e”:

Looking at all three of these reports together, there were some supplier lines that were serious losers for several different reasons:

• Too many redundant lines in a product category spread the total customer demand over too many duplicate, substitutable items. Spreading out demand had demoted many items – in the smaller volume lines – to “C” (slow-turn) status. The automated forecasting formulas had, in turn, programmed in lower target fill-rates which would create more back-orders.

 Opp.#7: In one case, MD identified three duplicate lines that could be consolidated into the one best line for the long haul, the benefits would be:  

1. The remaining line/items would receive higher fill-rate targeting.
2. Opp.#8: Buyers could make more frequent line buys from the one, best supplier which would allow them to further fine-tune the line to minimize both excess stock and stock-out/back-orders.
3. Reps would more enthusiastically sell what was stocked, because it would have such high fill-rates and lower back-order frustration from customers.
4. And, QPS could quickly create reports for each sales territory of the customers that had been buying items from redundant line #2 and #3 that were targets for switching over buying to the #1 line.
5. Opp.#9: The switch-over reports dramatically reduced the anxieties that everyone (may have) had about how much business would be lost by asking customers to switch lines.

Conclusions:

• Get serious about getting enough new sales for a redundant line to turn it well and re-order it often or don’t add it and sell what you have.
• And, weed out existing, lagging lines – on a regular basis – to consolidate better buying economics, frequency of purchase and fill-rate levels on the best line(s). Don’t accumulate redundant lines.
• Hard, measurable data is necessary to do new plays; otherwise, enough people will imagine that (unmeasured) negative side effects will be much greater than they really are to stall change.

Key Related Concept: fill-rates increase and back-orders drop exponentially with the more frequently we can re-order a line. Wal-Mart achieves, for example, 99% fill-rates for its top 6% SKUs which generate almost 70% of its sales, because those items are replenished daily on an automated basis from the distribution centers.

Opp.#10: Most distributors could achieve breakthrough turn-earn, highest, total fill-rate economics if they could figure out how to partner with some type of master-distribution entity (or factory coop like CoLinx.com) for not just their slow items, but also their biggest volume ones!

A separate category of super-losing supplier lines involved mature, equipment lines for which MD had a big installed base and customers were now buying primarily replacement parts and pieces. The history for these lines was: “in the beginning”…there were three segments to the equipment line that had built in cross-subsidies:

• Initial equipment sales (often on a drop-ship basis) which are typically quite profitable, especially if: the distributor has an exclusive territory; and the product is in the growth stage of its life cycle with a good brand name.
• Common maintenance parts which turn well and make good profits.
• “C and D” parts and pieces which are ordered infrequently and often on a breakdown, need-it-now, rush basis. These items barely turn or are not stocked and must be special-ordered at a transactional-cost loss basis for everyone in the channel (including down time costs for both the end-user and repair mechanic).
• How then might this three-part story become unprofitable as the life-cycle goes into its commodity stage?
• The equipment becomes a profitless commodity that too many competitors give away in order to win the volume maintenance parts business.
• The “A”-volume parts get cloned in Asia; margins erode.
• And, a big installed base of equipment for users are ordering an increasing number of slow-moving parts and pieces.
• Conclusion: the “parts and pieces” small, rush, special-order, back-order problemis always a loser, cross-subsidized by equipment and maintenance sales. When the profitable segments of the line erode in the commodity stage of the life cycle, then a new, supply chain solution is required. 

 Opp.#11: There are channel precedents for manufacturers – that share common distributors – to stock 100% of the inventory in a central co-op location and sell end-users direct while re-intermediating their channel partners. There are software-as-a-service and robot-picking technology solutions that can be applied to this type of opportunity which will collapse total channel costs while getting parts-and-pieces to end-users faster and more reliably which will increase the value (differentiation) of the now-commodity equipment brand.

 From Report “f” on sales territories:

Sales-territory, B/O percentages simply reflect the mix of customers that a rep might have. The rep is typically not the original cause of or the immediate, main answer to a high B/O percentage problem within a territory. Often a company has historically hired a rep to be a product and/or customer category specialist, but the end-result for the new niche fell economically short. How?:

• Service Model Mis-Match: the (full-service) business model offered by the distributor has costs per transaction that exceeded the average margin dollars per transaction from the target customer.
• The target customer niche pool required a broader and/or deeper critical-mass of one-stop-shop items and services for which the distributor fell short on in contrast to the entrenched, more focused-on-that-niche competitor(s) that already had achieved critical mass economics and barriers to entry.

If the problem is one of falling short of critical mass, then how weak are we in light of the entrenched competitors?

Opp. #12: In customer niches where we are short of critical mass economics, should we redouble our efforts to dislodge the dominant distributor, or exit to free resources to bet on other niches were we can become a dominant #1 or a strong #2 to a sleepy #1?

OPERATIONAL TACTICS TO IMPROVE FILL-RATES, REDUCE BACK ORDERS

Finally, MD dusted off their DVDs from “High Performance Distribution Ideas for All” and reviewed the operational tactics that will reduce back orders considerably without any more investment and without any supply-chain innovations.⁷ These specific measures are:

• Opp.#13: Receive all in-bound warehouse goods before the start of the next day. Overtime costs to receive big slugs of A-item goods today is more than offset by reduced back-order costs tomorrow which would occur if the goods had not been properly put away and registered into the available inventory levels seen at the order entry desk. If goods that have arrived are shipped anyway – before being properly put away and entered into the computer – then cycle count problems/costs will escalate.
• Opp.#14: Achieve housekeeping systems that allow for 95% cycle count accuracy on some number of A+ items that are counted everyday. This will minimize back-orders that result from the computer saying the inventory is available, but the warehouse not being able to find that inventory, because it is in the wrong place. It also will reduce mis-picked items which create credits to correct shipping the wrong item that was in the wrong picking location.
• Opp.# 15: Teach inside sales people both the art and science of not back-ordering a small order,if and when possible, by:
• Shipping partially filled lines as “complete”, if it is for customer replenishment stock;
• Suggesting substitution items IF the demand for the substituted item is then assigned to the original item the customer wanted. Otherwise, the computer will buy increasing amounts of the substituted item and decreasing amounts of the initially desired item.

CONCLUSIONS

1. Back orders are more expensive – for every member of a channel – than most realize.
2. Just because back orders have always been part of the channel scene, doesn’t mean that we can’t use quantum profit mechanics and management (QPM) to dramatically reduce back-orders, improve service quality and satisfaction and improve profits.
3. Why play by the old rules and take the “good with the bad” if you can shrink the bad and maximize the good to significant degrees?
4. Software-as-a-service technology and economics have changed so much in the past two years that dramatic new profit improvement plays and tools are now available for both distributors and manufacturing distribution divisions for a low, monthly subscription fee. Request either a Waypoint demo or watch a taped, webinar at www.waypointanalytics.info.
5. Stay tuned for more case studies on MD’s progress as well as other solutions from other Waypoint QPS clients.

EXHIBIT ONE

SUMMARY OF BACK-ORDER OPPORTUNITY PLAYS

(For each play rate: how important the play could be for your business: A,B,C; how easy it would be for teammates to understand and execute: A, B, C; and how good your analytic information capability is to support the opportunity: A,B,C.)

1. What should MD do to: protect, grow and find more accounts like the high-volume, few-commodity-item, large-average-order-sized customers?
2. What supply chain, business-model innovations might MD explore with suppliers that have the highest incidence of small, rush special orders?
3. To become profitable, this niche of customers (small, custom-job shops) will have to get- from MD a new set of pricing, terms and unbundled services (like doing special orders for a fee), which may cause some customers to choose to switch to a supplier that will unknowingly subsidize their service needs.
4. By looking at the cumulative investment in the most popular items, the company hypothesized that by selectively beefing up the most frequently back-ordered items, there would be a significant positive trade-off amongst these factors:
5. MD subsequently created new goals for capturing between 50 to 80% of the net profits from each niche pool that they pursued.
6. They are also seriously considering exiting some niche pools in which they are too far below the critical mass numbers that entrenched competitor(s) already have.
7. In one case, MD identified three duplicate lines that could be consolidated into the one best line for the long haul.
8. Buyers could make more frequent line buys from the one, best supplier which would allow them to further fine-tune the line to minimize both excess stock and stock-out/back-orders.
9. The switch-over reports dramatically reduced the anxieties that everyone (may have) had about how much business would be lost by asking customers to switch lines.
10. Most distributors could achieve breakthrough turn-earn, highest, total fill-rate economics if they could figure out how to partner with some type of master-distribution entity (or factory co-op like CoLinx.com) for not just their slow items, but also their biggest volume ones!
11. There are channel precedents for manufacturers – that share common distributors – to stock 100% of the inventory in a central co-op location and sell end-users direct while re-intermediating their channel partners. There are software-as-a-service and robot-picking technology solutions that can be applied to this type of opportunity which will collapse total channel costs while getting parts-and-pieces to end-users faster and more reliably which will increase the value (differentiation) of the now-commodity equipment brand.
12. Should we redouble our efforts to dislodge the dominant distributor, or exit to free resources to bet on other niches were we can become a dominant #1 or a strong #2 to a sleepy #1?
13. Receive all in-bound warehouse goods before the start of the next day. Overtime costs to receive big slugs of A-item goods today is more than offset by reduced back-order costs tomorrow which would occur if the goods had not been properly put away and registered into the available inventory levels seen at the order entry desk.
14. Achieve housekeeping systems that allow for 95% cycle count accuracy on some number of A+ items that are counted everyday.
15. Teach inside sales people both the art and science of not back-ordering a small order, if and when possible.

©      Merrifield Consulting Group, Inc., Article 3.15

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