Modern TOC Distribution or Everything you wanted to know about TOC Distribution, but were afraid to ask

Transcription

1 Modern Distribution or Everything you wanted to know about Distribution, but were afraid to ask Presented By: Humberto R. Baptista Date: June/2014 1

2 Contents Distribution essentials The main variable of the Distribution solution Supplying Dynamic Buffer Management Seasonalities Mix and Category Management 2

3 Distribution essentials Distribution is a solution for distribution environments* that have the following Core Cloud Manage Well B Protect Sales D Increase Stocks Protect Cash Decrease Stocks * Environments where clients (demand points) tolerance time is smaller than the Supply Time (from supply points) 3

4 Distribution essentials Injections (from Insights): And if it is not a plant and/or there is no internal supply chain? Establish the plant (central) warehouse. All products? Everywhere? What numbers on the formula? At each place and for each product establish the inventory target according to the formula. What about batches? And how frequent periodically be? Move to "Order daily - Replenish periodically". What actions should be done? And What about peaks? Monitor the inventory targets according to the zones. How? What guides this? And where in the supply chain? Re-examine policies of make-to-stock, make-to-order. Necessary? Ever done? Educate sub-systems to monitor execution using Dollar-Days measurements. 4

5 Distribution: quick overview Until the end consumer has bought no one in the supply chain has sold Push Forecast -> Resupply Stocks Supply Chain goods flow Buffer Size = optimistic demand within the RLT factored by supply Murphy Pull Stocks Sale -> Resupply Aggregation Buffer Management Decide: Hold (MTA) or Order (MTO) 5

6 Distribution essentials What is really left Outside: Mix management (sort of) Lifecycle management Data integrity Pricing See my presentation on ICO 2010 (Retail : Myths and Truths) 6

7 The main variable Recognize the impact of the accumulation of stock in the different links of the supply chain Definition of distribution environment: Supply Supply Time Demand Leading to: Supply Stock Tolerance Time Supply Time Supply Time So we are talking about TIME! (every logistical solution is about time! Check it out!) Demand Tolerance Time 7

8 Main Variable We suffer with supplier s unreliability We suffer with unbalanced stocks & shifts in demand Suppliers aren t reliable Replenishment time is long Forecasts are Inaccurate Successful Distribution Protect Sales Protect Cash 8 Increase Stocks Decrease Stocks

9 Setup Buffers (what, where & how much) How Much Buffer sizing formula What is Optimistic demand? How can we gauge it from current data? Best solution (less distorted): Average speed of sales = sales/days with stock: Sales Traditional (more distorted): Average Sales = 3/8 = 0.375/day Stock Formula precision required? Days 9 Suggestion (less distorted): Average Speed of Sales = 2/4 = 0.5/day

10 Setup Buffers (what, where & how much) What/Where 10 What mix should be protected? This is not usually clear (why?) Lifetime x Turns (weight of the tail) Sales cycle: imagine a 8 units grid (8 sizes x 1 unit/ size) that sells on an average sales speed of 0.1units/day => typical sales cycle = 80 days. (optimistic since different sizes of the same product sell at different speeds) Active mix within the internal supply chain vs the sales points Different treatment for Central Warehouse, Regional Warehouse and Point of Sales Budget(/Space) Constraint(s)

11 Demand Curve Conceptually Demand speed curve Buffer sizes (integer numbers) Should we hold these? 11

12 Setup Buffers (what, where & how much) What / Product CWH RWH POS Avail. RLT <= Life Cycle RLT >> Life Cycle Not Avail. NA NA SKU Buffers [Macro P] FMs: SKU Buffers SMs: Cat Buffers SKU Buffers [Micro P] OR FMs: SKU Buffers SMs: MTO/Pass Tru SKU Buffers* * Taking care of the 1 size buffer problem on the tail 12

13 Too much variety/mix/assortment Usually retail suffers with too much assortment (except when there is a variety constraint in the supply chain) A good rule of thumb: 50% of the existing variety on the POS or less if this will still insufficient to reduce overall stock on the POS AFTER the balancing of stocks Careful and forceful consideration of tail liquidation schemes 13

14 Is variability equal throughout the chain? How many consumption points do you have? 14

15 Quiz: Aggregation or not? Question: if we re moving from 1 store to 2 stores should we use aggregation? Why? And if the products sold are usually slow movers? Store 1 Store 2 Sales Area Sales Area Backroom+ CWH Backroom (FMs) FMs Backroom New Products Resupply of FMs New Products Supplier(s) 15 Supplier(s)

16 Fast & Slow Movers Some real data Type Speed (un/day) Fast Runner 2.87 Average 0.27 Slow Mover 0.10 Difference on speed: order of magnitude between average and fast and slow movers 16

17 Virtual CWH (RWH?) POS POS POS POS POS POS WH1 RWH for (3) WH2 RWH for (1) and (2) CWH for (1) and (2) CWH for (3) Replenishment Imports (1) External Suppliers (2) Internal Supplier (3) (factory) 17

18 FlashBack! See my presentation on ICO 2010 (Retail : Myths and Truths) Also: lost sales due to sales area and cash BLOCKAGE! 18

19 Setup Balancing Stock After Buffers are set what do we do with significant (> 2x buffer) excesses? -> Back to WHs! Not a nicety! (not explicitly in the classic injections also) Close to end of life products x far from end of life products Even close to the end of life should do the balance -> the impact on sales outweigh the cost of liquidation (by far, check it). Impact for the transition period Reducing of stocks (and possibly mix) on the POS Increasing availability of β (Betas) Reducing damage of γ (Gammas) (very significant!) Focusing sourcing on α (Alphas)! 19 Immediate Sales Impact (SLT) Fast Sales Impact Strong Sales Impact

20 Supplying Above or within the buffers (or what do I do with my batches)? Impact on Priorities What to do with batches Potential negatives Careful with grid batches (one of the worst kinds) Definition of grid batch: multiple SKUs in one batch 20

21 Batches: above or within Releasing batches WITHIN the buffer: wait until the stock level + minimum batch is below the buffer size. Releasing batches ABOVE the buffer: wait until the stock level is below the buffer size. Buffer size Buffer size RELEASE POINT Time 21 RELEASE POINT Time

22 Effects Within Less WIP (at first) No portion of WIP above the target (buffer size) Priorities are more aggressive DBR pushes buffer size up (batch size is a variability) Seasonalities might built too much/little inventory Only works well if the batch is less than a zone Above More WIP (at first) Priorities are less aggressive DBR must deal with buffers above green (cyan) Seasonalities are OK Works well with any batch size Relax the pressure to reduce batches 22

23 Example for 1 SKU (pull, no DBR yet) Estoque inicial WIP P.Rep/Alv o Before (MinMax): Stock: 103 Losses: 10% Turns: After (Pull): Stock: 54 Losses: 2% Turns: MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX MINMAX

24 Supplying Priorities On hand (measures the execution vis a vis the goal) In Transit (measures the execution of the supply) Evaluation matrix and expected behaviors 24

25 Uniform priorities! Priorities valid throughout the supply chain: Color: green low low priority New PO to release WIP Transit Stock Color: yellow, normal priority Color : red, high priority On Hand Stock Color: red, high priority 25

26 Buy or buy & expedite? CWH buffer: PO 3 PO 2 PO 3 PO 2 PO 1 PO 1 On hand On Hand 26

27 Focusing MTA Matrix On Hand + WIP Buffer Status (virtual Pipe) Blue Green+Yellow Red+Black On Hand Buffer STatus Blue Green+Yello w Red+Black if WIP = 0: OK else: Orders to be canceled/adjusted Down OK Expedite back to FG WH Release Orders Release Orders and Expedite Back to FG WH Release Orders Urgently Quick tip: List # of SKU Locations in each cell and below list # of SKU Locations with active Orders outstanting (WIP). 27

28 Focusing MTA Matrix On Hand + WIP Buffer Status Blue Green+Yellow Red+Black On Hand Buffer STatus Blue Green+Yello w Red+Black if WIP = 0: OK else: Orders to be canceled/adjusted Down OK Expedite back to FG WH Release Orders Release Orders and Expedite Back to FG WH Release Orders Urgently Any # of SKU Locations here = Order Releasing 28 Problems

29 Focusing MTA Matrix On Hand + WIP Buffer Status Blue Green+Yellow Red+Black On Hand Buffer STatus Blue Green+Yello w Red+Black if WIP = 0: OK else: Orders to be canceled/adjusted Down OK Expedite back to FG WH Release Orders Release Orders and Expedite Back to FG WH Release Orders Urgently Any significant # of SKU Locations here = Priority following 29 Problems

30 Focusing MTA Matrix On Hand + WIP Buffer Status Blue Green+Yellow Red+Black On Hand Buffer STatus Blue Green+Yello w Red+Black if WIP = 0: OK else: Orders to be canceled/adjusted Down OK Expedite back to FG WH Release Orders Release Orders and Expedite Back to FG WH Release Orders Urgently Any significant # of SKU Locations here with WIP >0 = Excess Releasing 30 Problems

31 Focusing MTA Matrix On Hand + WIP Buffer Status Blue Green+Yellow Red+Black On Hand Buffer STatus Blue Green+Yello w Red+Black if WIP = 0: OK else: Orders to be canceled/adjusted Down Focus OK on Priorities Release - Focus Orderson Release Expedite back to FG WH Release Orders and Expedite Back to FG WH Release Orders Urgently 31

32 Focusing MTA Matrix Real case example: 58 SKUs in production = Excesses to be trimmed urgent SKUs to expedite SKUs=43 regular SKUs not released 73 urgent SKUs released with insuficient quantities SKUs=57 urgent SKUs not released 32

33 Exceptions of DBR Reserved or segregated stock MTO consumption Auto DBR during seasonality changes Contractual limits (min/max) Coupled SKUs: One SKU substitutes an old (obsolete) SKU One active SKU may be used for another active SKU Symmetric (both ways) Asymmetric (only one way) SKU Equality (assumption) Recyclable stock 33

34 Supplying Frequency x transportation lead time Different variables, which (or both) should be used? Buffer Size = optimistic demand within the RLT factored by supply Murphy Transportation Lead Time Transportation Lead Time 34

35 Dynamic Buffer Management Classic DBM and its limitations Implementing DBM Too much green, too much red Exceptions to automatic DBM Very Small Buffers Exception handling Fast movers POOGI? 35

36 Implementing DBM (Too Much Green) Last green penetration Consecutive penetration area Coolling Period Buffer is reduced when consecutive penetration area > limit 36

37 Implementing DBM (Too Much Red) Last red penetration Consecutive Penetration Area Coolling Period Buffer increases when consecutive penetration area > limit 37

38 Small Buffers How to increase 1 unit buffers? (decrease to 1 unit is easy) TMR = # consecutive days of consumption adjusted for RLT 38

39 Exception Management When there are problems in supply DBR cannot be allowed to work automatically There must be some sort of Exception Management: MAIN GOAL: allow maximum scalability! IO: fully automated DBM IO: robust way of handling exceptions 39

40 Fast Movers and DBR Speed of change for DBR is big, but on events that change structurally the product distribution (like activating buffers in more consumption points, etc.) an additional change may be in order Also the RLT limits how fast FM Buffers can grow, therefore: Expediting actions should be always cultivated Resting period should never be tied to an specific RLT (but to a zone change) Additional change (upon detection of a new FM) may be necessary, but can be treated as an exception: an aggressive consumption triggers TMR, and leaves an exception, to be reviewed, presenting the possibility of further increase 40

41 POOGI on Distribution Is there? Is it DBM? Can we use the statistic of red penetrations? And green penetration? What is the assumption behind gathering red penetration instances and attacking the most frequent ones? The instances are coming from a same system (i.e. can be grouped) Problem with DBM: each target (buffer size) change modifies the system (transition period) and therefore is not a good source of statistics. 41

42 POOGI on Distribution A proposal Stable: good source of instances to aggregate statistically Transition: inadequate source of instances to aggregate statistically In an stable situation any given red penetration is statistically significant and may lead to sources of variability we can trim (internal) or mitigate (external) 42

43 Seasonalities 43 A Sudden change in demand that is known in advance (exception to regular DBM) Usually > 33% increase/rlt Types of seasonalities Within the noise: <=33% / RLT Normal: above Super-seasonality: above to a degree that many links in the supply chain cannot supply enough to meet the demand How to prepare (MTA/MTO) and supply during event Raising buffers (gradually) is a nice solution, other is to raise the reserve stock effectively raising the black level (more sensitive to changes in demand during change, but not very useful)

44 Seasonality Should we multiply all buffers by a factor? What about end-of-life products? Treat Fast Movers (FMs) as we do Slow Movers (SMs)? Error on the 1 size buffers Vast majority of 1 size buffers have sales speed so slow even a very strong seasonality should NOT change the buffer size. And what about transient Bottlenecks (or better: overloaded CCRs)? Some simple actions alleviate this: increasing frequency of shipments, establishing temporary WHs, pre-loading of some FMs. 44

45 Seasonality If we use the buffer (integer) values to recalculate: Here we have the correct values All this light blue area are roundup errors (due to the long tail their impact is quite big) 45

46 Seasonality Only applied on FMs or buffers > 1 Here apply the seasonality adjustment All this light blue of errors are saved by NOT applying seasonality here 46

47 Mix and Category Management Buy-once products (above) How to buy and resupply on the various points of the chain (above) Buffer Management and DBM Also for category buffers (where different SKUs are in the same buffer) Dealing with very slow products in not so long life cycles How to cut the tail even further and increase the freshness at the same time (Fashion and freshness myths and truths) 47

48 Demand Curve FAST MOVERS SLOW MOVERS 2 parts Fallacy of average ABC or αβγ? (more on my ICO 2010 presentation) 48

49 Demand Curve FAST MOVERS Only 40 out of 48, SKUs have on average more than 1 unit sold/day SLOW MOVERS 2 parts Fast Movers: 3-5% -> 50% sales, Tail: rest. Still want to average? 49

50 1 unit size Stock/Buffer 12.0 And the looooooooooooooooooooooong tail Quant(Y) Y

51 Tail Thinning Challenge the 1 unit buffer/tail problem by tail rotation (ex: 2 stores, 2 NP groups): Store 2 Store 1 SM2 NP2 FM2 SM1 NP1 FM1 = move to other store (rotate) TIME SM1 FM1 FM2 SM2 FM2 FM1 = preserve and promote This reduces significantly the buffer size for NP in the WHs and in the Stores and still allows for the effective discovery of FMs. 51

52 Freshness Usually seen as an aid to generating more traffic (impact on the constraint) More important: the gateway for more FMs! FMs FMs NPs FMs FMs SMs NPs SMs NPs SMs NPs SMs NPs 52 (not to scale) Time

53 Thank You Comments, questions? 53

54 About Humberto Husband and father changing the world one person at a time Scientist seeking to apply science to people s endeavors Hunter of hidden assumptions Teacher, student and colleague of students Strong believer of values over tools Practicing atheist and skeptic humberto@vectis-solutions.com 54