Why behaviours determines supply chain outcomes …...Use Analytics to overcome the fog of SKU complexity, discover bad behaviours and value platforms and improve business outcomes

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Why behaviours determines supply chain

outcomes more than theory or tools.and how analytics helps one see things as they are.

John Harhen

27th Nov 2018

Engagements & Experiences

Group

Site

Global

short medium long



Industry 4.0

Big Data IOTArtificial

Intelligence

Digitalization Blockchain

An unfortunate choice of terms



Artificial Intelligence is a big house with many rooms

Framesheuristic search

Natural Language

understanding

Programming Languages

Semantic networksScripts

Rule based

systems

pattern recognition

machine learning



Pattern recognition = machine intelligence is not necessarily good science.

IF YOU CANNOT SEE YOUR SUPPLY CHAINS,…

YOU CANNOT EVEN BEGIN TO HAVE A THEORY OF HOW TO MANAGE THEM.

Use Analytics to overcome the fog of SKU complexity, discover bad behaviours and value platforms and improve business outcomes

Good Analytics is better that good theoryIt cuts like a knife through the butter of all the blah-blah theories that populate our business discourse

Moving beyond tools and theories to behaviour remediation. It is about using that data to understand and diagnose business behaviours.

Seeing the supply chain and knowing what is to be done

Rapid Analytics enables one do discovery and go deep in the characterisation of value platforms

Obtaining better Supply Chain Outcomes

95% of companies cannot see their supply chains



ERP data structure

Sales order

Work

Orders

InventoryPurchase

Orders

RoutingBOMitem

master



Sales Order & Sales invoice

Material Description Order Order Line Request

Date

Promise

Date

Order

quantity

Ship Date Ship

Quantity

Order Value Cost Currency Customer Bill to Ship to Country City Post Code

This data structure represents how you sell. This has every thing you need to know about• How individual customer orders, (below SKU level buying behaviour)• Abnormal sales bookings• Impact of promotions• MOQ, MOV base data, supporting cost to serve• How you promise• How you deliver that promise (OTIF)• Demand variation (possible use in demand stabilization).• Sales patterns (trend, seasonality, events)• Portfolio Strength (Gross Margin, Growth, complexity)• Where you deliver (Centre of Gravity) for distribution network design• Truck load utilization (Weight, Volume, pallet height)



Production Receipts to inventory

Material Description Order Receipt Date Order quantity Batch

This data structure represents how you produce. This has every thing you need to know about• Yield• Stability of the plan, and the creation of flow• Alignment of the production plan with the demand plan.• Adequacy of actual Campaign size, presence of inefficient production or over campaigning.• Execution of PFEP in reality• Gaps in the schedule.

also receipts from routing sequences



Purchase Order Receipts

Material Description Order Order Line Request

Date

Receipt

Date

Order

quantity

Order Value Currency

This data structure represents how you buy. This has every thing you need to know about• Price evolution by item• Category spend• Spend on Non Inventory Items• Abnormal pricing within a complex portfolio. Cherry Picked Prices



Inventory Record by Batch

Material Description Status Quantity Value Shelf

Location

Batch Expiry Comments

This data structure represents inventory outcomes. This tells you about• Quantity per storage location (configuration : units per pallet)• Inventory Aging• Consumption of space• Unstructured narratives about hold status• Excess of deficit compared to target by brand.• Warehouse consumption by commodity



enhancement

Temporal Handles

Parsing Arrays for item

description

SKU supercession

Washing to current

description

Group Technology

Codes

Missing master data

Grouping (brand, recipe,

ad-hoc category)

Store versus compute

optimization

Graph generators and macro control

from ppt-> excel

3 years data plus current

yearSegmentation



Adhoc Rapid analytics

Grab the data

Enhance the data

Use the data for supply chain diagnosis



It is not just about ERP

Sales order

Work

Orders

InventoryPurchase

Orders

RoutingBOMitem

master

OEE systems

ASRS crane movements

Laboratory information

systems

Labour Capture systems

Dosing control

systems (IMM’s)

CAPA

Advanced Planning system



Data Scale in Analytics

B2B

Business Transactions

100mb

1GB

B2CConsumer transactions

Online key clicks

N TeraBytes

N PetaBytes

Seeing the supply chain is having at hand and being able to analyse

▪ every sales order (data scale >1,000,000)

▪ every inventory item in inventory (data scale >100,000)

▪ every production order (data scale >100,000)

▪ every purchase order (data scale >100,000)Much of the supply chain value

in big data is at the B2B level.

We don’t need machine

learning/neural nets for that

We lack the cross disciplinary

capability to do the value

discovery



What we are after

▪ To know the business, within 2 weeks, better than the business owners themselves.

▪ This isn’t an IT project.

▪ It is an rapid discovery process to find value in changing supply chain behaviours.

▪ In some cases, it can have a due diligence component.

▪ One has to be agnostic about theories. Each supply chain presents differently, and often with surprises about what is really going on.

▪ To understand the costs of activities in the business, and how these drivers can be changed.



Favourite Diagnosticalignment of historic Sales, Production and inventory

External SalesProduction

Inventory evolution

Time>

Quantity

This simple diagnostic tells you every thing you want to know about ubiquitous erratic replenishment, bad forecasting, poor inventory outcomes, bad customer service outcomes

System ForecastManual Forecast(holt : double exponential smoothing)

Forecasting Behaviours• When naï ve users let advanced

systems do their stuff without really understanding the science behind the tool, or mastering the parameters, then the outcomes can particularly be bad.

• This is most evident when events are in play.

• While all of these behavioural failures are obvious once visualized, in most cases they remain hidden in the complexity of many SKU’s

a black swan sales event

Over intelligent forecasting sees seasonality where there is none, leading to over production

Forecasting Behaviours• The most common failure is the

use of a first order method, e.g. moving average, or single exponential smoothing in a situation where there is significant growth or decline.

• Such methods will lag in dealing with a step change or a ramp in volumes. In the case of naï ve methods they may even fail to pick up growth at all.

• B2B external lumpy demand can make the seeing of reality just that bit more difficult



Common Disfunctional BehaviourLack of Flow in Replenishment

▪ A depletion at the distribution centre triggers a replenishment, that is amplified by lot sizing and campaigning rules, shifts in forecasts or movements in inventory policies.

▪ dynamic safety stock setting is dangerous.

▪ We don’t make safety stock work for us.

▪ We don’t create flow for big runners.

▪ Doing naïve replenishment planning without a replenishment architecture is tantamount to letting the customer schedule the factory

▪ All one has to do, is examine the history of replenishments from factory to distribution centres, compare it to external sales, and thus see how we conspire to destroy flow.

▪ Erratic and lumpy demand waves hitting the factory is a value destroyer

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Pallets

Containers

▪ Changes in replenishment flow rates +/- 20% can be accommodated much closer in, than traditional order based replenishment

▪ This has the potential to reduce the frozen period in front of the plant to a couple of weeks!



Diagnosing the inventory behaviour gap Actual vs Target Inventory by Family and Category

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Deficit

Excess

Target

Actual

the theory-actual gap is far

more interesting issue than

who has the best theory

Problematic Brands with hidden and

interesting behavioural histories

Understanding Demand Behaviours• While we chase forecast accuracy, we

typically don’t look at how forecastable is demand, i.e. what is the best accuracy a robust technical algorithm can give us. This then serves as a limit to tools.

• It is often very interesting to look at demand below the SKU level and at the customer level.

• In general, we summarize the forecast accuracy to a % number, and don’t in fact reflect on what the visualization of demand tells us about behaviour.

• This can often tell use why demand is or isn’t badly behaved (bad selling or buying behaviours, poor maturity in promotions planning, parallel imports, wholesaler/customer buy ahead etc).

• It also talks to what this means for agility in our replenishment response.0

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Value Platforms in end to end Cost to serve

Packaging Line

Efficiency

Reduced annualized

cost of operations

Reduced

Consolidation costs

at factory

Inventory rightsizing

at factory

More Cases per load

in Primary freight

Better Primary

Freight rate via

appropriate

tendering

Direct shipment to

customer

Modal Choice in

Primary Freight (Box

type/transport

mode)

Inventory

Rightsizing in DC

Tenders for 3PL DC

operations and

secondary Freight

MOQ applicability to

ex-Factory orders

Returns behaviour

Rebates and Free

goods

MOQ/MOV

applicability to ex-

DC orders

Governance on

Packaging

configurations

Perfect Order

compliance

Route to marketDistribution network

Design

Portfolio

Governance

Customer Service

Enhancement via

fulfilment

compliance

Customer Service

Enhancement via

agility

Strategic Portfolio Management exemplar

Brand instance based

SKU Pareto within segment

Multiple rationales for SKU culling

Recognition of fellow travellers



Biotechnology example

▪ Biotechnology Company which underestimated its capacity by 24%, and proposed an unneeded and costly outsourced strategy

▪ Cell growth, and upstream time, and down stream activity yield in the real world were out of alignment with the complex recipe on the BOM.

Laboratory information

system

ERP transaction

history



Medical Device example

▪ Medical device company involved in clean room injection molding and assembly, was unable to deal with material losses of uncountable material consumption.

▪ The resultant analytics not only dealt with the material accounting issue, but provided a model of material flow that was used for both factory and 3PL warehousing sizing and tender.

OEE system

ERP transaction

history

Dosing system for

IMM’s



Medical Device example

▪ OEM manufacturer, with unknowable demand, found its demand was fully predictable, when viewed through a postponement lens.

▪ This opened the door from decoupling buffers, cell formation for high runners, and capacity management for a complex job shop.

Labour Capture

ERP transaction

history



Pharma example

▪ €200m Pharma site found 34% more capacity by identifying an unknown bottleneck.

▪ Additional contributions came from PFEP in Bulk, and product to line assignment in the packaging hall.

OEE system

ERP transaction

history

Transactions analysis for

ASRS movements



Pharma example

▪ €100m Pharma site found 180% more capacity by pursuing PFEP.

ERP transaction

history



Premium Drink example

▪ 28% removed off the global balance sheet.

▪ Particular horror stories about recyclables and premium wines, identified by data observation, and not by theory.

OEE system

ERP transaction

history

Advanced Planning system



Pharma example

Due diligence identified premature revenue booking at year end.

ERP transaction

history

IT IS NOT MAINLY ABOUT THEORY AND TOOLS….

IT IS MORE ABOUT BEHAVIOUR



The world of Supply Chain and Operations Management in 10 statements

▪ Most people just don’t understand capacity, mainly because capacity is elastic. This lack of understanding is a grave error. Capacity is cost. Capacity creation is opportunity.

▪ One size fits all planning models is not a value platform.

▪ Flow is the most important word in the lean lexicon.

▪ Do you know the tipping point between flow and efficiency as a value driver?

▪ Inventory is like cholesterol : Do you know the difference between good and bad?

▪ If you cannot see your supply chains, you cannot even begin to have a theory of how to manage them.

▪ It is not mainly about theory and tools, it is more about behaviour.

▪ We don’t know how to triangulate Production, Logistics and Inventory costs.

▪ We don’t think end to end.

▪ If you don’t do the supply chain engineering, you are only fooling yourself with lean-speak.



Value Propositions

▪ See your supply chain behaviours through use of rapid analytics

▪ Manage your cost to serve on an end to end basis

▪ Capacity is elastic with significant resonance. Capacity is Cost.

▪ Know the tipping points to pursue campaigning for efficient operations, and beyond which to go for level flow.

▪ Flow isn’t 1 number, flow can be varied.

▪ Move the average resource deployed as close as possible to the average demand

▪ Create clear capacity space which can be further loaded in order to dilute your fixed costs

▪ Review your conversion costs on a family basis

▪ Do examine demand patterns at the customer level and below the SKU level.

▪ Know how much inventory you should have globally by inventory node and category and compare this with how much you actually have. Use this gap to diagnose behavioural weaknesses in your supply chain.

▪ Maintain an opportunity map for inventory rightsizing

▪ Beware of lean-speak disconnected from the business imperative

▪ Choreograph a Sales and Operations Planning Process that adds value to the business

▪ Apply good governance to your portfolio’s

▪ Have a strategic roadmap for the journey you are going to take.



Offerings in Transforming the value chain

▪ Cost of a tablet (Cost Drivers, Appropriate Conversion Cost Benchmarking, roadmaps)

▪ Global Inventory (Rightsizing and targeting)

▪ Rapid Analytics (Demand, Inventory, Production, Purchasing, Customer service/Customer behaviours)

▪ Planning Architectures (Tipping point : Optimization via campaigning versus Stabilization via level flow, PFP)

▪ Capacity Assessment and Management (Capacity is elastic, Capacity is cost, most of the time capacity is unknown)

▪ Supply Network Optimization (Products and Plants assignment and future network design)

▪ Distribution Optimization (Container Fill, Modal Optimization, DC network structure)

▪ Cost to Serve (Pre-wholesaler Storage, Picking, Order Fulfillment, and Wholesaler behaviours)

▪ Portfolio Management (Landscapes of Value and Potential, Costs of Complexity, Targets for Rationalization, Fellow Travelers)

▪ Due Diligence and Post Acquisition Transformation

▪ Generic Defense (In the endpoint everything is generic, or is it? , Why many originator companies are not creating shareholder value?)

▪ Strategic Roadmaps

▪ Sales and Operations Planning (A choreography of required Business Conversations)

▪ Value Platform Discovery

▪ Extracting Value from Systems

▪ Purchasing optimization

▪ Supply chain review

▪ Measurement and change



Consultant Profile : Dr. John Harhen▪ Deep functional competencies in Supply Chain Management, Operations Management, Strategy

Development and Systems Deployment.

▪ 25 years senior management experience in Pharmaceuticals (Rx Originator, Generic, and OTC), FMCG (Premium Spirits, Beer), Medical Device, Private Equity, Healthcare Distribution Technology (hardware, software and services).

▪ Orbsen Consulting Incorporated in 2003. Reincorporated as Orbsen Knowledge Services Ltd in 2012.

▪ Main Clients : Diageo global supply chain, Teva Pharmaceuticals (Czech, Waterford), Lake Region Medical (Ireland, Minneapolis, US), William Grant (Glasgow), KRKA (Slovenia), Creganna (Galway), Walmark(Czech), West Pharma/Tech Group (Dublin, Aachen), Ivax Pharmaceuticals Europe, GSK CH (London), Juniper (UK/US), Glanbia, Skyepharma, Egis (Hungary), Natus (Galway), United Drug (Ireland), East Pharma/Deva (Turkey), Fournier(Cork), Wyeth, Qualitest (US), Amneal. Many international private equity due diligence activities and many other short assignments.

▪ Previous Employments : Wyeth, SmithKline Beecham and Digital Equipment Corporation, in roles such as Materials Director, Operations Manager, and Knowledge Based Solutions Manager.

▪ Very strong accreditation▪ Ph.D. Industrial Engineering/Operations Research University of Massachusetts (Amherst)

▪ M.Eng.Sc. U.C. Galway, Ireland

▪ B.E. Industrial Engineering, U.C. Galway, Ireland

▪ Author : Production Management Systems plus 1 dozen papers.

▪ Successful patent application USA (Strategic Planning).

▪ M.I.E.I.,

▪ CFPIM (until 2003)

▪ Expert advisor EU DG XII (4th Framework Programme) and XIII (CIM Europe)

▪ Council Member of the Court of Experts of Supply Chain Management.

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Why behaviours determines supply chain outcomes …...Use Analytics to overcome the fog of SKU complexity, discover bad behaviours and value platforms and improve business outcomes