Forecasting and Inventory Control: Mind the Gap

IFF Workshop on Supply Chain Forecasting and Operations 28 June 2016

Thanos Goltsos1, Aris A. Syntetos1 and Christoph Glock2

1Logistics & Operations Management, Cardiff University, UK 2Department of Production and Supply Chain Management, Technische Universität Darmstadt,

Germany

Background information: topic

• Background: • KTP: 2 years, co-funded by EPSRC, Innovate UK, company partner • PhD candidate in Inventory Forecasting in Remanufacturing

• The Issue: • Widely reported lack of integration between forecasting and inventory control, by

both the academic and practitioner communities • Little work towards addressing the fragmentation

• What: • explore & expose the issue, • consolidate arguments, • and provide insights

• How: classify, quantify BUT first we needed to QUALIFY

Inventory forecasting

• Example of an inventory forecasting system • Framework of analysis

Context: DGP ForecastingDemandInventory ControlForecast

Service Level

Inventory Cost

Structure of the presentation

Forecasting and (for) inventory control Context • Forecasting • Inventory Control • The Gap

An attempt to define integration and a classification framework Integration • What do we mean by Integration? • Our Integration framework • Dimensions of analysis

Brief discussion of searches and report of some preliminary results Literature Review • Keyword Exploration • Searches • Analysis of results

Forecasting perspective • Forecasting is a means, not an end. • Forecasting always serves a

decision making process: • Strategic: Make or buy, network

optimisation, new product development, etc.

• Tactical: targets for salespeople, promotional activities, etc.

• Operational: transportation, inventory control at the SKU level

Context: DGP ForecastingDemand Unknown

ForecastPerformance

• Typically assuming no subsequent stages of computation • Mostly concerned with forecast optimisation

Implications • Relative performance reversed

• Relative comparative advantages do not translate to any benefits at all

The fact that method x performs better than method y in terms of forecast accuracy, does not mean that this will also be the case in terms of inventory performance; both for fast and slow (intermittent) demand forecasts

Forecasting method x

Forecasting method Y

Inventory Forecasting

System

Service Level

Cost/Quantity

• Relative performance sustained but gains of different orders of magnitude

Inventory perspective

• Demand assumed to be known

• Assuming no preceding stages of computation

• Concerned with inventory optimisation while often disregarding uncertainty

Context: DGP Inventory ControlForecasting

DemandKnown

Implications

• Inventory theory is built around one main concern: given a service level, decide when and how much to order so as to minimise the inventory investment

• Different interpretations of this objective are available, but one should end up with achieved service levels that are more or less equal to the target ones

• It is well known though that this is not the case • This has been repeatedly shown empirically; especially for

intermittent demand items and very high service levels

The gap Proportional Sizes

Forecasting and Inventory ControlForecasting or Inventory Control

Forecasting and Inventory

Control

Inventory ControlForecasting

Inventory AND NOT

Forecasting papers = 80,623

Forecasting AND

Inventory papers =

874

Forecasting AND NOT Inventory papers = 20,120

Inventory papers = 81,497

Forecasting papers = 20,994

Legend Forecasting = “Forecasting Demand” Inventory = “(inventory or stock) control” Results from Scopus on 26 June 2016

The integration dimension level: 0

Context: DGP Forecasting?

DemandKnown

Inventory Control

Service Level

Inventory Cost

The integration dimension level: 1

Context: DGP Somebody,Forecasting!

Inventory Control

Service Level

Inventory Cost

Demand Unknown

The integration dimension level: 2

Context: DGP Forecasting Inventory Control

Service Level

Inventory Cost

DemandUnknown

The integration dimension level: 3

Context: DGP Forecasting Inventory Control

Service Level

Inventory Cost

DemandUnknown

Dimensions of analysis

• Forecasting methods • Forecasting metrics • Inventory policies • Inventory metrics • Methodology: analytical/simulation • Data: empirical/theoretical • Demand patterns: fast/slow • Supply chain nodes: single/multi

• Certain classes of papers troubling our taxonomy: Bayesian, CPFR, VMI, etc. • Degree of relevance of the assumptions

Keyword set generation process

Keyword Selection

SCOPUS Search

Initial Papers Sample

Relevant?

Paper Sample

Keyword Analysis

Yes

Expert Consultatio

n

Email Correspond

ence w/ Experts

Revised Keyword

Sets

Initial Keyword

Sets

Final Keyword

Sets

Keyword Sets

Populated

Keyword analysis

0

100

200

300

400

500

600

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Title, Abstract and Keywords

Exploring integration

Inventory Keyword

set

Forecasting Keyword

set

ANDNOT AND

Levels of integration 1-3: “forecasting and

Inventory control papers”

Level of integration 0: “Classic inventory

literature”

Levels of Integration:

1-3

Level of Integration:

0Excluded

Contextualised Inventory Control

Literature: 34,985

Contextualised Forecasting Literature:

58,730

Focus of the study:

920

Integration levels

31%

63%

6%

Level 1Level 2Level 3

Analytical vs simulation

51% 42%

7%

AnalyticalSimulation DiscreteControl Theory/SD

Demand data and distributions employed

0

0.1

0.2

0.3

0.4

0.5

0.6

Empirical Theoretical

0

0.05

0.1

0.15

0.2

0.25

Forecasting metrics

00.05

0.10.15

0.20.25

0.30.35

0.4

MSE MAD RMSE MAE ME RGRMSE MAPE

Forecasting Metrics

Inventory control policies and metrics

0

0.1

0.2

0.3

0.4

0.5

0.6

OUT (s,Q) (s,S) (t,s,S) (S-1,S)0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Cost Profit VAR SL Volume

Conclusions

• Defining and classifying the integration of inventory forecasting literature is not straightforward

• Integration attempts subject to oversimplification through unrealistic assumptions

• Trade-off between integrated theory and relevance • Any input/recommendations greatly welcome!

Thank you for your time!

Over to our discussant! (Bonus pie chart:)

Discussion of: Forecasting and Inventory

Control: Mind the Gap

IFF Workshop on SC Forecasting and Operations 28 June 2016

Kostas Nikolopoulos Bangor Business School, Bangor University, UK

Overview

• Thanos and Aris are right to report this lack of integration but what really constitutes integration is the thorn here

• We know that forecast performance cannot be linked with inventory performance: very few forecast accuracy metrics have some ‘inventory meaning’ (e.g. ME, MSE). However, these metrics are rarely used for accuracy reporting purposes

• It is also natural that the higher the level of integration attempted the more constraining assumptions we will need to make

• Understanding these trade-offs should be of considerable value in making further progress in inventory forecasting