forecasting and inventory control: mind the gap · forecasting and inventory control: mind the gap...
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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
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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
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!
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