Major challenges for the meat processing industry – Yield and Traceability

Niels T. Madsen, DMRI [email protected]

FACTS ABOUT DMRI Founded in 1954 by the Danish pig producers

R&D tasks for the meat industry

International consultancy service especially on productivity improvement, product quality and food hygiene

120 specialists with competences covering all aspects of meat production and processing

Since 2009 a division of Danish Technological Institute

DMRI focus areas

Operations and manning Training Yield optimization and sorting

Carcass grading and sorting Carcass chilling systems Process and packaging with quality

Hygiene and cleaning Decontamination methods Increase shelf life Food safety inspection and control Traceability

Environment

Process design

Optimal animal handling systems

Food safety and hygiene

Automation

Product quality

Efficiency

DMRI offers innovation and consultancy based on research:

Reduction of water and energy consumption Heat recovery

By-product collection and handling Odour abatement

Scope of presentation

A: Managing the Yield potential processing pork • The tools • Optimisation • New methods and options

B: Traceability in the value chain  Drivers  Solutions  New trends, demands and options

Carcass grading and sorting Classification center 1990 -> ? Optical insertion probe

BCC-2 1997-> Vision AutoFom 2000 -> Ultrasonic-scanning

Manuel

CT-scanning 201? Virtuel cutting

The optimum carcass usage challenge

Matching quality requirements with carcass attributes to optimize the total value of the carcasses

Supplier data Carcass/cut data Sold goods & stock

Market sizes/quality req.

Production costs

Prices Farmer payment

Data

Sorting Production

Maxsimize profits

Planning

Optimizing

Sales

The importance of optimum usage and yield Typical cost distribution in a Danish slaughterhouse COST INCREMENT

Raw material 75%

Administration

1%

Sale + distribution

3%

Depreciation + financial costs

5%

Different indirect costs

6%

Packing material

2%

Labour costs

8%

TOTAL COST INCREMENT

25%

Finished products 100%

The raw material is the most significant slaughterhouse cost! Improving yield of higher value products will substantially increase profits!

Optimising yield  Sales and production planning  Matching orders and ed raw material  Grading and sorting of carcasses for optimum yield

 Reduce process deviation - give away:  Improved cutting and deboning methods, - by technology, or working with operator training and monitoring

 Production control  Production follow-up, weighing systems  Yield models

Basic Requirements for optimizing carcass usage

Multiple choices of utilization (carcass product assortments)

Yield models for carcass products (sorting group vs. yield) Technology for carcass and primary cut assessment and sorting logistics Other production costs (operator, transport, etc.)

Market data (min. and max. quantities of products, prices)

Yield optimisation model Meeting costumer product quality requirements How much of each product in an assortment does a carcass in a sorting group yield?

Middle: Product mix 1

Fat is better prized on the main product Middle: Product mix 2

Middle: Product mix 3

Middle: Product mix 4

Optimum carcass usage Inputs

Output

Raw material base: • Carcasses (LMP, kg, etc.) • # sorting groups

Product assortments (mutually consistent products made of a carcass) For each product: • Yield model (LMP, kg, etc.) • Quality requirements (LMP, kg, etc.) • Product specific variable costs • •

Price pr. kg Market limitations (min. max. sales)

Optimum usage of carcasses: • Optimum product assortments • Corresponding set of sorting classes (groups)

IT-tool GAMS optimization Value of raw material base ( turnover) Application examples: • Determination of optimum sorting plan • Analysis of developments in: • Market • Pig population

The optimization potential

What is the estimated value of optimum carcass usage by sorting compared to random usage? Probably 7% turnover increase (ref. Fleisch Wirtschaft Int.) How are we doing today? Best: Maybe about 4-6% compared to random usage Many: Un-exploited potential Often: Limitations in measurement accuracy, logistics, and sales Requires a business for the individual site

Carcass classification technology characteristics

Probes (optical rulers)

Vision

Ultrasound

Invasive Invasive/noninvasive Manual/Automatic Manual

non-invasive

non-invasive

Manual/Automatic

Automatic

Accuracy

Acceptable SD  2 LMP

Acceptable/good SD  2.2 LMC/1.2 LMP

Acceptable SD  2 LMP

Cost

low

high

Middle

Robustness

middle

high

Middle->low

Ref. examples: Commission Decision 2009/12 (AutoFOM,Denmark), 2011/258 (CSB ImageMeater, AutoFOM, Germany), 2005/240 2011/506 (various instruments, Poland) regulated by COMMISSION REGULATION (EC) No 1249/2008 of 10 December 2008 laying down detailed rules on the implementation of the Community scales for the classification of beef, pig and sheep carcases and the reporting of prices thereof

Accuracy and the value of sorting  Case: Population distribution (µ = 60 LMP, SD = 2,7 LMP):    

Q4: Q3: Q2: Q1:

23% 27% 27% 23%

Accuracy and the value of sorting  Population distribution (µ = 60 LMP, SD = 2,7 LMP)  Distribution of pigs measured to be e.g. 61 LMP (SD = 1,2 LMP) Table of true by measured (SD 1,2 LMP) Measured

True LMP Q4: < 58 Q3: 58-60 Q2: 60-62 Q1: 62 < Total

Q4: < 58

Q3: 58-60

Q2: 60-62

Q1: 62