blog for the global summit of supply chain insights compiling experiences with the adoption of multi-echelon inventory optimization, cfr http://supplychaininsightsglobalsummit.com/adopting-multi-echelon-inventory-or-other-advanced-analytics-the-struggle-with-supply-chain-collaboration/

1. Adopting Multi-Echelon Inventory or other Advanced Analytics? The Struggle with Supply Chain
Collaboration.
I came into the field of supply chain management in 2004. The master project for my MBA program in
that period was on ‘Collaborative Planning in the Extended Supply Chain’. Key questions to be tackled
were ‘how do we quantify the benefits’, ‘how do we create mutual trust’. The breakthrough of internet
technologies were assumed to drive the adoption of CPFR, Collaborative Planning Forecasting and
Replenishment.
2006-2009 I did a PhD on Multi-Echelon Inventory Optimization. It certainly helped me to define the
‘business case’ for collaboration. Basic principle of Multi-Echelon is we need to let go of a ‘local’ or
‘echelon per echelon’ optimization and adopt a ‘holistic’ or ‘network view’.
My PhD was on safety stock optimization. Instead of targeting high service levels from one echelon to
the next, and buffering local uncertainties by local safety stocks, you try to regroup uncertainties in a
limited number of locations. By letting go of the internal service agreements, you can pool risk and
lower the safety stock requirement. It’s counterintuitive at first, but you don’t want to duplicate safety
stocks, right?
When it comes to cycle stock, which is influenced by the applicable lot sizes, a similar holistic view is
required. Instead of having a local rationale where each step in the chain is defining its own optimal lot
size or re-order frequency, you look at how lot sizes across echelons match up and try to benefit from
synchronization.

2. The benefits from multi-echelon are always convincing. Reducing safety stocks with 30% is common.
Reducing cycle stocks and ordering/change-over costs with 30% as well. The benefits are higher as the
complexity of the network increases.
Though benefits are clear, adoption of multi-echelon has been very slow. Pilots within and across
company boundaries have been convincing, but we have not seen a widespread adoption. A research
report from the Aberdeen Group reported a 13% adoption in 2004. My gut feeling is that todays figure is
not necessarily much higher.
How come that the adoption is limited? Can we take a lesson for other advanced analytics tools?
First, multi-echelon is counter-intuitive. Since my first simulation results in 2006, indicating it’s better to
push out safety stocks towards the customer facing echelons, I have been fighting with people for them
to accept the conclusion.
Second, multi-echelon is quite complex. You need a lot of data, which is available, but never clean. The
algorithms are more advanced than the simple safety stock or EOQ. Not every organization is ready to
adopt that level of knowledge.
Where 1 and 2 are a problem, they are, in the end, easily overcome. My biggest concern over the last 10
years remains how to create trust.
Trust within the company boundaries, when it comes to optimizing inventories from raw materials, over
intermediates to finished products in centralized and forward stocking locations. There is an apparent
conflict with the cost focus of purchasing, the OEE focus of production, the reluctance of the central
warehouse manager to push out stock and lose control.

3. Trust across company boundaries, when it comes to letting go of the high On Time In Full delivery target
for the supplier, and measuring the stock availability on your side instead. Or when it comes to
increasing dealer stocks while reducing your stocks. In many cases the increase at the dealer can easily
be financed (via consignment) by the bigger reduction you have centrally. We might intellectually agree,
but we’re very cautious to adopt this type of practices. Sales and purchasing are notoriously suspicious.
I see a comparable challenge for other advanced analytics. Going against gut feeling takes time and
effort. Complexity requires maturity, which takes time to build and sustain. Cross borders, within or
across companies, requires trust, which comes on foot.
On the positive side, companies have been investing in supply chain and analytics competence, are
developing horizontal career paths to stimulate end-to-end thinking. Pressure on cash, cost and service
has been steadily rising. All together it will stimulate further adoption of multi-echelon and other types
of advanced analytics. It might only take more time than at least I anticipated some 10 years ago.