Role of Logistics Analytics in Warehouse Operations Automation Projects.

We live in the digital age, where enormous amount of data is gathered around us with (or sometimes without) our aware consent. Logistics is not an exception here. But data itself is still not the information – the analysis process is the one which put them in right order and allow to make conclusions, creating real value of the data in logistics. Here is huge area of expertise, where logistics analytics is bringing value and prove to be irreplaceable. The very special area, where using logistics analytics plays crucial role, is automation of warehouse operations.

Orbico Supply, a division of Orbico Group - European wide distributor and contract logistics operator - is present in 20 countries and operate in dozens of warehouses. Having digitalization and automatization of the processes as important element of our Supply development  strategy, we already implemented number of automation solutions covering shuttle systems, pick-by-light, pick-by cart, conveyor and carousel systems. Other solutions like voice picking systems, AMR or autonomous forklifts are under analysis or in early stage of implementation.  

In each of these projects data analytics is important and supporting for better decisions taking.

In the conceptual phase of automation projects, data analysis help to understand what are our streams and where is potential for automation.

Here following elements needs to be considered: 

- preparation and understanding basic data about products masterdata; class of the rotation; structure of the distribution (wholesalers, e-commerce, modern or traditional trade); daily, monthly and yearly peaks; any special packing and delivery conditions expected by customers; sales units definition; share of returns etc. 

- assumed overall throughput of the system for the moment of start, taking into account all critical points of the installation (stocking up, storage, picking, packing and dispatch)

- future trends – the system is not designed only for go-live date, it is scheduled to realize the processes for 5-10 years period afterwards

- general shape of software landscape in order to ensure right data and process flow,

- alternative costs which will appear due to switch to automation (for example unification of the shipping boxes can require additional transport capacity  or higher consumption of filling materials , standardization of the processes ensure repeatable quality and optimal operational costs, however that might result in lower flexibility in process realization etc.)

Every of these factors might have serious impact on the right selection of final automation solution and its assumed capacity. That will finally determine if our automation will bring expected ROI, or not. Usually, automation system provider or external consultancy advisors do the analytics job, but having own perspective prepared by internal analytics team is pretty much helpful, giving better comfort for decision makers.

Second important area of data analysis in automation projects is ramp-up process and daily operations procedures.

Ramp-up is process of starting the solution and gradual achieving of target efficiency and quality. Depending on the automation type, team expertise, support from the vendor and size of the  operations, this process may take from few weeks up to even one year.

Here logistics data analytics is helping in:

- right allocation of the goods to the automated and manual part of the warehouse.

Despite initial assumptions done at the moment of project starting, the business is going forward and some of the facts might change. In multi-customer 3PLwarehouses this is highly variable,assortment subject to the changes, their rotation class is changing etc.

There are two important aspects to be considered: one is related to the decision which products shall be stored in automated part of the warehouse (and in which sector of the automation), the second has to answer how many already selected products shall be stored in automation (how many days on stock in automated area) and how to organize replenishment process.

- in the multi-client automated warehouses where different type of goods and streams are mixed, logistics analytics is helping in  finding the best pattern in order to consolidate this shipments, which can not be fulfilled only from one zone (manual or automated), but anyway they have to be delivered to customer as one package.

- tracking the achieved efficiency and comparing it versus expected target efficiency. Usually in first stage achieved numbers are different versus expectations. Data analysis shall help us in better understanding what is link between technology and productivity of certain processes, and where are the gaps. Either they came from process, products or maybe people side. Or maybe all reasons all together.

Decisions about investment in automation has long effects on cost efficiency and productivity of certain supply chain. There is several things which need deeper analysis and about which sellers of the automation system do not want to talk too much. Reduction of the uncertainty when taking decision with help of right logistics analytics may bring significant reduction to the risk exposure, and definitely shall not be underestimated. Also getting the solution to the expected efficiency numbers will be much easier with right analytical skills onboarded.