Big data for analytics and prediction in warehouse production

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Big data for analytics and prediction in warehouse production

In order to increase efficiency and/or quality, you need to analyse the current workflow and working methods. For that, you must be able to collect and work with clear and reliable data. This is one of the reasons I propagate for an established WMS. The analytic tools definitely are features to look at when investing in a WMS. The system should also have a good interface to connect to other systems like for example TMS (transport management system) and/or LMS (labour management system).

Something important to keep in mind is that the quality of the data you get out of the system will never be better than the data you put into it. For example, basic data such as volume, weight, hours etc.

Examples of features for analysing: 

  • inventory optimisation tools that can help you plan your warehouse layout and picking routes based on shortest forklift driving distance or picking frequency;
  • replenishment analysis specific to customers or target regions;
  • workforce management tools that track total and individual time for each department and task in the warehouse and also can give you forecasts regarding man-hours you need at each department.

Some WMS have simulation and visualisation tools to make it easy to find time-thieves and areas in the warehouse where you can improve the flow. The system can help you locate bottlenecks in the flow. It is easy to place too many high-frequency items in the same aisle or rack.

When you reach a certain point where you feel that your employees have a great pace and their performance has reached the limit, you need to work scientifically (with facts) and methodically with help of data and analytical tools to take your warehouse performance to the next level. You need to look at where you spend most hours in the warehouse and how you can make that area more efficient -maybe with the help of features in your WMS.

You want as few human “touches” on the products as possible from the time the products arrive in the time they leave the warehouse. Every touch or every extra meter on the forklift decrease the value of the product. With good analytical tools, you can make these “touches” and extra meters visible and change the flow for the better. In addition, with the help of performance tools in WMS, you can see the effects of your changes.

Big data will be an increasingly important part of the work to develop warehouse production. I think those WMS vendors who will be successful in the future must broaden their product portfolio with more advanced modules. They must be able to offer, for example, integrated LMS, TMS and advanced interfaces to connect with IoT hardware like forklifts, RFID, tablets and smart-glasses. They must also be able to gather and consolidate data efficiently, as well as be able to present the information to the management teams.

The most successful WMS vendors in the future will be the ones who can develop technology to use big data for prediction of near-future scenarios in the warehouse. That would really help companies save money for example in labour and transport costs. It is important that the WMS has the great ability to gather data from other systems like ERP (purchasing), TMS and LMS and also IoT hardware to analyse data and make more precise predictions of for example workload in different tasks in warehouses and how many man hours you need for every task. It is important the systems have access to data as early as possible in order to analyse and make forecasts.

Remember; for a successful warehouse, you need to be able to collect and analyse data and make use of it together with great leadership and communication skills.

Roberth Karlsson is a logistics expert and the author of

Photo: Pixabay

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