Get the Best from Your Fashion Fulfilment Systems with Data Analytics 

A fashion fulfilment business has two sites and operates each in the same way. But while one performs well, the other experiences routine bottlenecks and full chutes in certain areas, causing recirculations and reducing capacity. Why is it and what can this fashion logistics business do about it?

By Sebastian Tietze

 

In this article we take a look at how data analytics can provide insights into the causes of these types of problems that can be used to make practical differences to everyday fashion warehouse operations.

Why focus on data analytics?

In today’s complex fashion logistics, facilities are under pressure to optimise their processes for greater efficiency and uninterrupted performance.

At the same time, they are dealing with the complexities of omnichannel sales, seasonal demand fluctuations, returns, overstocking and labour-intensive processes of unloading, picking and packing.

Fortunately, most facilities are collecting enormous amounts of information from their equipment and operators. By understanding and working with this data, they have extremely valuable tools at their fingertips to make better-informed decisions and improve the performance of their fulfilment centres. They can gain invaluable insights into what’s happening at their facilities, why it’s happening and how to respond for the future.

How data analytics enable data-driven asset and operations management

Fulfilment facilities tend to be concerned with one of two subjects in their day-to-day operations:

  • Equipment: How well the system is operating
  • Operations: How efficiently orders are processed

On the technical, or mechanical, side of the facility, data analytics can provide insights into component failures or threat of failures, breakdowns and replacements. On the operations side, data analytics assists with stock and resources monitoring, and overall operational capacity.

Data analytics give fashion fulfilment facilities the unique opportunity to improve operational efficiency without having to make drastic changes to their systems: they can enhance performance simply by basing their operations and maintenance on data.

1. How data analytics assist maintenance efforts

By collecting and analysing their data, over time asset managers will start seeing trends that can inform them with accuracy about the wear and tear of their components.

This is important as the lifecycle of any component will be impacted by operational and environmental conditions and these will differ from facility to facility. But with data analytics, managers will be able to determine with greater certainty when individual components need to be replaced and whether they can increase their maintenance cycles.

Think of the chains in the pouch system. Using data, maintenance professionals can monitor the lengthening of the chain over time to ascertain their wear and tear and determine timeframes for replacement.

Or the analysis of a conveyor sortation system’s vibration data. This can inform conclusions as to the chain’s lubrication status and when the system requires maintenance.

Or analyses that send an alarm when certain digital preconditions are met, such as an increase in a system’s drive power. The data can clearly indicate a problem that needs investigating.

Data analytics provides maintenance professionals with clear views of system performance: what’s happening, why and how to react.

2. How data analytics assist operational efforts

On the operational side of the fulfilment business, data analytics provide visibility into order volumes and structures. This allows managers to determine with accuracy the numbers and types of both resources and workstations needed, as well as identify any need for further operator training.

Take the fashion warehouse that typically handles large volume retail orders. How does it then prepare for workloads that are different from when its system was designed? Data analytics can identify the best use of workstations in relation to order structure and the best allocation of operators. Using a data-based strategy, managers can re-dedicate resources, workstations and packing materials.

Or what if the warehouse that uses the pouch system wants to ensure it always has enough empty pouches available to process its orders? Data analytics assists managers with ‘housekeeping’ routines and cycles by providing visibility into the dwell time of returned items stored in its buffers, for example, indicating they may need to be cleared to make way for immediate orders.

Here are some further, helpful applications of data analytics in operations:

  • Read rate of the RFID tags in the pouches. A suboptimal read rate will impact throughput.
  • Response time of the WCS for destination requests: A slow response rate due to, for example, heavy data traffic, will negatively impact capacity and the IT team will need to investigate.
  • Load rate on a manual induction workstation: Discrepancies in workstation speed can be detected and investigated. Is there a technical issue? Or is there a need for better load sharing between workstations?
  • Induction quality: A common issue impacting tilt tray and cross-belt sortation systems is the quality of item induction. Data analytics can help identify if operators need further training on how to better interact with the automated systems.
  • Movement of pouches in different areas of the system: Data analytics can prevent overload of specific areas, while ensuring there is available capacity (such as sufficient numbers of porches in a pouch system) to process orders.
  • Events from the unload areas: Data analytics can help determine whether certain workstations should be added to handle larger orders, smaller orders etc.
  • Server room performance: Based on data analysis, an alarm can warn something is wrong, such as a disk about to run full or overheating of the system.

How to implement data analytics

So how do fashion fulfilment facilities start implementing data analytics?

It’s actually rather straightforward: as long as a facility has a sufficient data collection infrastructure in place – the electrical or mechanical sensors that measure physical or electrical values – and an IT system, it can start. Read more about how fashion fulfilment facilities gather data.

Using data analytics for the different organisational units

Then it’s a matter of each organisational unit leveraging the assembled data:

  • Base: Most fulfilment businesses are already familiar with data analytics and are receiving basic trends and insights to certain criteria such as induction counts, discharges and discharge failures, recirculations and alarm counts. Now it’s a matter of looking at insights in real time and showing trends over time.
  • Maintenance: Maintenance professionals can start to create their maintenance plans based on real-time system data. Maintenance efforts will become less about routine schedules and more about focused priorities, leading to more efficient use of staff. Predictive analytics will also minimise reactive repairs and improve maintenance planning.
  • Operations: With data, operations professionals can start to gain deeper understandings of order flows and processes to change, improve and optimise them. They will start to uncover operational inefficiencies and counteract adverse performance effects.
  • Management: Management teams will be able to forecast every aspect of their operations, such as order volumes and maintenance needs and plan for future throughput. With data analytics, management will be able to analyse week-over-week or year-over year performance, set live site benchmarking and receive automated scheduled reports.

A facility does not require significant digital maturity and advanced automated systems in order for data analytics to be beneficial. There are standard sets of KPIs and data that are of base interest to all facilities.

A lot will depend on the type of information the facility wants to know.

Data analytics can be customised to answer specific inquiries to meet individual facility requirements.

Data analytics packages are typically transaction-based, reflecting the volume of data and the level of services provided. A facility sorting 100,000 items per day attracts a different pricing range than if it is sorting 50,000 or 20,000 items per day. But both volume and services are scalable values in data analytics.

Conclusion

The use of data analytics tools can assist fashion logistics operators in understanding what is happening at their facilities, why it’s happening and how to respond for the future. They can provide an easy and intuitive overview of current operations, upcoming events and maintenance status. Data analytics tools are critical components for any facility looking for insight into its everyday operations and seeking to improve performance and efficiency by optimising the use of the existing equipment.

 

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