Manufacturing companies need effective tools to monitor employee working time and performance. This statement appears in many conversations, pointing to a tricky challenge, both managerial and financial. The reasons why companies choose to use these tools include:
Production management is a complex process, consisting of a combination of many indirectly and indirectly dependent components. This is defined somewhat more simply by Elwood Buffa, a professor at the University of California and co-founder of the modern approach to production management in his 1965 book entitled "Modern Production/Operations Management":
Production management deals with decision-making related to production processes so that the resulting goods or service is produced according to specification, in the amount and by the schedule demanded and at minimum cost.
So we can influence the production process in many ways, e.g. through:
I would like to focus on cost and process.
According to Eurostat, the evolution of labour costs in the European market from a historical perspective is as follows:
We can see that annual labour costs have been rising in the European market for more than 10 years. Depending on the period within a given year, the figure ranged from 0.5% to a whopping 3.5% quarter-on-quarter, compared to the previous year. Even more interesting are the statistics for Poland, narrowed down to the group of labour costs in manufacturing:
Why is this so important?
This is a long-standing trend which is both a catalyst for all kinds of optimisation and adjustments to production processes*.
One of these adjustments, according to a recent report from ARK Invest Big Ideas, is Manufacturing Automation [I recommend this report, as it addresses many technological trends: delivery drones, autonomous cars, manufacturing robots, including 3D printers]
So are there ways to optimise production costs? Yes, and interestingly enough, often without significant cost, and using existing data present in the IT systems you already have.
ERP, CRM, MES, CAD, CAM – each of these abbreviations represents an easily identifiable tool supporting (in different ways) production management. ERP, which stands for ‘enterprise resource planning system’, is most frequently responsible for order flow and sometimes for the warehouse, while also supporting the human resource, payroll and finance functions. CAD or CAM are tools that support computer-aided product design and manufacturing. CRM is a tool that supports building relationships with our customers. MES are tools which control and execute production processes in machines and operations on production lines.
In all these systems, the production process usually consists of 3 basic components:
From a mathematical viewpoint, the component most vulnerable to error is the employee. In almost every instance of a production process, machine behaviour can be predictably foreseen (for example, breakdowns, downtimes, servicing) with the aid of the software mentioned earlier, such as MES, OEE (Overall Equipment Effectiveness) or other management techniques available to Plant Managers.
This is also the case with the task (the manufactured product), whether at the lowest level of granularity, i.e. a specific activity, or across the entire work centre. In most cases, we are able to monitor this on an ongoing basis using appropriate techniques such as: Kanban compatible with Lean Management, software such as ERP, or simply by analysing the end result, i.e. the stock of finished goods vs. orders.
The situation, however, is different with the employee. Here, despite advanced management techniques, implemented procedures, standardisation of working hours, it is our biggest challenge within the production process of a manufacturing facility. What I observe most often are 3 approaches:
In order to achieve the best possible financial outcomes for a manufacturing company, the costs associated with both the task, the machine and the employee should be as low as possible. So, by creating or participating in the production process we have a real impact on these variables. One of the most important variables is the working time of a production worker. Working time is an effective factor for the cost of manufacturing the end product. How do we optimise it and how do we use IT systems for this? This is discussed below.
The approach I want to share is relatively simple – it’s a 5-step approach to generating savings and optimising the company’s bottom line on your own in almost every production facility:
In order to successfully implement any change, you need a well-considered strategy that you can finally turn into a pathway to achieve your goal. At this stage, it is a good idea to ask yourself a few questions:
If after answering the questions, you are thinking → yes, I want to use data to optimise the bottom line of my production facility, then you can safely move on to the next step, which is to have a good understanding of the environment in which we work.
However, perhaps you have come across answers that I hear often myself in conversations:
Then, I encourage you to reflect on whether this is the right approach and the right answers for the company from the strategic perspective.
We are surrounded by data that can be transformed into information to help us achieve our goals. For example, thanks to GPS data used by an application, we are able to determine the optimal route to a destination and then follow it (optimal in terms of any preference: shortest route, fastest route, etc.). The same is true for the management of production and the data that is produced within the company. They can be a great help and support to the Production Director, but they can also be a nuisance to the IT department, CTO or IT Manager. Here the important questions are:
Who/what do we trust: people or data? This question often arises behind the scenes in discussions relating to the optimisation of production processes. If we already understand what role data plays in the production process from a strategic perspective – then we should verify whether the people surrounding us in the production plant have a similar perception of data and its relevance.
It is possible that data with relevance to quality improvement or production efficiency, or even data that tracks the on-going operations within the production plant, is completely ignored by the ‘producers’ of this data. There might be an employee who does not swipe his time card or record the selected machine in the system before starting a job, or – even worse – there is no system at all that allows him to clock the working time.
People and teams should make business decisions based on data relating to the time/quality/scope of reported work in production.
To illustrate the overall concept of monitoring production workers, I will use a diagram:
In this way, in almost every production plant we are able to:
Depending on the size of the production facility, the above diagram can be used for a plant with a workforce of 10-50, 300-600 and 1000-2000 people. Useful input at this point will be provided by IT systems which:
The combination of several IT systems and their databases provides the best and most cost-effective results, sometimes accounting for up to 20-30% of production cost optimisation*.
*according to the Deloitte report “Digital lean manufacturing - Industry 4.0”
By combining the monitoring of production workers not only at the entry and exit level, but also with reporting/self-reporting and shift manager control, the first of the desired effects can be achieved – a full picture of working time, which is also a cost.
An organisation that relies on qualitative data is able to use it to solve any problem it faces.
Below, you can see on the graph how, in one of the production plants using this particular tracking method, the scale of completed operational data on the manufactured product and processes is increasing.
If you are wondering how you can save time in your production facility and how you can achieve this, you can schedule a free, no-strings-attached consultation to discuss a specific aspect of your organisation with a GGS consultant.