Oct

We have been experiencing an increasing demand for dashboards lately. In these uncertain times, I have noticed that there is a strong need among customers to gain insight into the performance of the supply chain. Companies have more and more data at their disposal and with the right insights from these data, companies can make better decisions. According to BI Survey, this “data driving culture” is one of the most important Business Intelligence developments of 2020 [1].

A recent example is the coronavirus dashboard of the Dutch government [2]. Data from different sources have been merged into one dashboard, which quickly shows an increase in the spread of the corona virus. In case of a significant increase in the number of IC and hospital admissions, the Dutch government may decide to institute a new (regional) lockdown. In case of a significant increase in the number of IC and hospital admissions, the Dutch government may decide to institute a new (regional) lockdown.

I myself created a dashboard for one of our customers this spring, showing the performance of the service supply chain. In this blog I present my three main tips for designing a good dashboard.

1. Define a clear goal

First of all the client’s goal must be clear before you set up a dashboard. At Gordian, we are convinced that customers’ capital goods do not have to experience downtime due to logistical shortcomings. Based on this belief, we help customers to improve their performance. The goal of the customer is of great influence on the design of the dashboard. It determines the matters on which to focus on most Without a clear goal, it remains uncertain which information to focus on and which decisions must be taken.

2. Keep it simple

Secondly, apply the principle “less is more”. Try to display your company goal by means of three or four Key Performance Indicators (KPI) on one page. A KPI makes the performance of a company measurable and transparent, which is indispensable to achieve your objectives. At Gordian we mainly look at the relationship between the KPIs logistics delay time, stock value and operational costs to quantify the performance of the service supply chain.

By displaying a limited number of KPIs in one overview, you can focus on the most important indicators to achieve your company goal. In addition, use color coding to show compliance or non-compliance to the standard and line charts to plot the development of the KPI over time. By means of colors and graphs you quickly get a general picture of the trend of a KPI.

3. Add indicators to explain bad actors

Thirdly, the dashboard should help you answer the following questions:

• What is going well?
• What is not going well?
• What is the reason why things are not going well?
• Which actions are needed to improve it?

By creating a hierarchical KPI dashboard, bad actors at the highest level can be traced back to the root cause and the responsible process owner. For example, the logistics delay time is influenced by the delivery reliability (OTIF) [3] and lateness [4]. The logistics response time [5] and stock availability [6] are the main influencers of the delivery reliability. By means of cross-sections and drill down options in the dashboard you can quickly get an overview of the most important bad actors of a (K)PI.

By applying Deming’s Plan-Do-Check-Act cycle, you keep making decisions time and again in order to continuously improve performance.

With a good dashboard you can display the most important data in one overview, in order to make decisions based on facts. Make sure that decisions are always in line with the goal being pursued. Let’s hope that the coronavirus dashboard of the national government does not give cause to take extra measures to keep the virus under control.

[1] https://bi-survey.com/top-business-intelligence-trends

[2] https://coronadashboard.government.nl/

[3] The percentage of requests that were fully delivered on or before the requested delivery date

[4] The average number of days in which the requested delivery date is exceeded. Measured on all late deliveries

[5] The average number of days between issuing the request and the requested delivery date

[6] The probability that a request can be fulfilled from stock at any point in time