Supply Chain Control Tower (SCCT — Part 3 of 3)
How to Setup a Supply Chain Control Tower (SCCT)?
Previous: Supply Chain Control Tower (SCCT — Part 2 of 3)
Contents
How to Setup a Supply Chain Control Tower?
Figure 7 shows the 4 key steps to kick-start a SCCT.
Step 1: Identify & Prioritize Business Objective
a. Identify and Prioritize the Supply Chain Functional Areas
- Figure 8 shows a table that help define the business objectives.
- Create the 1st column first, with each Supply Chain Area
(SCA) placed according to priority.
b. Define Problem Statements
- Create the 2nd column.
- This column highlights the problem in each SCA.
c. Identify the Target KPIs
- Create the 3rd column.
- The Key Performance Indicator (KPI) highlights what the company needs to achieve.
Step 2: Develop Detection & Response Mechanism
a. Identify Data Elements (DE) Pertinent to Target KPIs
- Now that the company has defined its SCA KPIs, we need to find out… how to achieve these KPIs?
- We do this by first identifying the Key Data Elements (DE) that will directly impact these KPIs. (Refer to 1b. Data Processing and Figure 3 in the earlier pages)
- These DE could be found scattered across the company’s enterprise systems.
b. Define Thresholds for DEs
- Thresholds mean “Lowest or Highest Expected Value” or “Trigger Points” that will be activated whenever these DE uncovers non-conformity to the company’s KPIs.
c. Develop Response Strategy
- What if the DE Thresholds or Trigger Points get activated?
- An effective response is to develop “processes” across teams (aka Road mapping).
- We will see this in the next section…
Step 3: Enable Through Technology
The title “Enable through Technology” comes in after Step 3c: Design & Deploy. Do not worry about it for now, it will be explained later. The key purpose of this step is “Road mapping” — a strategy to react when DE Thresholds are crossed.
a. Identify Control Tower Building Blocks
Before we start “Road mapping”, we need to “Identify the SCCT Building Blocks”. Figure 9 shows the “Building Blocks” of the SCCT.
Actually,
i. Visibility = Early 360 Degree Visibility
- the 1st Layer of the SCCT as presented in Figure 1
ii. Analytics and Decision — Making = Cognitive Computing Engine
- the 2nd Layer of the SCCT in Figure 1
iii. Process Orchestration = Collaborative Responsive Mechanism
- the 3rd Layer of the SCCT in Figure 1
We are simply breaking down each layer into “blocks”. Remember that we are creating the SCCT for the very first time, thus these “blocks” are not finalized and will be added/removed later. After multiple runs, these “blocks” will inevitably transform to those “layers” as shown in Figure 1.
b. Create Roadmap
Figure 10 shows how “Road mapping” is done. We align the “Business Objectives” with the Control Tower “Building Blocks”. ince every objective is unique, they require only certain blocks, which are color coded in Figure 10.
c. Design and Deploy
Once the “Road map” is done, we “Enable this Road map through Technology” (which we mention in the title of Step 3). This means that technology is used to implement the “Road map”.
Step 4: Evolve Through Continuous Improvement
a. Post-Implementation Benefit Realization
b. Continuously Assess Value Creation by Monitoring KPIs
- Meaning, constant improvement/editing of the “Road map” and its “Building Blocks” until it solidifies to Figure 1.
c. Provides Input to the Roadmap in Recommending Enhancement of Capability Maturity
References
- Dhaneshwar, G., Singh, N., & Khan, A. A. (2018). How a Well Executed Supply Chain Control Tower Can Accelerate Digital’s Business Benefits: Cognizant.
- Magazine, P. (2019). Definition of Data Element. from https://www.pcmag.com/encyclopedia/term/40771/data-element
- Wikipedia. (2019a). Cognizant. from https://en.wikipedia.org/wiki/Cognizant
- Wikipedia. (2019b). Enterprise resource planning (ERP). from https://en.wikipedia.org/wiki/Enterprise_resource_planning
- Wikipedia. (2019c). Material Requirements Planning (MRP). from https://en.wikipedia.org/wiki/Material_requirements_planning
About Dr. Alvin Ang
Dr. Alvin Ang earned his Ph.D., Masters and Bachelor degrees from NTU, Singapore.
Previously he was a Principal Consultant (Data Science) as well as an Assistant Professor. He was also 8 years SUSS adjunct lecturer. His focus and interest is in the area of real world data science. Though an operational researcher by study, his passion for practical applications outweigh his academic background
He is a scientist, entrepreneur, as well as a personal/business advisor. More about him at www.AlvinAng.sg.
