The Impact of Hard and Soft Attributes in Your Supply Chain

By Dr. Sanjoy Paul, Prof. Hau Lee and Mahesh Veerina

In this series, we put different tracking approaches on a common foundation and categorize them into that we refer to as hard and soft attribute-based tracking. Then we argue why both hard and soft attribute-based tracking are important and how one complements the other, leading to near-optimal visibility. In this blog post, we discuss the impact of hard and soft attributes on different members in the supply chain, and how blind spots can be eliminated if both the hard and soft attributes are tracked. For more on this topic read the whitepaper, A Holistic Approach to Supply Chain Visibility.

Hard and Soft Attributes

At its core, supply chain visibility provides the status and location of the raw materials/components, from the suppliers to the factory to logistics to the shop floor to the finished goods in the warehouse and distribution centers, and finally, to consumption.  As we described in our earlier blog post, there are two dimensions to visibility – one that involves hard attributes and the other that involves soft attributes. Hard attributes are location, condition (vibration/shock, ambient temperature, humidity, pressure), timestamp and count (see Figure 1) that can be captured using sensors. Soft attributes are classified mostly as context.

Figure 1: Framework for Supply Chain Visibility

We identify two types of soft attribute context: (1) business process context and (2) environmental context. Business process context refers to the steps in business processes, such as requisition, approval, purchase order, invoice, payment, picking, packing, shipping, storing, etc. that can be captured in the system during the execution of various business processes. Environmental context, on the other hand, refers to factors adjacent to the core functions of the supply chain, but have an impact on the efficiency of the supply chain. These contexts are captured by systems for purposes other than the supply chain. For example, weather conditions are captured for weather forecasts and traffic conditions are captured for helping drivers with navigation. But both of these parameters – weather and traffic – are directly relevant for logistics operations – impacting the supply chain.

The Impact of Hard and Soft Attributes

These attributes, whether hard or soft, signify potential changes in values or impacts to different members in the supply chain. For example, if a product is damaged (hard attribute), then the value of the product would decline for the receiving member, or another member's liability (e.g., insurance or logistics company) may go up. Similarly, if a payment term has changed (soft attribute) so that a physical product sitting in the warehouse has not changed ownership, then the balance sheets or liability risk of the potential owners could be affected. It is from such changes in values and impacts to the members of the supply chain that we derive value from visibility (accurate predictions, better planning, timely decision making, lower risk, higher revenue). Basically, hard and soft attributes are directly tied to values and consequences, making them relevant in the context of business. While the link of hard attributes to values and consequences is clear and recognized, we argue that the same is true for soft attributes, as they could fundamentally change the values and consequences of a product or an object.

Figure 2: Value Diagram when only Hard Attributes are tracked

If only the hard attributes are tracked using sensors, the Visibility Index will move through steps a-g (as shown in Figure 2) as the raw material/component moves from Supplier through Logistics to Factory and the finished good moves from Factory to Warehouse through Logistics to the Distribution Center, as there is no visibility of the soft attributes. That means there is no change in the visibility of raw material/component at the Supplier until it is in Logistics, although there have been multiple changes in soft attribute states at the Supplier. Similarly, there is no change in the visibility of raw material/component at Logistics until it reaches the Factory, although there have been multiple changes in soft attribute states at Logistics. The same thing keeps happening at the next stages as well, as the Finished Good moves from Factory to Warehouse to Distribution Center through Logistics. Thus, as the stage of Consumption is reached, the visibility is only at stage g while the actual visibility should have been at stage 6.

While the observation above is broadly true, that is not strictly true, as there are hard attribute-based events that can provide visibility during the so-called blind spots. This is captured in Figure 2 via the points in between a and b (or equivalently 0 and 1), b and c (or equivalently 1 and 2), and so on, until between f and g (or equivalently 5 and 6).

Let's specifically look at the points between a and b along the X-axis (or equivalently, between 0 and 1). When the materials to be shipped out are loaded on pallets before transporting them to the loading zone, the sensors can capture that information and make that visible. Similarly, when the materials reach the loading zone, the sensors can detect that and make it visible. Therefore, if the material is not picked up within a pre-specified duration of time (threshold), an alert could be raised with the shipping department stakeholder at the Supplier responsible for ensuring that the materials are loaded on to the 3PL trucks so that he can inform the 3PL and follow through with them until the material leaves the Supplier's warehouse.

One more example of fine-grained visibility based on hard attributes is at the factory (between c and d or equivalently between 2 and 3). Note that sensors can indicate if the materials needed for production are left unutilized for a duration of time. This is an example of the dwell time exceeding a threshold, which can trigger an alert with the production manager at the factory and ensure that the material is used for production. Typically, production involves a workflow as the materials move from one cell (or substation) to another and get transformed/assembled along the assembly line. Sensors are the best means to track the progress of the workflow as the work order (kept in a plastic envelope) for a specific batch moves from one cell to another. This is because a sensor kept inside the plastic envelope with the work order can be tracked. That means the location of the work order at any instant of time is visible. This is referred to as "At sub-station X" in Figure 2. Therefore, if the work order stays in a cell for more than a threshold amount of time, the line supervisor can be alerted, and actions can be triggered. Similarly, there are condition-based triggers that can make certain events of interest visible. For example, when the finished product is transported by Logistics from the Warehouse to the Distribution Center (between e and f or equivalently between 3 and 4), if the vibration exceeds a threshold indicating that the product could be damaged, alerts could be sent to the product owner so that the product quality could be checked before it reaches the Distribution Center and discarded (rather than stored in the Distribution Center) if damaged. Micro events triggered by hard attributes (or sensors) leading to better visibility along the X-axis are shown in Figure 2 as green dots.

In Figure 2, the Y-axis represents the Value of the system as it moves through various members of the supply chain. As the raw materials/components move from Supplier to Logistics, the Value of the system jumps from 0 to 1, but it is not obvious as to "how" the Value changed. That is, there are "blind spots" in the supply chain. Similarly, when the raw materials/components reach the Factory, the Value jumps to 2 in a step. Once again, it is unclear as to "how" the Value changed from 1 to 2. The same thing continues as the Value keeps jumping in steps 2 to 6 as the finished goods move from Factory to Consumption via Warehouse, Logistics and Distribution Center. As described earlier, there are micro-events triggered by hard attributes to provide more granular visibility during what are otherwise blind spots. However, that is not enough from a complete visibility perspective.

On the flip side, if only the soft attributes are tracked, the Visibility Index will move through steps  a'-g' (as shown in Figure 3). The steps in the business processes from Supplier through Logistics to Factory are executed and the business processes from Factory to Warehouse through Logistics to Distribution Center are executed, as there is no visibility of the hard attributes. That means there is no change in the visibility of raw material/component at the Supplier until the business processes at the Logistics start to execute, although there have been multiple changes in hard attributes (location, vibration, timestamp, etc.) as the raw material/component is transported by the Logistics company from the Supplier location to the Factory location. Similarly, there is no change in visibility of raw material/component at Logistics until the business processes at the Factory start to execute, although there have been multiple changes in hard attributes (location, vibration, dwell time, ambient temperature, ambient humidity, etc.) as the raw material/component is processed by the Factory after receiving them from Logistics, until finished goods are ready and delivered to the Warehouse. The same thing keeps happening at the next stages as well, as the business processes keep transitioning from those of the Factory to those of the Warehouse to those of the Distribution Center through the execution of business process of outbound Logistics. Thus, as the stage of Consumption is reached, the visibility is only at stage g' while the actual visibility should have been at stage 6.

Figure 3: Value Diagram when only Soft Attributes are tracked

In Figure 3, Y-axis represents the Value of the system as it moves through various members of the supply chain. As the business process execution transitions from the Supplier business process to the Logistics business process, the Value of the system jumps from 0 to 1, but it is not obvious as to "how" the Value changed. That is, there are "blind spots" in the supply chain. Similarly, when the business process execution transitions from the Logistics business process to the Factory business process, the Value jumps to 2 in a step. Once again, it is unclear as to "how" the value changed from 1 to 2. The same thing keeps happening at the next stages as well, as the business processes keep transitioning from those of the Factory to those of the Warehouse to those of the Distribution Center through the execution of business process of outbound Logistics.

Interesting things start to happen when both hard and soft attributes are tracked. Figure 4 shows the Value diagram with the supply chain visibility from hard attributes superimposed on the supply chain visibility from soft attributes.

Figure 4: Value Diagram when the diagrams from both Hard and Soft Attributes are superimposed

Note that in Figure 4, hard attribute-based tracking moves supply chain visibility in the horizontal direction (X-axis), while soft attribute-based tracking moves supply chain visibility in the vertical direction (Y-axis), each with their respective "blind" spots. However, the blind spots get eliminated when they complement each other. The best way to visualize the resultant Value diagram is Figure 5.

Figure 5: Value Diagram when both Hard and Soft Attributes are tracked

The "blind spots" can be eliminated in the supply chain and the progress in Value can be observed clearly if both the hard and soft attributes are tracked. That is, when soft attributes are tracked and are complemented with the tracking of hard attributes, the movement of Value can be clearly observed as it changes progressively from 1 to 2 or from 2 to 3 or from 3 to 4, or from 4 to 5 or from 5 to 6 (as shown in Figure 5 indicated by the solid black rectangles and the blue rectangles on the 45-degree line). The black rectangles in the diagram represent the Value corresponding to specific soft attributes on the X-axis, while the blue rectangles represent the definitive position in the value diagram as confirmed by the tracking of hard attributes. Note that there are no more "blind spots" as the raw material/ component moves from Supplier through Logistics to Factory, and the finished good moves from Factory to Warehouse through Logistics to Distribution Center, as there is continuous visibility of both the hard and soft attributes. In other words, there is no "ambiguity" about "how" the Value changes. Referring to Figure 5, when the raw material/component is at the Supplier, and the Supplier receives a Requisition followed by Purchase Requisition Approval followed by Purchase Order (PO), the Value keeps changing progressively. When the raw material/component is shipped and is on the truck as indicated by real-time location of the material available via hard attribute-based tracking, it reaches 1. Thus, the entire process of change in Value is transparent, compared to the case when only either the hard or soft attributes are tracked. Similarly, the Value keeps changing progressively from 1 to 2 as the Logistics is contacted, transportation is scheduled, raw material/component is loaded on to truck and gets transported as indicated by real-time location of the material available via hard attribute-based tracking, and finally the supplies are delivered at the Factory. This is possible if the soft attributes are tracked in addition to hard attributes. Finally, when the finished good reaches the stage of Consumption, the Value will be 6, as both soft and hard attributes are tracked as opposed to when only the soft or the hard attributes are tracked.

In summary, the visibility of the hard and soft attributes removes the blind spots, so that we get to see the true values over time. Such added visibility enables better performance management, better actions, and more accurate predictions (values of visibility).

About the Authors

Dr. Sanjoy Paul is an innovator, disruptive entrepreneur, and an industry-recognized expert in AI & IoT.

Prof. Hau Lee is a Professor at Stanford University Graduate School of Business and Co-Director of the Value Chain Initiative.

Mahesh Veerina is a seasoned Silicon Valley entrepreneur, technology executive and investor and is the President and CEO of Cloudleaf.

All blog posts in this series:

1. Hard and Soft Attributes: A Framework for Supply Chain Visibility
2. The Impact of Hard and Soft Attributes in Your Supply Chain. (This blog post)
3. 1 + 1 = 3: The Power of Tracking Hard and Soft Attributes in Your Supply Chain.
4. The Benefits of Tracking Both Soft and Hard Attributes.
5. Using AI/ML Analytics to Further Improve Efficiencies Within Your Supply Chain.