Supply chain forms the backbone of most of the organizations, especially the ones which are operations heavy. It is essential for such organizations to run their supply chain processes efficiently and smoothly. In the recent years, analytics has played a significant role in overall improvement and optimization of supply chain processes of most of these organizations. In fact, supply chain analytics has become an inevitable part of large organizations.
This post is a continuation of the first post Sales & Marketing Analytics – Top Analytics Projects in Retail Industry. In the first post, we talked about the function which is at the forefront of every business and has a direct impact on the top line of the company. This post talks about the other functions of the business which may not be at the forefront but forms the backbone of the business. Operations and supply chain are as essential functions, if not more, to the business as the sales & marketing. Sales & marketing focus on selling products to consumers; while, operations and supply chain focus on producing those products and ensuring that the products are accessible to consumers.
There are five different sub-functions that we will discuss under operations and supply chain function. Then under each of the sub-functions, we will talk about top analytics use cases to make the business more data-driven.
The five sub-functions in operations and supply chain are as follow (refer to the image):
- Demand Planning
- Procurement
- Inventory
- Logistics & Transportation
- Vendor Management
Moving a step further, let’s look at the top supply chain analytics use cases spread across these five sub-functions.
A) Demand Planning
1. Forecasting
B) Procurement
2. Reorder Level Identification
3. Price Variance Analysis
C) Inventory
4. Inventory Rationalization
a) 9-Box Analysis
b) Variability (sigma/mu) Analysis
5. Stock-out Prediction
D) Logistics and Transportation
6. Fleet Monitoring
7. Network Planning
8. Route Optimization
E) Vendor Management
9. Vendor Scoring
10. Vendor Consolidation
Let’s now talk about each of the use cases in detail and understand how and why they are relevant in the business context.
1. Forecasting:
Forecasting is one of the most essential parts of demand planning. Forecasting sales (based on primary or secondary or both datasets) is essential to maintain a balance between meeting consumers’ demands and not get bogged down by excessive inventory. Traditionally, forecasting has been done through rudimentary approaches or based on one’s judgement. However, with advancements in technology and sophisticated algorithm, it is possible to do forecasting at SKU level with decent accuracy.
2. Reorder Level Identification:
Once you have identified (forecasted) the number of goods you need to procure, identifying the right reorder level is essential to ensure that the production doesn’t stop because of stock-outs, as well as ensure that working capital is not blocked because of incorrect orders. Historically, procurement managers have been identifying reorder levels at a product category level based on their judgements; however, now it has become convenient to calculate reorder level for each item that is procured. Moreover, based on historical data one can also identify the demand distribution curve that different products have and orders for each can be placed accordingly using statistical techniques.
3. Price Variance Analysis:
This may not be a very common use case, but this essentially provides procurement teams to identify cost savings opportunities. Price variance analysis means analyzing which all items were procured from which all vendors and at what costs? Are there any significant disparities in unit economics? Are we buying the same item from different vendors at different prices? Is there a business rationale for a higher price? Is this an indication for kickbacks or any kind of frauds by the procurement function? This analysis, though mostly descriptive and statistical in nature, plays an important role in identifying cost savings opportunities and uncover any fraudulent activities that may be taking place.
4. Inventory Rationalization:
This is one of the most common and most useful use cases of analytics for the operations function. Any amount of unsold inventory is blocked working capital for the company. The unsold inventory doesn’t just use the limited amount of space that you may have in your store/warehouse, it also has a significant impact on your working capital. Imagine you are a billion-dollar retail enterprise and you find out that you have an inventory worth USD 25 million lying in your warehouse which has not been sold for over a year now. Do you know how much money are you losing because of that? If you were (or if you already have) to borrow that amount of money from a bank at 6% interest rate per annum, you will be paying USD 1.5 million as interest. Assuming your net average profit margin for products in that USD 25 million inventory is 8%, your total margin would have been USD 2 million. Now, after a year you have already lost USD 1.5 million as interest, you are just left with USD 0.5 million as your profit which is 2% margin. That’s why inventory rationalization is one of the most common and most sought-after use cases in the industry.
5. Stock-out Prediction:
According to Retaildive.com, retailers are loosing around USD 1 trillion annually because of out-of-stocks (Link). Identifying which SKUs are likely to stock-out at what time is the key to handle this problem. This is not a stand-alone use case but has to be carried out with efficient forecasting and right reorder level calculation (Use Case 1 and 2). Avoiding stock-outs does not just help in preventing lost sales, it increases customer loyalty as well. If a customer visits a store and every time she finds what she is looking for, the likelihood of repeat visits increases considerably.
6. Fleet Monitoring:
Fleet monitoring is mainly a diagnostic exercise that companies carry out to understand the movement of their fleet, esp. in the cases where companies have owned or leased fleet. This helps companies in evaluating if their vehicles are under-utilized or over-utilized or adequately utilized. Also, the performance of fleet at a trip level, route level and by a driver can be assessed. Furthermore, the analysis at a trip level can help companies identify performance improvement opportunities in their fleet movement by following best practices.
7. Network Planning:
Network planning is an optimization problem carried out by companies having a presence across multiple-states or large geographical area. The objective of this problem is to identify the right locations of warehouses or distribution centres from where a company can ship goods to its distributors or dealers. In this optimization problem, the objective function is to minimize cost while meeting all the SLAs (service level agreements) signed with dealers. This exercise is usually carried out by companies once a quarter or half-year, depending on the growth trajectory, or when new locations are to be identified.
8. Route Optimization:
Route optimization is again an optimization problem but mainly carried out on run-time for truckers delivering goods to different locations. Route optimization is an exercise carried out to identify which route should a vehicle follow to deliver goods to intended locations. The inputs that go into this problem are the delivery loads (or orders from different locations), truck capacity, the distance of delivery locations from distribution centres and time constraints (defined from SLAs). Basis these inputs, our objective is to define a route which a vehicle should follow such that the cost is minimized, and all the deliveries are made within the stipulated time.
9. Vendor Scoring:
Vendor scoring, though descriptive in nature, is a very useful application of analytics for companies to score their vendors based on multiple parameters such as quality of products and service, adhere to service level agreements, the price charged for products and services, the criticality of a vendor in the overall functioning of the business. This is mainly carried out by companies which have thousands of vendors (manufacturing companies, for instance). This helps companies identify their top vendors objectively and develop strong relations with them to ensure business continuity.
10. Vendor Consolidation:
Vendor consolidation is another cost-saving technique followed by a lot of companies (primarily by companies having thousands of vendors, as in the manufacturing sector). The objective of this exercise is to identify if there are any opportunities to reduce the number of vendors that the company may have and by giving more business to a limited set of vendors, negotiate better prices or terms of business. Terms of business could mean shorter lead times or longer payment period. The latter part will help the company optimize its working capital.
All the projects listed above may not essentially be data science and machine learning projects, but one thing to note here is that the simple descriptive analytics can also provide multiple use-cases for companies which have a significant impact on business operations. Also, with the volume of data that is being generated these days, it is becoming more and more essential to use analytics tools even for slicing and dicing data.
We would love to hear your experiences with any of these projects. If you have done any other project that is not listed here, we would be happy to learn from your experience. Please free to reach out to us at info@aimonks.com.