Consumer Products

One Network Solutions for Consumer Product Enterprises

Every enterprise in this sector is part of a larger network with a clear:

• Demand network - Buyer of services and products, Channels, Distribution, VARs, Retailers, downstream partners, parties, and enterprises; and,
• Supply network - Trading partners that are involved with the design, source, make, move, and deliver aspects, upstream partners, parties, and enterprises.

And every enterprise has to reckon with: demand variability, supply variability, and lead times.  Lead time is largely comprised of physical and system latencies.  It is the system latencies that One Network basically eliminates to improve responsiveness while dramatically cutting costs.

In the Retail-Consumer Products environment we saw the development of the CPFR protocol to pass forecast information between trading partners.  The retailer created a forecast they would then send to the Manufacturers.  The Manufacturers take the retailer forecast and revise it to reflect a picture of demand they think will more likely happen.  The Manufacturer would then send the forecast to their Plants and Co-Manufacturers who would then adjust the production requirement to reflect desired plant operational efficiencies.  The Plants and Co-Manufacturers then generate requirements for their Suppliers who again adjust the material forecasts based on local knowledge.  By the time the forecast ripples across the supply chain it bears little resemblance to the original forecast.  The original demand signal has been substantially distorted.

What does this create? Demand variability: a difference between the actual demand and that which was forecasted. Where does the name buffer come from? It’s a term that refers to the practice of creating inventory stores to buffer against demand variability. The level of inventory is proportional to the amount of variability in the demand signal.

What if you could get the actual demand signal and backward calculate its aggregate effect at every level, netting available on-hand and in-transit inventories?  And as you have supply problems, what if you can forward project them so that you don’t sell what you can’t make? One Network has changed the way the problem is addressed. Rather than view the solution approach as requiring complex algorithms, we have used relatively straight forward and simple math. The industry has grossly over-complicated the problem, using sophisticated mathematical techniques to generate ‘educated guesses’ what demand and supply will look like. One Network has created a platform that allows you to propagate actual demand and supply signals more clearly and without distortion. We have taken the guesswork out of the process, and thus the math is uncomplicated.

To do this you first need the ability to represent multiple parties within value network. We can model all parties; the store, the retailer DC, the manufacturer DC, the plants, co-manufacturers, and suppliers. That’s multiparty. That’s why supply chain solutions cannot do this as they are architected today. Next, we need to model multi-party transactions.  An order is both a buyer and a seller’s transaction.  Forecasts, shipments and movements are other examples. When one can model the forecasts, orders, inventories, and shipments at every point in the value network, one can now manage the process with greater accuracy and better results than conventional systems.

One can create time-series view so as to manage the present situation as well as anticipate the future. So at each level in the value network one can model a time-series view of anticipated demand and supply, to calculate the upstream forecasts based upon the aggregate downstream forecasts. To accomplish this you need the multi-enterprise network model, the transaction data as well as the system resource scalability to accommodate massive data processing load inherent in this problem. For example, Walmart has 4500 stores and 110 DCs. Del Monte has 45 forward DCs and 60 manufacturing locations. With 17 weeks of forecast the number of location-forecast combinations becomes enormous, and thus only specialized computing architecture can manage. Conventional planning tools are incapable of generating and managing this data because they were never architected to do so. 

There are three issues to address when you build a supply chain solution.

1. Demand Variability. The difference between expected and actual demand. In the retail world, best in class forecast error at the DC is 18 – 25%.  This variability amplifies as it ripples upstream (backward) through the supply chain in a process known as the ‘bull whip effect.’ Best in class supply chain services are those that are driven by the forward-most demand. 
2. Supply Variability. The difference between expected and actual supply.  Supply variability ripples downstream (forward) through the supply chain, impacting service levels and operational costs.
3. Lead Time. The two major categories of lead-time include system and physical.  Physical lead times include manufacturing, transportation, picking, packing, etc.  System lead-times include order transmission and processing times.  System lead-times are the source of One Network solutions’ biggest differentiation and opportunity because of the interrelationship between system lead-times and demand and supply variability.

When trading partners operate from their own internal enterprise systems, demand variability is increased because each participant is using a different forecasted demand plan. Second, supply variability is increased because each participant is also managing a separate supply plan. If the forecast changes, for example, it takes significant system lead-time to flow the revised demand signal backward through each level of the supply chain, across multiple disparate systems. This is why there are significant out-of-stock problems on retail store promotions. 

It is common for a retailer to take 6 – 15 days to process an order to Del Monte and get the product back to their distribution center. CP companies like Del Monte outsource much of their manufacturing, while the retailers hold them accountable to service levels. Since Del Monte and other manufacturers typically get little or no information from their co-packers on what is actually being produced and shipped, it is difficult for them to meet the retail customer expectations with any confidence. 

The objective then is to reduce both demand and supply variability through improved visibility across the value network, and reduce lead-times. The reason for this is that inventory safety stock at each stocking location in the value network (in Days Of Supply) can be calculated as follows: SS = DV * SV * LT. At Del Monte we were able to reduce on-hand inventories at the DC as low as 3 days. We were able to do so by keying off of actual store sales and inventory data each day, and linking the supply process to that signal instead of the forecast. The then forecast serves to prepare the supply chain to act, but the execution itself is based upon actual demand. The weekly forecast ensures only that inventory is produced and staged appropriately in the distribution network. Producing excellent store service-level results with minimal DC inventories requires synchronization to the forward-most actual demand data each day.  This strategy substantially mitigates forecast error by moving pre-staged inventories to the appropriate locations based upon actual demand. 

Because demand variability is reduced and system response times are minimized because the order is managed across multiple participants, buffer inventories (safety stocks) can also be reduced significantly. If the forward-most actual demand data can be acquired each day (particularly for promotional items), and we can replenish every day, then we can ensure that supply is deployed to the actual near-term point of need.  Del Monte incrementally re-plans every 15 minutes. If demand hasn’t changed, but we’ve run into supply issues, we can re-plan how to move supply to mitigate the problem.

One Network Unique Capabilities

1. Services: every system capability on the network is a service. You don’t have to implement the entire solution; rather you can implement only a piece of it. 
2. Many-to-many: multi-party, multi-tenant model, ability to manage transactions across multiple parties, tunable system of control allows for secure multi-party interaction against a common transaction
3. Lead-time reduction: (1) reduce system lead-time by managing the order across multiple parties, and propagating demand and supply events, (2) reduce physical lead-times by managing transportation and appointment scheduling. The system manages according to a composite of transactional and fixed lead-times rather than purely fixed lead-times used in most conventional applications. 
4. Planning married with execution
5. Incremental planning
6. Results: reduced inventories, increased service levels/decreased lost sales - increased revenues (Del Monte experienced a 16% increase in revenues by selling more into the same customer base), reduced system lead-times, reduced physical lead-times and reduced transportation costs (due to more effect load building and reduced expediting costs). For more details on Del Monte case study, please view the Webinar hosted by AMR.

One Network Delivery methods and Solutions

One Network recognizes that a lot of effort and money has been expended especially into the traditional enterprise-centric systems and also most of the bandwidth of the IT staff is consumed by the run-and-maintain aspects. This leaves precious little bandwidth so our primary method is to deliver these solutions as Software-as-a-Service or SaaS. SaaS in an on-demand paradigm of IT solutions delivery that allows for trial before usage, pay for what you use, and rapid value with minimal IT hassles.   According to an Aberdeen study - SaaS can provide capabilities similar to traditional implementation with up to 40% lower costs! One Network provides solutions in a number of various areas each of which is largely a plug-and-play with the current infrastructure.

One Network has developed multi-party solutions using its Platform (Refer Platform technical paper for more details) for 

• Data Management
• Transactions and Execution Management
• Continuous planning and execution

The power of One Network platform is such that anyone – any Enterprise, 3rd party, or One Network can develop such solutions using the Platform-as-a-Service or PaaS paradigm. The concept is similar to applications on iPhone, Facebook, or Force.com.  The differences being that instead of dealing with generic apps, here the applications have to content with multi-enterprise supply chain business processes and preserving the sanctity of multi-party transactions and data models.

One Network has Customers using its SaaS solutions in the following areas:

1. Data Management
2. Order Management
3. Procurement
4. Manufacturing
5. Inventory Management
6. Deployment
7. Transportation
8. Demand Management
9. Replenishment
10. Store Operations

Each of these solutions is available on the network as a service. You don’t have to implement the entire solution, rather you “mash-up” the relevant SaaS with your Enterprise process such that you can extend into your trading partner value network.  This allows you to simplify the truly end-to-end supply chain while making it much more responsive and dramatically reducing costs.

 

Top Consumer Products SaaS offerings from One Network

1. Demand Sensing. Traditional demand management has been content with passive demand management, largely within the four walls of the enterprise. The ability to sense actual demand consumption and detect anomalies at the most granular level (Ex. Shelf stock-out, phantom inventory, poor sales, etc.) is known as Demand Sensing. 
2. Demand Translation. The ability to translate the foremost actual consumption to every upstream node from the while netting out inventories and factoring the replenishment policies and rules at every node such that future demand is calculated. Since the near term demands (or more precisely order forecasts) are continuously calculated vs. forecasted, it is much more accurate.
3. Promotions Management  
4. VMI  
5. Inventory Planning   
6. Demand-Driven Distribution Planning with Order Aggregation
7. Last Minute Allocations 
8. Inbound Order Shipment prioritization 
9. Transportation Planning and Execution 
10. Purchase Order Forecast Management with Suppliers 
11. Data Management 

According to AMR, “Bottom line: with the price fluctuations of materials, the increased complexity of supply networks, and the compression of product lifecycles, buying groups need frequent and more accurate demand information to tune supply commitments dynamically. It is not the forecast per se, but the ability to sense and translate demand across the organization to make smarter tradeoffs.”