2Is Inc.

Case Studies

Learn what 2Is has accomplished for others, and what we can do for you.

Stochastic Modeling

Challenges

Making accurate bids in order to win business development pursuits.

Solutions

2Is forecasts accurate prices backed by data that helps customers attain business development pursuits.

Case Study

2Is Inc.’s initial engagement with one of four business sectors of a prime OEM required us to develop a stochastic model for a multi-$B business development pursuit. In one of the final program review meetings with Executive Management, the discussion focused on their internal “Price to Win” Cost when placed on 2Is’ stochastic model “Cost to Perform” S-curve.

The customer’s internal “Price to Win” forecast was less than 2Is’ forecasted “Cost to Perform.” In addition, we were able to answer executive questions by providing answers grounded in the data. The internal “Price to Win” team eventually fell back on assumptions. The Sector President approved a price higher than the internal “Price to Win” team’s recommendation. Our client lost the bid.

One year later, the winning contractor was in default of contract performance requirements, paying significant penalty costs and losing money. The OEM Sector President hired 2Is to model four additional pursuits and priced consistent with the stochastic model S-curve performance probabilities.

Our new customer won all four pursuits, and extended their engagement with 2Is beyond the Stochastic Model into 2Is Inc. Analytic Platform (2AP) to support execution. They are now performing at a level that satisfies both the customer and the comptroller.

Stochastic Modeling graphic

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Multi-Echelon Inventory Optimization

Challenges

Balance customer availability while minimizing inventory investment and holding costs.

Solutions

2Is reduces inventory investment costs through the use of stochastic modeling and machine learning capabilities.

Case Study

One of 2Is Inc.'s long-term customers contracted us to support a US Partner-country Performance Based Logistics pursuit of a small, non-US, fleet of aircraft. The operating environment involved a multi-echelon, hub and spoke supply chain to support operations and repair involving multiple levels of component indenture. The challenge is to balance customer availability while minimizing inventory investment and holding costs.

Their in-country supply chain organization, using deterministic models with fixed parameters and rules of thumb, recommended a relatively large inventory investment to support the contractually required operational availability targets.

2Is' approach used a combination of stochastic models and the judicious use of machine learning capabilities. 2Is' inventory optimization approach reduced our customer's inventory investment by 30%, contributing to their winning bid. In the execution phase, the ongoing inventory optimization continues to support Operational Availability targets at a reduced cost. We are now partnering with this customer on a second opportunity with the same Ministry of Defense.

Multi-Echelon Inventory Optimization graphic

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Expert Rules and Predictive Analytics

Challenges

Program personnel spend so much time firefighting yesterday’s problems they don’t have time to detect and resolve tomorrow’s.

Solutions

Using SME created predictive performance measures, the system flags anomalies and alerts users to issues that require resolution, before they impact program performance and/or cost.

Case Study

A DoD customer required a retail stock level at a forward operating base for critical spares associated with a particular weapon system. Stock replenishment orders outside the continental U.S. (OCONUS) normally fall under DoD time-definite delivery (TDD) Category 3, which is the slowest transportation mode and can take several months to deliver OCONUS shipments. However, TDD Category 1 provides expedited transportation modes, which can deliver OCONUS parts within two weeks or less. A stock replenishment order would automatically be assigned TDD 3, but assigning a specific required delivery date (RDD) code on the stock replenishment order assigns the shipment to expedited transportation. The assignment of the expedited shipment RDD code for the critical component stock replenishment required manual detection and intervention.

2Is’ Expert System Platform (ESP) uses business rules to programmatically identify discontinuities before they become costly problems or affect future program health. ESP rules also provide clear resolution strategies to guide users through corrective action. A DoD subject matter expert (SME) created a rule in ESP to check all stock replenishment orders for these specific parts, from that specific location, for the expedited shipment RDD code. Using the SME-created ESP rules, the system flagged the stock replenishment orders without the necessary TDD Category 1 code the day after the reorder was established in the system. DoD users referenced the case studies to swiftly correct the RDD code before the stock replenishment was shipped, ensuring the parts arrived on time.

Expert Rules and Predictive Analytics graphic

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Data Aggregation and Synthesis

Challenges

In order to make informed program decisions, it is necessary to have access to a variety of data, which is stored in multiple databases that generally do not interface with one another. At best, one can extract and paste most of the required data into an Excel spreadsheet; otherwise, teams must manually toggle between databases.

Solutions

2AP is “data agnostic” in that it integrates diverse databases containing inconsistent data formats and data transfer protocols. 2AP can extract, transfer, transform, and load data from any location, presenting it to decision makers in the format they require.

Case Study

An original equipment manufacturer (OEM) customer was supporting the maintenance, repair, and operations (MRO) requirements for a military aircraft under a contractor lifecycle support contract. During the transition, the incumbent, who owned the item master database, did not provide it to our client or the government. The program used different systems to manage bills of material (BOM) and maintenance reporting systems at the organizational and depot levels. Each supply chain management system had user access restrictions, constraining the users’ ability to perform certain operations. Compounding the problem, the systems identified the same part in different ways; e.g., by part number in a BOM, by National Stock Number in a contract, and by vendor-specific part numbers in SAP.

2AP pulled part identifiers and associated data from each system. Data analytics were based on subject matter expert (SME) decision rules for data source precedence and part identification schema. These data analytics allowed the disparate data to be consistently linked to the preferred part number. Subsequently, in face-to-face interviews with our OEM customer, the 2AP development team designed the user interface that transformed the data into information to drive knowledgeable decisions.

The seamless integration of several previously unlinked data systems generated a clearer picture of component challenges, leading to better and more timely outcomes.

Data Aggregation and Synthesis graphic

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Have a Question?

Contact 2Is Support:

support@2is-inc.com
(508) 850-7520 x260