Our team delivers comprehensive services for optimization of the supply chain in wholesale and retail trade. It is such a full-range approach that ensures sustainable growth in the number of our customers in the context of ever-changing market. Methods that are workable today can and must be modified and improved tomorrow. We make use of the advanced technologies, such as Data Mining and System Dynamics, for analyzing and updating the supply chains, These and similar tools are successfully used by not only leading companies but also retail industry innovators (e.g., Wal-Mart) thus securing predictability, transparence and, therefore, stability of their businesses.

OUR SERVICES are designed to detect and prevent losses related to (1) eventual negligence, omission and/or poor knowledge of best practices by the employees at the audited facilities in the absence of ample instructions; (2) their deliberate acts (fraud).

Examples of losses at the perimeter of the supply chain in wholesale and retail trade:

• Errors and abuse in procurement planning that result in out-of-stock and/or hoarding.
• Incorrect calculation of the parameters of discount and bonus program, both for procurements and finished goods sales.
• Planning errors resulting in wrong supply management solutions (failure to meet the delivery dates, rising production costs, scarcity/surplus).

• Overpriced, fictitious, double and/or excessive purchases, including those deliberately effected by company employees.
• Losses caused by deviations from the planned purchase parameters.
• Procurement of raw materials in cooperation with or by involving contractors affiliated with the responsible executives while discriminating against the interests of the business owners.
• Unfounded financial conditions of delivery/operations/contractors' services (financial risks and losses).
• Losses due to deviation (including intentional deviation) from the discount and bonus policy, for instance, by persistent nonfulfillment of agreed contractual terms.

• Hoarding or shortage of finished products, inter alia through inventory control data garbling, as well as culling rate manipulations.
• Low efficiency of order processing (delay in shipment, wrong order picking, failure to perform obligations, etc.).
• Insufficient utilization of storage space and/or equipment.
• Low labor efficiency of warehouse personnel in their routine duties.
• Losses caused by non-observance of warehousing rules.

• Hoarding or shortage of finished products, inter alia through inventory control data garbling, as well as culling rate manipulations.
• Losses caused by incorrect discounts, e.g. discounts failing to meet the accepted practice of sales of low-margin products and high-season sales.
• Losses due to non-observance of shelf storage requirements.

Approaches and methodology

System dynamics – is a mathematical modeling technique to efficiently address a wide array of complex problems ranging from supply chain management in the sphere of industry and logistics up to selecting strategy parameters for business development. The System Dynamics was created during the mid-1950s in the Massachusetts Institute of Technology with a view to help top managers gain a better understanding of manufacturing processes and, accordingly, pay due regard to interrelation of managerial and financial decisions with manufacturing cycle. The System Dynamics based models are used to solve problems proceeding from existing cause-effect relations between all parameters (including the effect of delaying the implementation of decisions made) of a complex dynamical system. The advanced logistics experience has proven that the System Dynamics features make it possible to find optimal solutions of retail problems for a triangle of procurement planning, inventory management and distribution of orders.

The FDA purpose is to generate the smart data samples (transactions) in which the concentration of losses due to intentional (abusive) acts of involved employees is much greater as compared to random samples and even samples created by conventional statistical tools. The Forensic Data Analytics represents an approach that could be rather called a special thought pattern to select transactions via analytical tests. The effectiveness of this approach is achieved by specific algorithms to be developed for each particular enterprise by taking account of business profile, internal control weaknesses and detail levels.